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How to Write a Research Paper

Writing a research paper is a bit more difficult that a standard high school essay. You need to site sources, use academic data and show scientific examples. Before beginning, you’ll need guidelines for how to write a research paper.

Start the Research Process

Before you begin writing the research paper, you must do your research. It is important that you understand the subject matter, formulate the ideas of your paper, create your thesis statement and learn how to speak about your given topic in an authoritative manner. You’ll be looking through online databases, encyclopedias, almanacs, periodicals, books, newspapers, government publications, reports, guides and scholarly resources. Take notes as you discover new information about your given topic. Also keep track of the references you use so you can build your bibliography later and cite your resources.

Develop Your Thesis Statement

When organizing your research paper, the thesis statement is where you explain to your readers what they can expect, present your claims, answer any questions that you were asked or explain your interpretation of the subject matter you’re researching. Therefore, the thesis statement must be strong and easy to understand. Your thesis statement must also be precise. It should answer the question you were assigned, and there should be an opportunity for your position to be opposed or disputed. The body of your manuscript should support your thesis, and it should be more than a generic fact.

Create an Outline

Many professors require outlines during the research paper writing process. You’ll find that they want outlines set up with a title page, abstract, introduction, research paper body and reference section. The title page is typically made up of the student’s name, the name of the college, the name of the class and the date of the paper. The abstract is a summary of the paper. An introduction typically consists of one or two pages and comments on the subject matter of the research paper. In the body of the research paper, you’ll be breaking it down into materials and methods, results and discussions. Your references are in your bibliography. Use a research paper example to help you with your outline if necessary.

Organize Your Notes

When writing your first draft, you’re going to have to work on organizing your notes first. During this process, you’ll be deciding which references you’ll be putting in your bibliography and which will work best as in-text citations. You’ll be working on this more as you develop your working drafts and look at more white paper examples to help guide you through the process.

Write Your Final Draft

After you’ve written a first and second draft and received corrections from your professor, it’s time to write your final copy. By now, you should have seen an example of a research paper layout and know how to put your paper together. You’ll have your title page, abstract, introduction, thesis statement, in-text citations, footnotes and bibliography complete. Be sure to check with your professor to ensure if you’re writing in APA style, or if you’re using another style guide.


covid 19 research paper in computer science

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How computer science can help fight COVID-19

Uchicago researchers launch projects exploring health disparities, machine learning.

The COVID-19 pandemic has mobilized the world’s scientific community like no other recent crisis, including many researchers using the most modern data science and artificial intelligence approaches. At the University of Chicago, public health experts, computer scientists, economists and policy analysts have launched projects using computational tools to better detect, diagnose, treat and prevent the spread of the deadly virus.

This summer, three of these projects received seed funding from the Digital Transformation Institute (DTI) , a new partnership of technology companies and universities committed to accelerating the benefits of artificial intelligence for business, government and society. The research attacks the pandemic from several angles: helping policymakers control disease spread by identifying and addressing key social factors, physicians detect the disease at earlier stages, and hospitals decide which patients require admission. A fourth project, a collaboration led by UChicago Medicine’s Maryellen Giger , was funded by the organization in spring.

The awards were part of $5.4 million in funding distributed by DTI, after their inaugural call for proposals in March. The group also provides AI software tools and a “data lake” of COVID-19 datasets to aid researchers studying the pandemic.

“The enthusiastic response among scientists and researchers coupled with the diverse, high-quality and compelling proposals we’ve received suggests that we have the potential to alter the course of this global pandemic,” said Thomas M. Siebel, CEO of “In the face of this crisis, the Institute is proud to bring together the best and brightest minds and provide direction and leadership to support objective analysis and AI-based, data-driven science to mitigate COVID-19.”

Modeling health disparities

The early toll of the COVID-19 pandemic revealed severe health inequities in who catches the disease and who suffers death and morbidity. Latin and African Americans are more than three times as likely to catch the virus and twice as likely to die as white Americans, according to CDC data . Many experts believe this disparity goes beyond medical comorbidities, to social determinants such as housing, jobs and neighborhood features.

Anna Hotton , a research assistant professor at UChicago Medicine, previously studied the relationship between social factors and viral spread in the context of other infectious diseases. With her DTI grant, she’s working with fellow UChicago researchers Aditya Khanna , Harold Pollack and John Schneider to adapt that work to COVID-19, with help from agent-based modeling experts Jonathan Ozik and Charles Macal at Argonne National Laboratory.

“A lot of my substantive work focuses around understanding social and structural factors as they impact HIV transmission,” Hotton said. “With COVID-19, there are a lot of similarities in terms of the social factors that shape people’s vulnerability to infection, and I’m motivated to shed light on some of these social issues and help guide work around reducing health inequities.”

Agent-based modeling is a powerful form of computer simulation for studying complex systems, from molecular interactions to traffic congestion. Over the last decade, Argonne researchers Ozik and Macal have gradually assembled a computer model for the entire city of Chicago and its population, using it to observe and predict the spread of diseases both real (MRSA, influenza) and imagined ( a zombie outbreak ). Recently, the team has focused their ChiSIM model on the spread of COVID -19, looking for types of buildings and areas of the city where people gather and disease transmission risk is high.

With Hotton and her collaborators, Ozik and Macal are working on adding new data to their synthetic Chicago population of 2.7 million “agents,” including information on housing, occupations and other social determinants that likely influence virus spread. The team will also use machine learning to identify the data elements that are most important to include in the model from a long list of options, such as time spent on public transit, ability to work from home, number of family members in a household, and many other details.

Once enriched with this data, the researchers will be able to better simulate various scenarios of disease spread and virtually test how different public health or social policy strategies can help mitigate the disease. Their results will be shared with partners in the Chicago and Illinois Departments of Public Health, advising these agencies on how best to deploy testing, reopening of businesses and schools, and, eventually, vaccination.

“Agent-based modeling allows us to explore intervention approaches in a virtual environment before rolling out interventions in real life, in addition to making predictions about trends in incidence and mortality,” Hotton said. “Later, when vaccines are available, we’ll need to figure out how to deploy them most efficiently to the populations with greatest need.”

Admit or release?

One of the toughest decisions physicians face during the pandemic is deciding which COVID-19 patients to keep in the hospital, and which are safe to recover at home. In the face of overwhelmed hospital capacity and a brand-new disease with little data-based evidence for diagnosis and treatment, old rubrics for deciding which patients to admit have proven ineffective. But machine learning could help make the right decision earlier, saving lives and lowering health care costs.

A team led by Prof. Sendhil Mullainathan of Chicago Booth will work with a large northwest U.S. hospital network on creating a new model for predicting acute respiratory distress syndrome (ARDS), the most severe symptom and primary cause of death for COVID-19 patients. Using over 4 million chest X-rays, the team—which also includes Aleksander Madry of Massachusetts Institute of Technology and Ziad Obermeyer from University of California, Berkeley—will build a new machine learning model that predicts the likelihood of this pulmonary collapse.

To work around the issue of limited COVID-19 data early in the pandemic, the team will feed their model with X-rays from other conditions that affect the lungs, such as influenza and pneumonia.

“No one has enough data on COVID yet to apply the modern machine learning toolkit,” said Obermeyer. “But in a pulmonary infection such as COVID, the lungs actually have a very limited physiological playbook. When the lungs are attacked by a virus or bacterium, they basically only react in one way. Our hypothesis is that we can learn about deterioration in COVID by looking at deterioration in other conditions.”

Once validated, their AI model will be made open source and available to other health systems around the world. The project also allows Mullainathan and Obermeyer an opportunity to develop a medical decision-making algorithm that controls for the bias they identified in other health care software in previous research .

“Even if you’re using objective biological data like X-rays, your outcomes are biased because they’re produced by a health system that is biased,” Obermeyer said. “The optimistic view of our prior work on racial bias is that once you’re aware of those biases, you can make algorithms that take them into account.”

Early detection: Treating a pandemic like engine failure

In the early stages of a disease outbreak, detecting cases is critical to prevent population spread, but also very difficult—a proverbial “needle in the haystack” data problem. But computer scientists have already developed artificial intelligence systems for such challenges in other contexts, such as detecting mechanical faults in jet engines or anomalous and potentially fraudulent financial transactions. Models built for these applications must be able to accurately and reliably find rare occurrences in a flood of data—nobody wants to discover airplane engine failure too late.

In previous work at Caltech, UChicago computer scientist Yuxin Chen built these early detection systems for mechanical engineers and other domain experts. With DTI funding, he’ll work with researchers from UC Berkeley and UCSF on transferring these approaches to detecting infection from COVID and other diseases using medical and public health surveillance data. The team will adapt solutions for common challenges such as training models on sparse data, combining data from different sources and collection techniques, and minimizing false negatives that could have dire consequences if infected patients are missed.

Chen’s portion of the project focuses on his primary research interest: interactive machine learning . As opposed to the passive, “black box” of most AI models, these systems actively work with human experts, suggesting new data sources that should be gathered to improve predictions, or asking for help when a particular diagnosis is unclear.

“If the model is not very confident about the predictive results for a certain medical diagnosis that we have data on, it will flag these data and ask experts to verify or correct the predictive results,” said Chen, an assistant professor. “We also care about interpretable recommendations; we're training our AI system to effectively communicate with the human users to collaboratively make detection and diagnosis decisions. So we need to build an interpretable interface that sits between the system and medical professionals in order to make the collaboration seamless.”

—This story was first published by the Department of Computer Science.

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Covid-19 resources, publisher collections, curated literature, filtered (pre-run searches), data and statistics.

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COVID-19 Pandemic: A Brief Review on How Computer Science and Information Technology Can Help Shape the Future of Food Safety

The COVID-19 pandemic is affecting all businesses and the food industry in particular, posing new challenges for the next future. In a short time, industry had to deal with new plans and protocols in order to reduce the risk of exposure to the coronavirus ensuring that employees remain healthy. This meant introducing distancing measures, updating Personal Protective Equipment (PPE) requirements, wiping down surfaces and equipment on a regular basis and adapting delivery procedures. In addition to issues that are affecting all businesses, food industry has and will have additional challenges to deal with. People will always need to eat and so safe, high quality food must still be produced and provided to customers.

Food safety represents the primary concern of all manufacturers, but this should now encompass actions to slow down, as much as possible, the spread of the coronavirus. Even though there is no evidence that the virus can be transmitted via food or food packaging, there is evidence that it can remain viable on certain surfaces for a longer period. The present paper reports a brief review on how the profitable combination of computer science and information technology can help shape the future of food safety.

The literature review has been conducted by considering scientific contributions and official resources published by the main international governmental organizations and technical institutions. Some of the cited sources include World Health Organization (WHO), Food and Agriculture Organization (FAO), International Telecommunication Union (ITU) and the Institute of Electrical and Electronics Engineers (IEEE). As the COVID-19 pandemic is unprecedented and still ongoing around the world to date, the literature review was limited to contributions published no more than two years ago.

Information and communication technology (ICT) and computer science applications are already present in industry. Despite this, the challenges the world will have to face in the next future to stem the spread of the COVID-19 pandemic require further interdisciplinary and synergistic efforts. In this framework, technology can play a key role in help shaping the future of industry and food safety in particular.


The COVID-19 disease is having an unprecedented worldwide impact in health and socioeconomic terms; according to data provided by Johns Hopkins University [ 1 ], by 2020, September 21, the number of global cases passed 31 million with more than 960,000 deaths. Furthermore, the pandemic is certainly having an unprecedented impact also on food systems. This is what ITU, the United Nations’ specialized agency for ICT, reported on its online resource ITU News [ 2 ].

WHO declared COVID-19 disease a public health emergency of international concern on 2020, January 30 [ 3 ]. The 2020 edition of the Global Report on Food Crises (GRFC) [ 4 ], published on 2020, April 20 as the result of a joint, consensus-based assessment of acute food insecurity situations around the world, describes the worldwide scale of acute hunger. GRFC-2020 provides an analysis of the drivers that are contributing to food crises across the globe, and examines how the COVID-19 pandemic might contribute to their perpetuation or deterioration. When comparing the 50 countries taken into account in both the 2019 and the 2020 reports, the population in crisis rose from 112 to 123 million. This reflected worsening acute food insecurity in key conflict-driven crises and the growing severity of drought and economic shocks. Around 183 million people in 47 countries were classified in stressed conditions, at risk of slipping into crisis or worse. As stated by Antonio Guterres, Secretary-General of the United Nations (UN) and reported on GRFC-2020 [ 4 ], the upheaval that has been set in motion by the COVID-19 pandemic may push even more families and communities into deeper distress.

Even before COVID-19 began to affect food systems and agricultural livelihoods, the 55 countries and territories that are home to 135 million people were already facing acute food insecurity [ 2 , 4 ]. Such people are in need of urgent humanitarian food and nutrition assistance and are the most vulnerable to the consequences of this pandemic as they have very limited or no capacity to cope with either the health or socioeconomic aspects of the shock. Rising levels of food insecurity and lack of access to healthcare are likely to increase malnutrition rates. On the food supply side, movement restrictions necessary to contain the spread of the virus will disrupt the transport and processing of food and other critical goods, increasing delivery times and reducing availability of even the most basic food items [ 4 ].

Conclusions of GRFC-2020 state that, given the unprecedented nature of COVID-19 crisis, taking rapid collective action to pre-empt its impact on food security and food systems is of paramount importance and urgency. In particular, anticipatory actions have to be undertaken to safeguard the livelihoods of the most vulnerable people and related agri-food systems to protect the critical food supply chain. Early actions for mitigation include, among others, expanding near-real time, remote food security monitoring systems to provide up-to-date information on the impacts of the outbreak on food security and livelihoods, health, access to services, markets and supply chains [ 4 ].

In this framework ICT, applied computer science and artificial intelligence (AI) will support sustainable development worldwide and in particular can play a key role in help shaping the future of food safety. The aim of the present work is outlining how state-of-the-art and trends of such technologies could be useful for the whole agri-food sector.

Survey Methodology

As the coronavirus pandemic is unprecedented, the present work aimed to review scientific contributions and official resources published no more than two years ago.

ICT and AI are already helping in agriculture and food engineering [ 5 ]: picking vegetables, controlling pest infestations, soil and crop health monitoring and predictive analysis are only a few examples. Improving food traceability as well as the efficiency that can be gained through robotics, automation and digitization in supply chain logistics are some of key requirements for the near future of food safety. Indeed, notwithstanding the essential role of farm, factory and food workers, it is recognized that any human interaction with the food value chain, at least in the context of a virus, presents some risk [ 2 ].

COVID-19 is a respiratory illness and the most recent advice from the WHO [ 6 ] is that current evidence indicates that the primary transmission route of COVID-19 virus is through person-to-person contact and through direct contact with respiratory droplets generated when an infected person coughs or sneezes. There is no evidence to date of viruses that cause respiratory illnesses being transmitted via food or food packaging. Coronaviruses cannot multiply in food; they need an animal or human host to multiply. The virus can spread directly from person-to-person when a COVID-19 case coughs or sneezes, producing droplets that reach the nose, mouth, or eyes of another person [ 7 ].

Alternatively, as the respiratory droplets are too heavy to be airborne, they land on objects and surfaces surrounding the infected person. It is possible that someone may become infected by touching a contaminated surface, object, or the hand of an infected person and then touching their own mouth, nose, or eyes. This can happen, for instance, when touching door knobs or shaking hands and then touching the face [ 7 ].

Recent research evaluated the survival of the COVID-19 virus on different surfaces and reported that the virus can remain viable for up to 72 hours on plastic and stainless steel, up to four hours on copper, and up to 24 hours on cardboard [ 8 ]. Then, it is imperative for the food industry to reinforce personal hygiene measures and provide refresher training on food hygiene principles to eliminate or reduce the risk of food surfaces and food packaging materials becoming contaminated with the virus from food workers.

Properly disinfecting public spaces can help stop the spread of coronavirus protecting workers’ health but if cleaning crews do not wear appropriate PPE, they themselves are at risk for infection. PPEs such as masks and gloves can be effective in reducing the spread of viruses and disease within the food industry, but only if used properly [ 7 ]. A disinfecting robotic arm that uses an ultraviolet (UV) light sanitizer to clean contaminated areas is being perfected at University of Southern California in Los Angeles [ 9 ]. Cameras mounted on the robotic arm help the operator, located away from the contaminated area, to navigate the robot. The robot scans the surroundings also using infrared (IR) radiation to determine their depth and then builds a 3D model of the area. Using a gripper, the robotic arm is able to open drawers and closets, and manipulate objects to perform a thorough sanitization of hard-to-reach surfaces.

Thermal imaging cameras represent a fast, contactless, and reliable method to detect a fever, a common symptom of COVID-19. IEEE members are working on projects that aim to improve the technology used in these cameras so they can be used in public spaces and commercial buildings to provide fast individual screenings to help stop the spread of the virus. Thermographic cameras used in the healthcare field must meet specific standards. For example, the screening technology must have a measurement accuracy of ± 0.5 ºC; several manufacturers of such cameras are not following those requirements. Then, software is being developed to help cameras meet technical standards required by the healthcare industry [ 10 ]. The cameras have infrared temperature sensors and motorized focus, which are controlled by the software’s system operator. The temperature sensors detect electromagnetic waves from the person and the motorized focus allows the camera’s operator to zoom in and out.

A team of researchers at Thailand’s National Electronics and Computer Technology Center has built a temperature-screening system that can examine up to nine people at a time. More places are screening people so the ability to scan several at once could eliminate waiting lines [ 11 ]. The system combines a visible camera and a thermal imaging camera. It is equipped with features such as light detection and ranging (Lidar) remote sensing methodology and facial recognition [ 12 ], which can be used with or without a facial covering, to determine where a person is standing in the camera’s field of view. The system also uses an algorithm that compensates for distance shift that happens with traditional thermal-imaging-based temperature scanners. Distance shift occurs when several individuals being checked are not at the same measuring distance, leading to a fluctuation in temperature measurements. Measurements from the Lidar and other inputs are used to compensate for variations in distance between an individual and the scanner.

AI has been employed against infectious diseases having the ability to rapidly track, analyze, and diagnose various infectious processes in real time [ 13 ]. The ability of AI technology to augment decision-making processes is due to the speed of pattern recognition and the robust amount of data that are digested and analyzed for optimal outcomes.

Augmented reality (AR), an interactive experience where real objects are enhanced by computer-generated perceptual information, has been widely used as an instructional tool to help the user to perform the task in real world conditions [ 14 ]. Recent advancements in AR have helped assisting operators in industrial safety applications in a control room environment by providing extra information in the decision making process [ 15 ].

AI, Internet of things (IoT) and the resulting massive amount of streaming data, often referred to as Big Data, are having a disruptive role in food quality assessment (using sensor fusion), food safety (using gene sequencing and block chain-based digital traceability), agriculture (including intelligent farm machines and drone-based crop imaging), and finally supply-chain modernization [ 16 ].

Mixed reality (MR) is a hybrid reality where real and virtual objects are merged to produce an enriched interactive environment [ 14 ]; in 2019, software taking advantage of AR and MR has been used to monitor pathogens such as Salmonella and Listeria on surfaces in factories more efficiently than paper-based monitoring methods [ 17 ]. Software taps AI to identify areas inside manufacturing plants where bacteria could be present and grow. This information is then uploaded to a cloud-based task management system; it can also be presented in real time through a MR headset, allowing the user to track and add information whilst walking the factory floor. Following the COVID-19 outbreak, such software has been updated for tracking the virus on surfaces with the same accuracy and efficiency demonstrated for other pathogens [ 18 ].

ICT, AI and in general computer-based solutions will drive technology in helping food and beverage producers protect their employees and plants against COVID-19 contamination by detecting and tracking the virus on surfaces as well as body temperature to identify a fever.

However, since there are dangers embedded within the adoption of any digital technology, the current pandemic is liable to make these dangers worse. Security issues, for example, still plague the massive amounts of data generated online every day. As ICTs are clearly central to the many ways people will respond to the COVID-19 crisis, how to best safeguard the data such technologies produce will hinge on the answers to three main questions: what is too important in our every-day life to take place online? Who will protect data that needs to be stored and shared for contact tracing? Who will be held liable if such new data is left vulnerable, stolen or exploited?

Future directions in the response to the COVID-19 pandemic will also have to take into account human-technology relationship as the latter is mediated by the political and institutional context in which technology is implemented.


The author would like to acknowledge healthcare workers on the frontlines of battling coronavirus disease worldwide.

Article Type

Review Article

Publication history

Received: September 22, 2020 Accepted: September 29, 2020 Published: October 02, 2020

R-Infotext Citation:

Pellegrini M (2020) COVID-19 Pandemic: A Brief Review on How Computer Science and Information Technology Can Help Shape the Future of Food Safety. COVID-19 Pandemic: Case Studies & Opinions 01(05): 94–98.

Short Commentary

Received: September 16, 2019 Accepted: September 30, 2019 Published: October 03, 2019

Williamson V, Murphy D, Greenberg N (2019) Post-Traumatic Stress Disorder: Diagnosis and Management. Integr J Orthop Traumatol Volume 2 (5): 1#x2013;3.

Marco Pellegrini 1,2 1 Department of Agricultural, Food and Environmental Sciences, Università Politecnica delle Marche, Ancona, Italy 2 LIF Srl, Scandicci, Firenze, Italy

*Corresponding author

Marco Pellegrini, Department of Agricultural Food and Environmental Sciences, Università Politecnica delle Marche, Ancona, Via Brecce Bianche 10, Ancona, 60131, Italy; Email: [email protected]

Corresponding author

Prof Neil Greenberg, Kings Centre for Military Health Research, King’s College London, Weston Education Centre, 10 Cutcombe Road, London, SE5 9RJ, UK; Tel: +44 207 848 5351; Email: [email protected]

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The impact of covid-19 in collaborative programming. understanding the needs of undergraduate computer science students.

covid 19 research paper in computer science

1. Introduction

2. research questions.

3.1. Participants and Context

3.2. measure instrument design, 3.3. variables, 4.1. sample description, 4.2. rq1: need for group programming activities in a distributed way, 4.3. rq2: size of the existing programming groups, 4.4. rq3: how have they approached group programming tasks, 4.5. rq4: which was the students’ subjective perception of the different strategies adopted for group programming, 4.6. rq5: do students require tools that support distributed and synchronous group programming activities, 4.7. rq6: which features and functionalities should be useful for students to support synchronous distributed programming activities, 4.8. rq7: are there significant differences in the students’ needs depending on the enrollment year or the size of their programming groups, 5. discussion, limitations.

6. Conclusions

Author contributions, acknowledgments, conflicts of interest.

Share and Cite

Lacave, C.; Molina, A.I. The Impact of COVID-19 in Collaborative Programming. Understanding the Needs of Undergraduate Computer Science Students. Electronics 2021 , 10 , 1728.

Lacave C, Molina AI. The Impact of COVID-19 in Collaborative Programming. Understanding the Needs of Undergraduate Computer Science Students. Electronics . 2021; 10(14):1728.

Lacave, Carmen, and Ana Isabel Molina. 2021. "The Impact of COVID-19 in Collaborative Programming. Understanding the Needs of Undergraduate Computer Science Students" Electronics 10, no. 14: 1728.

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Microsoft Academic

Impact of covid-19 on the computer science research community, share this page.

By March 10 th , 2020 the novel coronavirus (COVID-19) has infected ~117k and been responsible for the deaths of over 4k people worldwide. The World Health Organization (WHO) has not yet classified the COVID-19 outbreak as a pandemic, however, COVID-19 has resulted in a significant impact on individual lives and global economics. Following the Microsoft Academic team’s goal to “help researchers stay on top of their game”, we are providing an analysis of the COVID-19 impact on the computer science (CS) research community to help enable conference organizers and institutions to respond accordingly and manage the impact.

Our analysis shows that:

Analysis and Discussion

1. cs and ai conference publication statistics.

The graph below (Figure 1a) shows the total number of CS and AI conference publications in 2019. Only the top 20 regions are shown here. The US followed by the EU, China, Japan and Canada had the highest volume of published papers among the 105 selected CS conferences as well as in the 32 AI conferences.

The table below shows the number of 2019 publications and percentages for the CDC warning/alert areas. Regions are categorized according to the US CDC travel risk assessment, please refer to the CDC for the description of each level.  China, Iran, South Korea, Italy, Japan and Hong Kong together contributed 21.25% and 21.33% to CS and AI publications in 2019. This could be the rate of authors who couldn’t attend the conferences in 2020 due to the travel interruption by COVID-19 in these areas.

To further confirm the rate of publications from impacted areas, we gathered data between 2000 and 2019. As shown in Figure 1b, there is clearly an increase in publications from the COVID-19 impacted areas. The impacted publication rate for both CS and AI conferences are above 21% in 2019 and possibly higher in 2020.

2. 2020 CS Conference Publications Impact – by Conference Location

The graph below (Figure 2) shows the estimated impact on 2020 CS conferences hosting in COVID-19 impacted areas. The number of publications from 2019 is used to estimate the number of publications in 2020. The solid blue line shows the accumulated number of impacted publications over time.

3. 2020 CS and AI Conference Publications Impact – by Author Location

The graph below (Figure 3) shows 2020 CS and AI conferences which are hosting outside COVID-19 impacted areas. The numbers of publications for each impacted conference are estimated by the number of publications in 2019 (2018 if it’s biennial) from COVID-19 impacted areas. According to our analysis, among the CS conferences scheduled in the coming four months, ICC, IMTC, ICDE and ISCAS have the most publications contributed from the COVID-19 impacted areas (each above 30%). We listed the conferences in the next four months with the 2019 publication statistics in Appendix 1 at the end.  A majority of the AI conferences for the next four months have 10% to 20% impact rate based on 2019 data (Appendix 2).

4. 2020 CS and AI Conference Publications Impact – Total

The graph below (Figure 4) shows the COVID-19 impact estimates in CS and AI conferences combining the impact from both conference location and author location. The impact has a similar pattern in CS and AI conferences. Starting from May 2019, the impact increases considerably as many conferences occur during the summer months (northern meteorological). If COVID-19 can be contained and the travel interruption is lifted before May 2020, the impact on CS conferences should be minimal. On the contrary, if the outbreak situation cannot be improved by September, there could be significant impact to the CS research community.

5. How to Determine if a Publication is Impacted by COVID-19

As mentioned earlier, we consider a publication to be impacted by COVID-19 if the headquarters of the first author’s affiliation is in one of the affected areas.

We choose the first author’s affiliation location instead of all authors because 1) first author normally is the presenter of the paper and 2) it simplifies our analysis while not significantly impacting the result. A previous paper pointed out there are 25-fold increases in international collaborations for scientific development. For the CS publications we analyzed, the cross-region collaboration increases from 7.8% to 23.9% in the past 20 years. Although the cross-region rate is high, only 4% of publications have authors from non-impacted regions while first author is located in impacted regions. Therefore, we believe the first author’s location is a good representation of the publication’s locations.

In the case that the first author is associated with multiple affiliations and one affiliation is in affected areas, we count the publication as affected. Only 0.17% of CS publications have first authors associated with multiple affiliations.

Some affiliations could have multiple locations, such as Microsoft. The headquarter location is used in this scenario. And we estimate there are less than 2% such cases.

All the above estimates are based on the the most current information we could obtain using MAG. If the current situation continues, the data shows the potential for significant impact on CS conferences unless conference organizers take actions to mitigate the impact.

Some conference organizers have already taken actions, such as:

Additional CS Conference Updates regarding COVID-19

In an effort to help the CS community we will continue to monitor CS conference announcements regarding COVID-19 and provide updates below:

Stay healthy and research on!

2020 March to June, Non-AI CS Conferences.

2020 March to June, AI Conferences.

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Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review

H. swapnarekha.

a Department of Information Technology, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India

Himansu Sekhar Behera

Janmenjoy nayak.

b Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh 532201, India

Bighnaraj Naik

c Department of Computer Application, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India

The World Health Organization (WHO) declared novel coronavirus 2019 (COVID-19), an infectious epidemic caused by SARS-CoV-2, as Pandemic in March 2020. It has affected more than 40 million people in 216 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate. As the early prediction can reduce the spread of the virus, it is highly desirable to have intelligent prediction and diagnosis tools. The inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of virus. In this paper, a state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done. Moreover, a comparative analysis on the impact of machine learning and other competitive approaches like mathematical and statistical models on COVID-19 problem has been conducted. In this study, some factors such as type of methods(machine learning, deep learning, statistical & mathematical) and the impact of COVID research on the nature of data used for the forecasting and prediction of pandemic using computing approaches has been presented. Finally some important research directions for further research on COVID-19 are highlighted which may facilitate the researchers and technocrats to develop competent intelligent models for the prediction and forecasting of COVID-19 real time data.

1. Introduction

Throughout history, several infectious diseases have alleged the lives of many people and induced critical situations that have taken a long time to overcome the situation. The terms epidemic and pandemic have been used to describe the disease that emerges over a definite period of time [1] . During a particular course of time, the existence of more cases of illness or other health situations than normal in a given area is defined as epidemics [2] . On the other hand, pandemics are outbreaks of the infectious disease that can enormously increase the morbidity and mortality over a vast geographical area. Due to the factors such as raise of worldwide travel, urbanization, changes in usage of land and misusing of the natural environment, the occurrence of the pandemics has increased from the past century [3] . In the past, the outbreak of smallpox has killed of nearly 500 million world population in the last 100 years of its survival [4] . Due to the outbreak of Spanish influenza in 1918, an estimate of 17 to 100 million deaths occurred [5] . From the last 20 years several pandemics have been reported such as acute respiratory syndrome coronavirus (SARS-CoV) in 2002 to 2003, H1N1 influenza in 2009 and the Middle East respiratory syndrome coronavirus (MERS-CoV) in 2015. Since December 2019 the novel outbreak of coronavirus has infected more than thousand and killed above hundreds of individuals within the first few days in Wuhan City of Hubei Province in South China. In the 21 st century, the pandemics such as SARS-CoV has infected 8096 individuals causing 774 deaths and MERS-CoV has infected 2494 individuals causing 858 deaths. While the SARS-CoV-2 has infected more than 3.48 million individuals causing 2,48,144 deaths across 213 countries as on May 3, 2020. These evidential facts state that, the transmission ratio of SARS-CoV-2 is greater than other pandemics. A list of some dangerous pandemics happened over time is listed in Table 1 .

Table 1

List of Pandemics over time.

Due to the rapid increase of patients at the time of outbreak, it becomes extremely hard for the radiologist to complete the diagnostic process within constrained accessible time [6] . The analysis of medical images such as X-rays, Computer tomography and scanners plays a crucial role to overcome the limitations of diagnostic process within constrained accessible time. Now-a-days, machine learning and deep learning techniques helps the physicians in the accurate prediction of imaging modalities in pneumonia. ML is a wing of artificial intelligence that has the ability to acquire relationships from the data without defining them a priori [7] . Due to the availability of large number of intelligent tools for the analysis, collection and storage of large volume of data, machine learning techniques have been extensively utilized in the clinical diagnosis. Machine learning approaches can be efficiently used in applications of healthcare such as disease identification, diagnosis of disease, discovery and manufacturing of drug, analysis of medical images, collection of crowd sourced data, research and clinical trials, management of smart health records, prediction of outbreak etc. Some recent studies show the usage of machine learning techniques in the time series forecasting of Ebola outbreak [8] . In order to select the better performing classifier for forecasting Ebola casualties, experiments were conducted on ten different classifiers. Further, results demonstrate that the proposed model achieves 90.95 % accuracy, 5.39 % MAE and 42.41 % RMSE value. Even though machine learning approaches have rapidly used in the diagnosis of outbreaks, these approaches still have some limitations such as complete utilization of biomedical data, temporal dependency, owing to high-dimensionality, sparsity, heterogeneity and irregularity [9] , [10] , [11] . On the other hand, due to the deep architectural design, the deep learning models are the best accurate models for handling medical datasets such as classification of brain abnormalities, classification of different types of cancer, classification of pathogenic bacteria and segmentation of biomedical images [12] , [13] , [14] , [15] , [16] . Several studies show that DL models are adopted in the diagnosis and classification of pneumonia and other diseases on radiography. A deep learning model build on Mask-RCNN has been utilized for the detection and localization of pneumonia in chest X-ray images [17] . In order to perform pixel-wise segmentation, the model makes use of global and local features. The robustness of the system is achieved through the modification of training process and post processing step. Further, results show that the model outperforms in the identification of pneumonia in chest X-ray images. To improve the performance of prediction, a bioinspired meta-heuristic optimization algorithm has been presented by Martinez-Alvarez et al [18] . In this approach, to prevent the researches from initializing with arbitrary values, the input parameters are initialized with the disease statistics. Also this approach has the ability to stop after certain number of iterations without setting this value. Further, to explore wider search space in less number of iterations, a parallel multi-virus approach has been proposed. Finally, it has been integrated with DL models for finding the optimal parameters in the training phase. Deep learning prototypes have been widely used in the prediction and forecasting of outbreak over machine learning models because of its features such as greater performance, feature extraction without human intervention identification and not making the use of engineering advantage in training phase.

The major objective of this paper is to provide a review of different machine learning approaches used in the prediction, classification and forecasting of COVID-19. First, we describe the origin of SARS-CoV-2 virus, its transmission rate, comparison of SARS-CoV-2 with other pandemics in the history and its impact on the global health. Then the analysis is broaden by describing the advantages of computing approaches such as statistical and mathematical models, ML and DL approaches in the prediction of COVID-19 along with its applications. Further, an analysis of number of articles published in different computing approaches by different countries till date, impact of the nature of data in the prediction of COVID-19 have been presented. As the researchers and technocrats are the main targets of this review, we highlighted some of the challenges in the ongoing research of different computing approaches at the end of the paper. The remaining section of the paper is systematized as follows. Section 2 describes the origin of COVID-19 and its impact. The application of statistical, ML and DL models in the diagnosis and prognosis of COVID-19 has been portrayed in section 3 . Section 4 illustrates the critical investigation on the analysed data types of COVID research, growth in publication of ML approaches for COVID, comparative analysis on the type of methods etc. Few challenges in ML related to covid-19 have been focused in Section 5 . Section 6 outlines the conclusion with a brief discussion on covid-19 impact in real life and novel research directions.

2. Genesis of SARS-CoV-2 and its Impact on global health

In the 21 st century, Human corona viruses such as SARS-Coronavirus (SARS-CoV) and MERS-Coronavirus (MERS-CoV) that have emerged from the animal reservoirs caused global epidemic with distressing morbidity and mortality. These human corona viruses belong to the subfamily of Coronavirinae that is a part of Coronaviridae family. It was named as corona because of the presence of spike like structure on the outer surface of the virus under electron microscope. Its RNA is a single stranded with a diameter of 80-120 nm and nucleic material range varying from 26 to 32 kbs in length [19] . These are basically divided into four types of genera named as alpha (α), beta (β), gamma (γ) and delta (δ). α- and β-CoV usually infects mammals, while γ- and δ-CoV tends to affect birds. Among the six susceptible human viruses, HCoV-229E and HCoV-NL63 of α-CoVs and HCoV-HKU1 and HCoV-OC43 of β-CoVs shows low pathogenicity and moderate respiratory symptoms as common cold. The other two familiar β-CoVs such as SARS-CoV and MERS-CoV exhibit acute and malignant respiratory diseases [20] . The Fig. 1 shows the transmission process of corona viruses from animal sources to human.

Fig 1

Transmission of Corona viruses from animals to Human.

The people in Wuhan City of Hubei Province in south china were reported in local hospitals with an unidentified pneumonia on December, 2019 [21] . Initially, these cases were related to Huanan Seafood Wholesale Market which is famous for variety of live species. All these cases have clinical characteristics similar to those of viral pneumonia such as dry cough, fever, dyspnea and lung infiltrates on imaging. After the analysis of samples collected from the throat swab, the authorities from Centers for disease control (CDC) announced the unidentified pneumonia as novel coronavirus pneumonia (NCP) on 7 th January, 2020 [22] . Later it was named as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses [23] , [24] and the disease was renamed as COVID-19 by the WHO on 11 th February 2020 [25] . The COVID-19 generated by SARS-CoV-2 is a β-coronavirus. The sequence analysis of SARS-CoV-2 matches with the typical structure of coronaviruses as depicted in Fig. 2 .

Fig 2

Interpretation of the SARS-CoV-2 virion [26] .

The structure of SAR-CoV consists of 14 binding remnants that collaborate precisely with human angiotensin-converting enzyme 2. As eight binding residues of the SARS-CoV has sustained in the SARS-CoV-2, the structure of SARS-CoV-2 genome contributes 79.5% similarity to SARS-CoV. [27] . In addition, both bat CoV and human SARS-CoV-2 share the identical ancestor as the genome sequencing of COVID-19 shows 96.2 % identity to Bat CoV RaTG13 [28] . Due to the genetic recombination occurrence at ‘S’ protein in the RBD area of SARS-CoV-2, the SARS-CoV-2 has greater transmission ability than SARS-CoV [29] . After the spread of COVID-19 to 18 countries through human-to human transmission , the WHO announced the epidemic as Public Health Emergency of International Concern (PHEIC) on 30 th January, 2020. In addition, critical situation was created when the first case not imported from china was registered in the United States on 26 th February, 2020. The WHO declared the COVID-19 as pandemic on 11 th march 2020 when it imposes serious hazard to public health as the number of cases outside the china has raised 13 times and the number of countries distressed by COVID-19 has increased by three times. Since the last two months, the number of covid-19 cases registered has crossed all the previous records of the viral disease. Due to its rapid spread, it is considered to be the most dangerous disease till date. According to the WHO, the SARS-CoV-2 has infected about 3.48 million people and caused 2,48,144 deaths across 213 countries of the World as on May 3, 2020. Among the countries, the USA has reported about 1,188,122 positive cases and 68,595 deaths as on May 3, 2020 and stood in first place in both positive cases as well as in death rate. Similarly, other countries like Spain, Italy, UK, France, Germany, Russia, Turkey, Iran, Brazil, Canada, Belgium, Peru and Netherland are placed as top countries with more than 50,000 cases after the pandemic of COVID-19 outside the mainland of China. The number of deaths per day due to COVID1-9 pandemic is also gradually increasing from the starting day of transmission to till date. Fig. 3 , Fig. 4 , predicts the top countries in the world having more than 50,000 and number of deaths per day as on May 3, 2020.

Fig 3

No of positive cases up to May, 3, 2020.

Fig 4

Daily no of deaths.

3. Developed Computing approaches in the classification, prediction and prevention of COVID-19

Due to the quick spread of the SAR-CoV-2, physicians are facing extreme difficulty in the diagnosis of COVID-19. Even though Reverse Transcription Polymerase Chain Reaction (RT-PCR) method is the standard method used in the diagnosis of Ncov-2019 [30] , due to pandemic it suffers from limitation such as low sensitivity, requires more time, and short in supply. In recent years, analysis of medical images is one of the most promising rising research areas in the healthcare sector. Therefore the analysis of medical images such as X-rays, Computer tomography and scanners can overcome the limitations of RT-PCR. As digital technologies is also playing a crucial role in the prevention of disease, the worldwide health emergency is also seeking the support of digital technology such as the application of statistical, machine learning and deep learning models for efficient diagnosis of medical images to stop the spread of the disease.

3.1. Machine learning techniques

The fundamental capability of the machine learning is the derivation of predictive models without having any knowledge of the basic mechanism that are usually not known or insufficiently defined. These techniques are capable of developing complex patterns from huge, noisy or complex data. As the combination of predictive models with expert system reduces the subjectivity problem, these models offer outstanding support for the clinical diagnosis. Previously, machine learning techniques can be used to find the epidemic patterns as these algorithms are used in the analysis and forecasting of medical images [31] . Hence in the present pandemics, several researches have also used different approaches of machine learning such as Support Vector Machine, Regression, Random Forest, K-means and so on in the prediction and diagnosis of SARS-CoV-2.

3.1.1. Random Forest (RF)

Random forest algorithm (RF) is one of the most promising and recognized classifier that uses multiple trees to train and predict data samples. This approach has been extensively used in the fields of chemometrics and bioinformatics [32] , [33] . Because of its praiseworthy characteristics, random forest has been used in resolving issues of the nCOVID-19 infection. For precise and rapid recognition of COVID-19, a tool based on random forest algorithm to extract 11 key blood indices from clinical available blood samples was suggested by Wu et al [34] . In this study, random forest algorithm is used as a discrimination tool to explore patients with COVID-19 symptoms. The proposed method achieved better outcome in the prediction of COVID-19 with accuracy of 0.9795 for the cross-validation set and 0.9697 for the test set. Further, the tools also achieved better performance in terms of sensitivity, specificity and overall accuracy of 0.9512, 0.9697, and 0.9595, respectively on an external validation set. Moreover, it achieved an accuracy of 0.9167 in a detailed clinical estimate of 24 samples collected from infected COVID-19 patients. After multiple verifications, the proposed approach has been emerged as a precise tool for the recognition of COVID-19 infection. To predict the hospital stay of patients infected with novel coronavirus, a model based on linear regression and random forest has been suggested by Qi et al [35] . The proposed model based on 6 second-order characteristics was refined on features obtained from pneumonia lesions in training and inter-validation datasets. Further, the predictive efficiency has been evaluated using lung-lobe and patients-level test dataset. From the conclusions, it is observed that model was efficient in segregating short and long-term stay of patients in hospital infected with coronavirus infection. Moreover, linear regression model exhibited a sensitivity and specificity of 1.0 and 0.89. While a sensitivity and specificity of 0.75 and 1.0 has been exhibited by the random forest model. The following Table 2 depicts the usage of random forest approach in the prognosis of SARS-CoV-2 infection.

Table 2

Applicability of Random forest approach in the prognosis of SARS-CoV-2 infection.

3.1.2. Support Vector Machine

As SVM is used as a powerful tool for data regression and classification, it has superior performance in many real world applications such as medical image analysis over other machine learning approaches. Because of its better performance, it has been used in the classification and analysis of COVID-19. To predict the threat of positive COVID-19 diagnosis, different machine learning approaches such as neural networks, random forests, gradient boosting trees, logistic regression and support vector machines for training the sample data was suggested by Batista et al [41] . The performance of the different machine learning approaches was trained on arbitrary sample data of 70% of patients and was tested on 30% of new not seen data. Form the results, it concludes that the support vector machine algorithm outperforms with AUC, sensitivity, specificity and Brier score of 0.85, 0.68, 0.85 and 0.16 respectively when compared with other machine learning algorithms. For the diagnosis of COVID-19 infected patients form the chest X-ray images, a machine learning model developed on multi-level thresholding and SVM has been suggested by Hassanien et al [42] . Furthermore, the results depict that the proposed model achieves better performance with an average sensitivity of 95.76%, specificity of 99.7%, and accuracy of 97.48%, respectively. Table 3 represents the usage of machine learning approaches in the recognition and diagnosis of COVID-19.

Table 3

Usage of Support Vector Machine in the prediction and diagnosis of COVID-19.

3.1.3. Other machine learning approaches

Besides random forest and support vector machine, other approaches of machine learning approaches such as linear and logistic regression, XGBoost, K-means, neural network have also been used in the clinical and public health approach. Many research works are being performed and Table 4 represents the usage levels of these approaches in solving some of the conflicts of Covid-19.

Table 4

Other Machine learning approaches in the prediction and diagnosis of COVID-19.

3.2. Deep learning techniques

Deep learning techniques are representation-learning algorithms that consist of simple but nonlinear modules which are used to alter the representation at one level into a presentation at slightly more intellectual levels [57] . The deep structural nature made the deep learning models capable of resolving the complex artificial intelligent tasks. Deep learning paradigms offer new opportunities in the field of biomedical informatics because of its features such as excellent performance, end-to-end learning scheme with combined feature learning, ability to handle complex and multi-modal data and so on. Deep learning techniques have also been used in the efficient classification and analysis of medical images of COVID-19 pandemic. Various deep learning approaches such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Residual Neural network and Autoencoder have been attempted by many researchers in the classification and prediction of COVID-19 infection.

3.2.1. Convolutional Neural Network (CNN)

Convolutional Neural Network has proven to be one of the most successful algorithms in the analysis of medical image with high accuracy. Previously, the identification of the nature of pulmonary nodules in CT images, prediction of pneumonia in X-ray images, labeling of polyps automatically at the time of colonoscopic videos have been done using convolutional neural networks [58] , [59] , [60] . The authentication features for identifying COVID-19 in medical images are bilateral distribution of patchy shadows and ground glass opacity [61] . Abbas et al. [62] have developed a Decompose, Transfer, and Compose (DeTraC) model based on convolutional neural network to categorize the COVID-19 chest X-ray images. Using the class decomposition mechanism the class boundaries are investigated that helps the model to accord with any anomalies in the image dataset. Further, the results show that an accuracy of 95.12% was attained by DeTraC in the recognition of COVID-19 X-ray images from other normal and pneumonia cases. To recognize COVID-19 patient from chest X-ray images, distinct convolutional neural network frameworks namely ResNet50, InceptionV3 and Inception-ResNetV2 have been proposed by Narin et al. [63] . Further, insufficient data and training problem can be overwhelmed by applying deep transfer learning technique using ImageNet. From the results it can be observed that highest classification performance with 98% accuracy can be attained by the ResNet50 model over the other two models. To interpret and predict the number of positive cases, a COVID-19 forecasting model build on convolutional neural network (CNN) was suggested by the Huang et al [64] . The main focus of the study was to consider the cities with large number of positive cases in China. The overall competence of different algorithms was compared using mean absolute error (MAE) and root mean square error (RMSE). The outcomes indicate that CNN achieves greatest prediction efficacy when collated with other approaches of deep learning such as LSTM, GRU and MLP. Furthermore, the actual usage and feasibility of the proposed model in forecasting the total registered cases were also documented in their study. To automatically recognize the Ncov-2019 positive cases from chest X-ray images, Mukherjee et al. [65] have proposed tailored shallow convolutional neural network architecture. The architecture was designed with few parameters for validating 130 COVID-19 positive X-ray images and 5-fold cross validation was used to avoid possible bias in the experimental tests. Moreover, the proposed method achieved an accuracy, sensitivity and AUC of 96.92%, 0.942 and 0.9869 respectively, which is dominative over other compared methods. A multitask deep learning model has been proposed by Amyar et al. [66] to perform the automatic screening and segmentation of COVID-19 chest CT images. For reconstruction and segmentation, one encoder and two decoders along with multi-layer perceptron has been used in the architecture for the classification purpose. Then the model has been evaluated with a dataset of 1044 patients, which includes 449 patients suffering with COVID-19, 100 normal cases, 98 patients with lung cancer and 397 cases of different types of pathology. Moreover, results indicate that the model obtains better performance over the other image segmentation and classification techniques. The application of CNN in the classification and diagnosis of COVID-19 has been depicted in Table 5 .

Table 5

Prediction and Diagnosis of COVID-19 using CNN.

3.2.2. Long Short-Term Memory (LSTM)

Long Short-Term Memory is a type of the Recurrent Neural Network that can store knowledge of previous states and can be trained for work that needs memory. LSTM is one of the efficient models for the prediction of time series sequential data [81] . As past data is retained in the hidden states, LSTM approach can perform more accurate predictions of the output. A novel multivariate spatiotemporal model has been proposed by Jana et al [82] . This model uses ensemble of convolutional LSTM to make accurate forecast of the dynamics of COVID-19 transmission in large geographic region. For converting the available spatial features into set of 2D images, a data preparation method is used. Further, the proposed model is trained using USA and Italy data. From the findings it can be observed that the model achieves 5.57% and 0.3% MAPE for USA and Italy respectively. To predict the number of COVID-19 cases in India, a data-driven estimation approach based on LSTM and curve fitting has been suggested by Tomar et al [83] . This approach was also used to estimate the effective of social distancing measures on the spread of the pandemic. Further, the findings show the accuracy of proposed approach in predicting the number of positive and recovered cases in India. Table 6 shows the applicability of LSTM in resolving the issues of COVID-19 pandemic.

Table 6

Application of LSTM in resolving issues of COVID-19 pandemic.

3.2.3. Other approaches of deep learning

In addition to CNN and LSTM approaches, several other deep learning approaches such as Generative Adversarial Networks and Autoencoder have also been used in the analysis and prediction of COVID-19 pandemic. Generative adversarial networks (GANs) are algorithmic architectures that consist of two neural networks called generator and discriminator to generate new, synthetic instances of data from real ones that have been never observed before. GANs have been widely used in image generation, video generation and voice generation. An autoencoder is a sort of artificial neural network used to grasp efficient data coding in an unsupervised way. The following Table 7 represents the usage of other deep learning approaches in the prediction and diagnosis of novel coronavirus disease.

Table 7

Prediction and diagnosis of COVID-19 pandemic using other Deep learning approaches.

3.3. Mathematical and Statistical methods

From the past few pandemics, mathematical and statistical models have been successfully used in the estimation of human loss and also in the prediction of total number of deaths until a specific period or end of the pandemic. As the mathematical and statistical approaches shows better performance, researchers have also used the same approaches in the estimation of spread rate and death count of the current pandemic. To foresee the window period for testing positive negative results as well as false negative result, a multivariate model has been developed by Xu et al [99] . The clinical characteristics that are responsible for false negative outcomes of SARS-CoV-2 nucleic acid identification are determined using these models. Zhong et al. [100] have used the simple mathematical models for the early prediction of novel coronavirus disease in china. The prediction models estimated that the total number of positive cases may reach to 76,000 to 230,000 in late February to early March. It is also estimated that from early May to late June, the number of registered cases will rapidly decrease. To predict the number of deaths in china, Boltzmann's function based analysis has been proposed by Gao et al [101] . The prediction model gives better prediction accuracy in the assessment of severity of situation. The impact of primary health conditions such as cardiac disease, diabetes and age of the patient in the prediction of death rate has been presented by Banerjee et al [102] . Azad et al [103] have used Holt's and autoregressive integrated moving average (ARIMA) model in the temporary forecasting of COVID-19 infected patients in India from 30 January to 21 April 2020. The following Table 8 shows some other analysis done in the prediction of COVID-19 using mathematical and statistical approaches.

Table 8

Other reviews done on the forecasting of COVID-19 using mathematical and statistical approaches.

4. Critical Investigation

A systematic analysis of articles related to COVID-19 has been carried out in this study using various intelligent computing approaches. From the analysis, it is observed that the machine learning and deep learning approaches have been successfully utilized in the interpretation of medical images because of the facts such as extraction of rich features from multimodal clinical dataset, ability to distinguish between bacterial and viral pneumonia, capable of detecting various chest CT imaging features and early detection of epidemic patterns. Therefore, the data obtained using these approaches may help the physicians and researchers in understanding and prediction of COVID-19 infection at the early stage. In this section systematic analysis on the week wise publications of COVID-19 using ML, DL and other approaches, number of articles published journal wise, country wise analysis of the publications, number of articles published using different approaches, performance analysis of different approaches, the impact of natural data on the prediction of COVID-19 has been carried out.

4.1. Performance analysis of different intelligent computing approaches

From the systematic review, it has been observed different researchers have used different intelligent approaches in the prediction and diagnosis of COVID-19 infection.

4.1.1. Machine learning approaches

It has been evident from the system review that machine learning approaches also have been efficiently used in the prediction and diagnosis of COVID-19. Among the available machine learning approaches mostly support vector and random forest have been used in the recognition of novel COVID-19 outbreak. As support vector machine is one of the best classifier algorithms with minimum error rate and maximum accuracy, it gives better prediction results. A machine learning model suggested by Barstugan et al. [46] , have been efficiently used in the classification of medical images. Using SVM for classification and Grey Level Co-occurrence Matrix (GLCM) for feature extraction, the proposed model achieves 99.68% classification accuracy. As random forest uses multiple tress to identify the samples and robust to noise, it has been extensively utilized in the classification of medical images. Moreover, random forest approach is suitable for multiclass problem, whereas SVM is suitable for two-class problem. A random forest model proposed by Tang et al. [38] , have been successfully utilized in the severity assessment of coronavirus infected patients. Using 30 quantitative features, the proposed model achieves 0.933 true positive rate, 0.74 true negative rate and 0.875 accuracy. Table 9 represents the analysis of performance metrics of various machine learning algorithms in the diagnosis of COVID-19 infection. It has been evident from the systematic analysis that most of the machine learning approaches trained on small datasets. Moreover, the limitations specified in the Table provide scope for the researchers to develop more accurate prediction and forecasting models in the future.

Table 9

Performance analysis of various machine learning approaches in forecasting and prediction of COVID-19 infection.

4.1.2. Deep learning approaches

Due to the advantages of deep learning approaches over machine learning approaches such as excellent performance, feature extraction without human intervention and handling complex and multimodal, more number of researches has been carried in the diagnosis of COVID-19 infection using deep learning approaches. From the systematic review, it is observed that CNN is one of the most commonly used deep learning approaches for the prediction of pandemic from the medical Images over other approaches due to its ability to extract features automatically. A deep learning algorithm proposed by Wang et al. [113] , have achieved an overall accuracy of 73.1% on external validation. This is due to the limitations such as large number of variable objects and presence of only one radiologist in outlining the ROI area. It has been also observed that the implementation of exact architecture may not yield in better solutions. A new modified CNN for categorizing X-ray images proposed by Rahimzadeh et al. [72] , improves the overall performance by extracting multiple features using Xception and ResNet50V2 networks. The model has been applied on 11302 images and conclusions show that the proposed method achieves an overall average accuracy of 99.56% for COVID-19 cases. An iteratively pruned model proposed by Rajaraman et al. [68] , improves the classification performance by combining distinct ensemble schemes. The empirical results show that the proposed model outperforms by achieving 99.01% accuracy and 0.9972 of area under curve value. A ResNet based framework proposed by Farooq et al [114] , have achieved an overall accuracy of 96.2% with augmentation. Hence, the performance can be enhanced by considering some aspects such as presence of expert knowledge about the task to be solved, additional augmentation and preprocessing steps, optimization of hyperparameters and so on in the implementation. The following Table 10 depicts the performance analysis of some deep learning approaches that may enable the researchers to select an appropriate deep learning approach and architecture for resolving conflicts in COVID-19 pandemic as well as further scope to improve the overall performance of different approaches.

Table 10

Performance analysis of various deep learning approaches in diagnosis of COVID-19.

4.2. Efficiency of COVID-19 Prediction model

Since the start of outbreak of COVID-19 in December 2019, several researchers and modeling groups around the world have developed abundant number of prediction models [125] , [126] , [127] , [128] using mathematical and intelligent computing approaches to predict the trends of COVID-19 in different regions of the world. The list of COVID-19 forecasting attempts all over the world using various statistical models is publicly accessible 1 . The modeling results have forecasted information about trends in COVID-19 around the world such as infection cases, future deaths, recovered cases, hospitality needs, impact of social distancing, travelling restrictions and so on. These models have shown a vast range of variations in predictions due to uncertainty of data. It has been found that the design issues have also been observed in the most cited forecasting technique from the IMHE [129] , [130] . Though the issues are resolved in later revised model, the prediction errors still persist high. One of the reason for these variations is only small amount of information is available at the beginning of the outbreak and lack of reliable data due to frequent segregation of data over different geographical regions. Another reason is most of the prediction models have forecasted the future results by considering data of confirmed cases of those who got infected with symptoms and tested at the hospitals. These predictions have not taken into consideration the data of asymptomatic patients. Furthermore, the factors affecting the positive and death rate such as age, gender, hypertension, chronic diseases etc., have not considered in some prediction models. The selection of suitable model for performing epidemic study also influences the predictions of the model. A simple model is not realistic as it does not include more epidemiological information, while the complex model is biologically authentic. Though the complex model includes more biological information, it requires more parameters when compared to simple model. The complex model also leads to larger degree of uncertainty, if the increased parameters are unknown. Therefore, to make more accurate predictions in the future more research has to be carried on improving the tools and models of the prediction on large biological information.

4.3. Growth in publication of COVID-19 research using ML and other methods

Despite having lower fatality rate, SARS-CoV-2 caused thrice the total of deaths when compared to the combined statistics of deaths caused by both MERS and SARS-CoV. As the symptoms of COVID-19 are similar to common influenza, it becomes difficult to detect the infection. In addition, COVID-19 is much more contagious than influenza, due to asymptomatic condition. Further, the shortage of medical supply, rapid spread and the non-availability of a vaccine or drug for treating COVID-19 are the major reasons that attracted most of the researchers to carry vast research on COVID-19 when compared to other pandemics. Fig. 5 represents the articles contributed for the analysis from Jan 13, 2020 to May 3, 2020. In the initial stage of disease, less than 1% of articles have been published in the month of January. Around 6% of articles issued in the month of February, 16% of articles reported in the month of March and 77% of the articles are published in the month of April. So it can be concluded that up to 3 rd May 2020, majority of the articles have been published in the month of April as the number of infected COVID-19 cases increased world wise. It is worthy to note that, with the increase of COVID-19 cases throughout the world, researchers have shown incredible interest to decipher the problem of various aspects on COVID-19 through intelligent ML and other computing approaches.

Fig 5

Week wise Analysis of publications on COVID-19.

4.4. Distribution of articles on COVID-19 by Machine Learning, Deep Learning and statistical and mathematical techniques

It is evident from the Fig. 6 , that majority of work on COVID-19 prediction/diagnosis has been conducted using deep learning approaches (39%). Next, 37% of research has been experimented using the machine learning approaches. Only 24% of the work has been done using mathematical and statistical models in the prediction of COVID-19. From the figure, it can be concluded that more appropriate prediction and diagnosis of COVID-19 diseases can be performed using deep learning approaches. More work has been contributed on deep learning approaches when compared to other approaches because of the features such as excellent performance, ability to handle complex and multi-modal data, feature extraction without human intervention and absence of engineering advantage in training phase.

Fig 6

Articles published on COVID-19 using different approaches.

4.5. Number of articles distributed by Journals

Articles from different sources such as Lancet, JAMA, NEJM, Elsevier, Oxford, Wiley, Nature, BMJ, Science and medRxiv have been considered for the systematic review. It is evident from the Fig. 7 that majority of the articles have been published in BMJ i.e., 17.6%. Next 17.1% articles have been published in Lancet. The articles from Nature have contributed 15.3 %. The science has contributed 8.5% of the studies. 7.6% of the studies have been contributed by Journal of Medical virology. NEJM, JAMA, Clinical Infectious diseases, Journal of Infection, Travel Medicine Infectious Diseases, International Journal of Infectious Diseases, Eurosurveillance, Emerging Infectious Diseases, Radiology, Viruses, Infection Control Hospital Epidemiology, Emerging Microbes Infection, Journal of Hospital Infection have been contributed between 2 to 4% of the studies. Remaining journals like Annals of Internal Medicine, Journal of the American Academy of Dermatology International Journal of Antimicrobial agents, Journal of Clinical Medicine, Journal of Travel Medicine, Journal of Virology, Methods in Molecular Biology have contributed less than 1% of the studies.

Fig 7

Distribution of articles by Journals.

4.6. Distribution of articles by Country on the diagnosis and Prognosis of COVID-19 using different approaches

The Fig. 8 displays the number of articles published country wise on the diagnosis and prognosis of COVID-19 using statistical. Machine learning and Deep learning approaches. From the figure. it can observed that majority of the articles i.e., 21.7% have been contributed by the researchers of China. 18.1% articles have been contributed from the US researchers. The researchers of UK have contributed 14.9% of the articles in the study. 13.16% of articles have been contributed by Italy. The countries India, France, Republic of Korea and Japan have contributed 6.04%, 5.6%, 4.2% and 3.5% of the studies respectively. 2.13% have been contributed by the researchers of the countries like Egypt and Australia. The contributions from the researchers of Turkey are 1.4% and nearly 1.06% of total research has been contributed by the researchers of Canada, Germany and Saudi Arabia. However, the researchers of Pakistan, Netherlands and Brazil have contributed the 0.711% of the studies. Next, the researchers in the countries like Iran, Spain, Iraq, Greece, Thailand, and Singapore have contributed 0.35% of the studies.

Fig 8:

Country wise distribution of articles on diagnosis and prognosis of COVID-19.

4.7. Type of data used in COVID research

The following are the different types of data that have been used in the prediction, classification and forecasting of COVID-19 by the researchers and technocrats.

4.7.1. Usage of Clinical data

The forecasting of pandemics can be done with the information of registered number of COVID-19 cases along with their geographical locations. The most used dataset for the forecasting and prediction of COVID-19 is collated by John Hopkins University [131] . This dataset contains information such as the daily positive cases, total patients recovered per day and the death rates at a country as well as state level. Another data source Kaggle also contains the daily number of COVID-19 cases [132] . This dataset is annotated with attributes such as patient demographics, case reporting date and location. When working with real datasets, most of the researchers face class imbalance distribution issues. Another limitation is the variations in interventions, population densities and demographics have a major impact on the prediction. However, many good researchers have suggested for the use of real clinical data under the supervision of doctors for further diagnosis of COVID-19 other than online data. The main problem with online data is the presence of large extent of missing values, which may affect the proper analysis using any intelligent based methods.

4.7.2. Usage of Online data

The growth, nature and spread of COVID-19 can be predicted using the rich textual data available from various online sources. The interpretation of the epidemic is quite complicated as most of the studies have taken into account only a few determinants. Assuming the affect of virus in terms of positive and death case everywhere in the globe could produce inaccurate predictions. To make accurate predictions, it is necessary for the researches to understand the spread at the local, regional national as well as international levels. To be accurate, analysts would also need data from the medical authorities that distinguish between deaths caused by the coronavirus and deaths that would have happened due to anonymous disease. In most of the studies this data is not known, or not available. The range to which people pursue the local government's quarantine policies or measures to prevent the spread of infection is also difficult to find. These factors need to be considered to make accurate prediction of COVID-19 using online data while experimenting with any ML based techniques.

4.7.3. Usage of Biomedical data

The screening of medical images such as chest CT images or X-ray images is considered as an alternative solution to overcome the shortage of RT-PCR supply. Now-a-days, most of the research for the classification and prediction of COVID-19 has been carried on medical image data set, as medical image analysis helps the physicians in the accurate prediction of imaging modalities in pneumonia. From the literature review it has been observed, there are still some limitations in the usage of medical image datasets. The vast challenge in the medical diagnosis is the classification of medical images due to the limited accessibility of medical images. The process of automated distinction is difficult in CT images, as those share some common imagery characteristics among novel COVID and other forms of pneumonia. None of the studies reviewed by the authors accurately reported the high specificity of CT in distinguishing COVID-19 from other pneumonia with identical CT findings, thus restricting the usage of CT as a confirmatory diagnostic test. The presence of mild or no CT findings in many early cases of infection highlights the difficulties of early detection. Moreover, the CT scan tools are expensive and patients are exposed to more radiation. Even though chest x-ray images are cheaper and expose the patient to less radiation, these images have higher false diagnosis rate.

4.8. Distribution of articles by Prediction, Classification and Forecasting of COVID-19

From the Fig. 9 it has been observed that majority of work has been done on classification of COVID-19 (46%). Next, 36% of work has been done on the prediction of COVID-19. Only 20% of the work has been done on the forecasting of COVID-19.From the Figure., it can be concluded that less work has been done on prediction and forecasting due to the lack of real world datasets and availability of less number of training medical images.

Fig 9

Articles published on COVID-19 Prediction, Classification and Forecasting.

5. Challenges

This section focuses on some of the challenges that raised while implementing the intelligent diagnostic tools in the prediction of COVID-19.

5.1. Limitation of data

The implementation of predictive tools using deep learning and machine learning requires huge volume of data. Even though few datasets for medical images and textual analysis are publicly available, these datasets are small when compared to the needs of the deep learning approaches. The main reason for the scarcity of measured data is the frequent segregation of data over different geographical regions. Therefore, aligning of the data sources is one of the key issues that need to be solved. Another limitation arises in the development of quality datasets, as real time datasets contain poorly quantified biases. As results of this, poor outcomes will be produced if models are trained on unrepresentative data. Although transfer learning allow models to be specific with regional characteristics, it is difficult to perform model selection due to the fast moving nature of the data. Therefore, designing an analytical approach to overcome these limitations is one of the key challenges that need to be addressed. Most of the researchers and technocrats are also facing the problem of lack of real data. This issue can be accomplished by creating more real world datasets with updated COVID-19 data. Another issue that needs to be observed is the less involvement of medical community. In most of the studies, either few or no physician has involved in the assessment of medical images such as X-rays and CT images.

5.2. Accuracy of prediction

There exists a hidden risk in all scientific work as most of the methods in the study are based on statistical learning on quickly produced datasets. The outcome of the research may have biases that may impact the policies taken by the government in controlling the spread of disease. Therefore the challenge is to find the uncertainty of conclusions produced in this research. The correctness of the data can be ensured by providing reproducible conclusions. This, in turn creates the challenge of balancing the requirements against the urgency.

5.3. Usage of advance approaches

Some data science approaches such as ultrasound scans and magnetic resonance imaging (MRI) have limited exposure in combating COVID-19. Even though ultrasound scans have shown good performance as that of chest CT scans, no studies have explored the usage of ultrasound scans in the prediction of COVID-19. Though some studies [133] , have shown the efficient usage of MRI in predicting COVID-19 infections, still the approach remained unexplored due to the scarcity of adequate training data. Therefore, the challenge is to develop well-annotated dataset to make potential usage of new approaches in the prediction of COVID-19 infection.

5.4. Providing feasible solutions to developing countries

The usage of predictive tools in diagnosis of COVID-19 imposes a problem in developing countries that have limited access to healthcare facilities. Therefore, a key challenge is the development of tools that should be capable of deployment in economically underprivileged regions. For example, the development of mobile app for contact tracing should consider factors such as low cost, limited resources, accessibility to illiterates or disability people and support of multiple languages, so that it can be effectively deployed in economically deprived regions.

5.5. Necessity of advance intelligent systems on symptom based identification of COVID-19

Most of the studies have carried out only by considering the characteristics of COVID-19 and other pneumonia. The outcome of these studies may not produce accurate results as they have not considered the impact of other factors such as age, gender, diabetes, hypertension, chronic liver and kidney disease and so on. Therefore, to perform accurate predictions more research has to be carried on symptom based identification of COVID-19. Moreover, apart from prediction and forecasting based models, future research requires more attention on the classification problems on COVID-19 through various symptoms for easy and quick diagnosis. Also, most of the research is dedicated to top affected countries with this pandemic disease and further research may be enhanced for remaining mostly affected countries around the globe. Further, accurate prediction in the number of deaths and infections with advance machine learning approach is of utmost importance in the present scenario. As most of the machine learning models are highly accurate with large amount of data, so it may be worthy to note that with the increasing no of data and datasets of majorly COVID affected countries, many highly accurate models will be developed as a leading solution to this outbreak.

6. Conclusion

The researchers are always active in addressing the emerging challenges that arises in different application domains. In recent days, COVID-19 an infection caused by SARS-CoV-2 is one of the most emerging research areas as it affected more than 3 million people in 213 countries within a short span of time. Therefore, to empower the government and healthcare sector, it is necessary to analyze various forecasting and prediction tools. In this paper, an overall comprehensive summary of ongoing work in the prediction of COVID-19 infection using various intelligent approaches has been presented. Initially, the origin, dissemination and the affect of COVID-19 on the public health has been discussed. The major contribution of the study is the analysis of various prediction and forecasting models such as statistical, machine learning and deep learning approaches and their applications in the control of the pandemic. Following this, the analysis is broadened by making a critical investigation on the growth of studies carried on COVID-19 in various journals by country and the performance analysis of the statistical, machine learning and deep learning approaches. Finally, at the end of the paper some of the challenges observed as a part of systematic review are highlighted that may further help the researchers and technocrats to develop more accurate prediction models in the prediction of COVID-19.

The SARS-CoV-2 has infected about 3.48 million people and caused 2,48,144 deaths across 213 countries of the World as on May 3, 2020 according to the WHO. As the total of covid-19 cases registered has crossed all the previous records of the viral disease since last moths, it is considered to be the most dangerous disease till date. The whole world political, social, economic and financial structures have been disturbed because of the outbreak of pandemic. The economies of the world's topmost countries such as the US, China, UK, Germany, France, Italy, Japan and many others are at the edge of destruction. As 162 countries have moved into the lockdown to prevent the transmission of pandemic, the business across the world is operating in fear of an impending collapse of global financial markets. The sectors such as supply chains, trade, transport, tourism, and the hotel industry have been damaged extremely because of the pandemic. Other sectors like Apparel & textile, Building and Construction sectors have been affected adversely due to the lack of labour supply and availability of raw material. The other sector that is badly affected is the aviation sector as both international and domestic flights cancelled due to the implementation of lockdown in many countries. Even though the effect of lock down is less on essential goods retailer, other retailers such as shops and malls have been highly impacted by the pandemic. Due to the pandemic, the educational institutions have also been seriously affected and led to the shutdown of institutions which caused an interruption in the students learning activity as well as in internal and external assessment necessary for the qualification of the student. Even though several sectors have been affected, there are some sectors such as Digital and Internet Economy, Food based retail, Chemicals and Pharma sectors that have seen growth during the pandemic lockdown. The deep learning and machine learning approaches are useful in forecasting the impact of COVID-19 on different sectors which may help the government in implementing proper policies to overcome the economic crisis. From the systematic analysis, it is evident that computing intelligent approaches such as ML, DL, mathematical and statistical approaches have been profitably used in the prediction and screening of COVID-19 pandemic. It is observed that SVM, RF,K-means and linear regression of ML approaches have been mostly used for solving issues of COVID-19. While in case of DL, CNN and its variants are mostly utilized for predicting the pandemic. Even though computing approaches have been successfully used in the prediction and forecasting of COVID-19 pandemic, still there exist certain limitations such as limited availability of annotated medical images, not taking into account predictive end events such as mortality or admission into critical care unit while forecasting, not considering some features of medical images such as GGO, crazy-paving pattern, and bilateral involvement which are prerequisite in the diagnosis of COVID-19, training on small datasets and not coping with data irregularities need urgent focus to develop more accurate models. It is also observed that every researchers and modeling groups all over the world are presently facing the issue of scarcity of data. Therefore, real-world datasets with more epidemiological data need to be created for the development of more accurate prediction models. Moreover, the accuracy of prediction tools can be enhanced by the usage of advanced computing intelligent approaches such as ensemble method like bagging, stacking etc., application of optimization techniques, usage of artificial neural networks and higher order neural networks in the screening and prediction of COVID-19 which is considered as further scope of research.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors declare that this manuscript has no conflict of interest with any other published source and has not been published previously (partly or in full). No data have been fabricated or manipulated to support our conclusions.


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Mini review article, covid-19 and computer audition: an overview on what speech & sound analysis could contribute in the sars-cov-2 corona crisis.

covid 19 research paper in computer science

At the time of writing this article, the world population is suffering from more than 2 million registered COVID-19 disease epidemic-induced deaths since the outbreak of the corona virus, which is now officially known as SARS-CoV-2. However, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of COVID-19 directly or its symptoms such as breathing, dry, and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient well-being. We quickly guide further through challenges that need to be faced for real-life usage and limitations also in comparison with non-audio solutions. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread.

1. Introduction

The World Health Organisation's (WHO) office in China was first made aware of the previously unknown SARS-CoV-2 “Corona” virus on the last day(s) of the last year. On March 11, 2020, the WHO declared the disease triggered by the virus—COVID-19—as pandemic. The spread of the disease induced by the SARS-CoV-2 or “Corona” virus is assumed to underlie an exponential growth. However, whether there are long-term effects after recovery is yet to be fully researched. In the light of this dramatic spread, one is currently internationally witnessing drastic countermeasures that have not been seen in this form over decades in many countries. These include significant public “shut-down” measures to foster “social distancing” in order to slow down and control further spread.

As research globally is making massive efforts to contribute to better understand and fight the phenomenon from a medical and interdisciplinary point of view, also computer science and engineering in terms of “Digital Health” solutions aim at maximum exploitation of available and realisable means. In particular, in combination with artificial intelligence (AI), one can exploit a powerful tool, which so far has largely been tapped for prediction of COVID-19 spread [cf., e.g., ( 1 )], and computer vision (CV) approaches in the corona context such as for automatic screening for COVID-19 on CT images ( 2 , 3 ). There is, however, broader belief that also other signals including such from sensors on a smartphone could help even in the diagnosis of COVID-19 ( 4 ), e.g., the heart rate sensor.

In the following, we aim to provide an overview on what computer audition (CA), i.e., the application of computing for audio processing including “machine listening,” “computational paralinguistics,” and more general speech and sound analysis, but also synthesis, could contribute in this situation. To the best of the authors' knowledge, this resource is so far not used in practise despite offering a plethora of opportunities in this context.

The remainder of this overview is structured as follows: We first summarise phenomena more and less closely related to the case of COVID-19 that have already been targeted by CA and would be readily available. Examples include automatic recognition of speakers suffering from a cold or wearing a mask, breathing, coughing and sneezing sounds, or recognition of audio in spatial proximity. We then shift to the introduction of concrete use-cases how CA could benefit the ongoing global fight against the corona crisis. Subsequently, we introduce challenges and entry barriers from a technical as well as ethical and societal point of view, and discuss limitations before concluding this overview.

2. Computer Audition: Related Phenomena

In the following, we set out by show-casing what CA has already successfully targeted as audio use-cases for recognition, and appears related to the task of interest in this contribution—fighting the ongoing COVID-19 spread.

2.1. Speech Analysis

Speech analysis by computational means is highly related to the field of computational paralinguistics ( 5 ). The field has several related recognition tasks on offer. These are often well-documented in the framework of competitive challenge events such as the Interspeech Computational Paralinguistics Challenge (ComParE). The latter has—perhaps closest related to the COVID-19 case—in its 2017 edition featured the automatic recognition of speech under cold ( 6 ), i.e., automatically recognising speakers affected by a cold from the acoustics of their voice. In the challenge of last year, the continuous assessment of breathing patterns from the speech signal appears relevant ( 7 ), e.g., as basis to recognise often witnessed symptoms of short-breathiness and breathing difficulties related to COVID-19. The last ComParE challenge further targets the recognition of speech under mask, i.e., the automatic recognition whether a speaker is wearing a facial protective mask, and the recognition of emotion of elderly, which may become interesting in monitoring the aftermath of social isolation of elderly, as was discussed, e.g., in the U.K. for 3 months. On the age scale's opposite end, toddlers' crying sounds seem to be the only indicator to understand if they are suffering from COVID-19 symptoms. In the ComParE challenge series, infant crying was investigated in 2018 ( 8 ), and the valence, i.e., positivity of baby sounds in 2019 ( 9 ). As symptoms of COVID-19 can also include lack of appetite, it seems further interesting to reference to the EAT challenge ( 10 ): In this event, it was demonstrated that one can infer from audio whether speech under eating and eating sounds indicate eating difficulty and “likability” related to whether one enjoys eating. The assessment of sleepiness—a further symptom of COVID-19—was first featured in ComParE in 2011 ( 11 ) as binary task, and as continuous sleepiness assessment on the Karolinska sleepiness scale in 2019 ( 9 ). Also pain such as headache or bodily pain can accompany COVID-19; speech under pain has also been shown to be automatically accessible ( 12 , 13 ). When it comes to individual risk assessment and monitoring, speaker traits may be of interest. High mortality risk groups include the elderly, and a (slightly) higher mortality rate was so far seen in male individuals ( 14 ). Age and gender were also shown in the context of ComParE, and can be automatically determined reliably given sufficient speech material ( 15 ). A history of health issue can further indicate high risk. A number of health-related speaker state information relevant in this context has been shown feasible such as individuals suffering from asthma ( 16 ), head-and-neck cancer ( 17 ), or smoking habits ( 18 , 19 ).

Speaker diarization, i.e., determining who is speaking when, and speaker counting ( 20 ) can become of interest in the ongoing social distancing. When it comes to counter measures such as quarantine, or risk assessment of individuals, one could also consider the usage of automatic recognition of deceptive speech when people are questioned about their recent contacts or whereabouts, as their personal work and life interests may interfere with the perspective of being sent to quarantine. Deception and sincerity were targeted in ComParE in 2016 ( 21 ). Monitoring well-being of individuals during social distancing and quarantine can further find interest in depression and fear recognition. Both were shown feasible to be assessed from speech in the Audio/Visual Emotion Challenge (AVEC) event series ( 22 ) including from speech only at reasonable deviation on a continuous scale.

Generally speaking, speech audio also includes textual cues. Broadening up to Spoken Language Processing (SLP), this can also be of help to gather and analyse information from spoken conversations available in individual communications, news, or social media. For textual cues, this has already been considered ( 23 ). From a speech analysis perspective, this includes automatic speech recognition (ASR) and natural language processing (NLP).

2.2. Sound Analysis

From a sound analysis perspective, one may first consider such interest for COVID-19 use-cases that are produced by the human body. In the context of COVID-19, this includes mostly the automatic recognition of coughs ( 24 – 26 ) including dry vs. wet coughing ( 27 ) and dry vs. productive coughing ( 28 ) and sneeze ( 26 ), swallowing, and throat clearing ( 25 ) sounds—all showcased at high recognition rates. As severe COVID-19 symptoms are mostly linked to developing a pneumonia, which is the cause of most deaths of COVID-19 as suggested by post-mortem biopsies ( 29 , 30 ), it further appears of interest that different breathing patterns, respiratory sounds, and lung sounds of patients with pneumonia can be observed through CA ( 31 ), even with mass devices such as smart-phones ( 32 ). Of potential relevance could also be the already possible monitoring of different types of snoring sounds ( 33 ), including their excitation pattern in the vocal tract and their potential change over time to gain insight on symptoms also during sleep. Further, highest risk of mortality from COVID-19 has been seen for such suffering from cardiovascular disease followed by chronic respiratory disease. In ComParE 2018, heart beats were successfully targeted from audio for three types of heart status, namely, normal, mild, and moderate/severe abnormality. Hearing local proximity from ambient audio further appears possible ( 34 ), and could be used to monitor individuals potentially too close to each other in the “social distancing” protective countermeasure scenarios. 3D audio localisation ( 35 ) and diarization further allows for locating previously recognised sounds and attributing them to sources. This could further help in the monitoring of public spaces or providing warnings to users as related to individuals potentially being locally too close with directional pointers. Audio source separation and denoising ( 36 ) of stethoscope sounds and audio ( 37 ) for clinicians and further processing can additionally serve as tool.

3. Potential Use-Cases

Let us next elaborate on use-cases we envision as promising for CA in the context of COVID-19. A coarse visual overview on the dependence of CA tasks and these use-cases is provided in Table 1 . Check-marks indicate that the already available automatic audio analysis tasks listed in the left column appear of interest in the three major use-case groups listed in the right-most three columns. Note that these are indicative in nature. Further, to provide an impression of the “readiness,” performance indications are given. For a strict comparability of these, they are only provided for tasks that have been featured in the Interspeech Computational Paralinguistics Challenge (ComParE) series. 1 Shown are the best results after the challenge including by fusion of best participant systems. Likewise, it is assured that test-set labels were unknown to participants and a strict subject independence and challenging conditions including no ability for “cherry picking” alike preselection of test examples are assured. The results overall show that under realistic conditions, the tasks are handled highly above chance level, yet, clearly below “perfect” recognition.

Table 1 . Interdependence of computer audition (CA) tasks and potential use-cases in the context of the corona crisis.

3.1. Risk Assessment

A first use-case targets the prevention of COVID-19 spread by individual risk assessment. As shown above, speaker traits such as age, gender, or health state can be assessed automatically from the voice to provide an estimate on the individual mortality risk level. In addition, one can monitor if oneself or others around are wearing a mask when speaking, count speakers around oneself, and locate these and their distance to provide a real-time ambient risk assessment and informative warning.

3.2. Diagnosis

While the standard for diagnosis of COVID-19 is currently a nasopharyngeal swab, several other possibilities exist including chest CT-based analysis as very reliable resource as outlined above. Here, we consider whether an audio-based diagnosis could be possible. While it seems clear that such an analysis will not be suited to compete with the state-of-the-art in professional testing previously named, its non-invasive and ubiquitously available nature would allow for individual pre-screening “anywhere,” “anytime,” in real-time, and available more or less to “anyone.” To the best of the authors' knowledge, no study has yet systematically investigated audio from COVID-19 patients vs. highly varied control group data including such suffering from influenza or cold and healthy individuals. Unfortunately, coughing and sneezing of COVID-19 patients does not differ significantly to human perception from “normal” patients. This includes lung and breathing sounds. However, ( 38 ) assume that abnormal respiratory patterns can be a clue for diagnosis. Overall, by that, it seems unclear if diagnosis from short audio samples of patients could be directly possible, given that most speech or body sounds are likely not to show significant differences for closely related phenomena such as influenza or cold, but a number of encouraging results show that breathing, coughing, and speech sounds could be suited ( 39 ). The current Interspeech 2021 ComParE event therefore features COVID-19 recognition from forced cough and speech.

Rather, we believe that a histogram of symptoms over time in combination with their onset appears highly promising. Table 2 visualises this concept in a qualitative manner by coarse ternary quantification of each symptom or “feature” from a machine learning perspective. 2 Each of the symptoms in the table can—as outlined above—(already) be assessed automatically from an intelligent audio sensor. In a suited personal application such as on a smartphone or smartwatch, smart home device with audio abilities, or via a telephone service, etc., one could collect frequency of symptoms over time and from the resulting histogram differentiate with presumably high success rate between COVID-19, influenza, and cold. By suited means of AI, a probability could be given to users how likely their symptoms speak for COVID-19. Of particular interest thereby is also the “Onset Gradient” feature in Table 2 . It alludes to whether the onset of symptoms over time is gradual (i.e., over the span of up to 2 weeks or more) or rather abrupt (i.e., within hours or a few days only), which can be well-observed by AI analysis in a histogram sequence updated over time. Collecting such information from many users, this estimate for histogram-based diagnosis of COVID-19 can be improved in precision over time if users “donate their data.” In addition, clinicians could be given access to the histogram or be pointed to typical audio examples in a targeted manner remotely that have been collected over longer time to speed up the decision whether the users should go for other more reliable forms of testing. This could help to highly efficiently pre-select individuals for screening.

Table 2 . Qualitative behaviour of symptoms of COVID-19 vs. cold and influenza (flu): Tentative histogram by symptom (“feature”/“variable”) in ternary quantification [from no/low (“+”) to frequent/high (“+ + +”)].

3.3. Monitoring of Spread

Beyond the idea of using smartphone-based surveys and AI methods to monitor the spread of the virus ( 40 ), one could use CA for audio analysis via telephone or other spoken conversation. An AI could monitor the spoken conversations and screen for speech under cold or other symptoms as shown in Table 2 . Together with GPS coordinates from smart phones or knowledge of the origin of the call from the cell, one could establish real-time spread maps.

3.4. Monitoring of Social Distancing and Effects

Social distancing—in already diagnosed cases of COVID-19 or direct contact isolation of individuals—might lead to different negative side effects. People who have less social connexion might suffer from even a weaker immune system, responding less well to pathogens ( 41 ). Especially, the high-risk target group of elderly could even encounter suicidal thoughts and develop depression or other clinical conditions in isolation ( 42 ). CA might provide indications about social interaction, exemplary speaking time during the day via phone or other devices, as well as measure emotions of the patient throughout the day or detecting symptoms of depression and suicidal risk ( 43 ).

In addition, the public obedience and discipline in social distancing could be monitored with the aid of CA. AI allows to count speakers, locate them and their potential symptoms as reflecting in the audio signal (cf. Table 2 ), and “diarize” the audio sources, i.e., attribute which symptoms came from which (human) individual. Likewise, public spaces could be empowered by AI that detects potentially risky settings, which are overcrowded, under-spaced in terms of distance between individuals, and spot potentially COVID-19 affected subjects among a crowd, and whether these and others are wearing a protective mask while speaking.

3.5. Monitoring of Treatment and Recovery

During hospitalisation or other forms of treatment and recovery, CA can monitor the progress, e.g., by updating histograms of symptoms. In addition, the well-being of patients could be monitored similarly to the case of individual monitoring in social distancing situations as described above. This could include listening to their emotions, eating habits, fatigue, or pain, etc.

3.6. Generation of Speech and Sound

While we have focused entirely on the analysis of audio up to this point, it remains to state that there may be also use-cases for the generation of audio by AI in a COVID-19 scenario. Speech conversion and synthesis could help those suffering from COVID-19 symptoms to ease their conversation with others. In such a setting, an AI algorithm can fill in the gaps arising from coughing sounds, enhance a voice suffering from pain or fatigue and further more by generative adversarial networks ( 44 ). In addition, alarm signals could be rendered which are mnemonic and re-recognisable, but adapt to the ambient sound to be particularly audible.

4. Challenges

The fight against COVID-19 has been marked by a race to prevent too rapid spread that could lead to peak infection rates that overburden the national health systems and availability of beds in the intensive care units leading to high mortality rates. Further, at presence, it cannot be clearly stated whether or not COVID-19 will persistently stay as disease. However, recent research and findings ( 45 ) as well as model calculations indicate that COVID-19 will continue to heavily spread over the next months in different areas of the world. Enhanced social distancing might delay the spreading. Additionally, at the moment there is no solid research available to prove persistent immunity against the virus after an infection with COVID-19. Therefore, the need to apply measures of enhanced risk assessment, diagnosis, monitoring, and treatment is urgently necessary to support the current medical system as well as to get COVID-19 under control.

4.2. Collecting COVID-19 Patient Data

Machine learning essentially needs data to learn. Accordingly, for any kind of CA application targeting speech or sounds from patients suffering from COVID-19 infection, we will need collected and annotated data. At present, such data are hardly publicly available for research purposes, but urgently needed. Hence, a crucial step in the first place will be to collect audio data including highly validated such from diagnosed patients and ideally control subjects under equal conditions and demographic characteristics including control data with a rich representation of further respiratory and other diseases.

4.3. Model Sharing

In order to accelerate the adaptation of machine learning models of CA for COVID-19, exchange of data will be crucial. As such data are usually highly private and sensitive in nature, the recent advances in federated machine learning ( 46 ) can benefit the exchange of personal model parameters rather than audio to everyone's benefit. Likewise, users of according services can “donate their data” in a safe and private manner.

4.4. Real-World Audio Processing

Most of the tasks and use-cases listed above require processing of audio under more or less constrained “in-the-wild” conditions such as audio recording over telephone, VoIP, or audio takes at home, in public spaces, or in hospitals. These are usually marked by noise, reverberation, varying distance to microphone(s), transmissions with potential loss, and further disturbances. In addition, given the pandemic character of the SARS-CoV-2 corona crisis, one will ideally need to be robust against multilingual, multicultural, or local speech and sound variability.

4.5. Green Processing

Green processing summarises here the idea of efficiency in computing. This will be a crucial factor for mobile applications, but also for big(ger) data speech analysis ( 47 ) such as in the case of telephone audio data analysis. It includes conservative consumption of energy such as on mobile devices, efficient transmission of data such as in the above named federated machine learning in order not to burden network transmission, memory efficiency, model update efficiency, and many further factors.

4.6. Trustability of Results

Machine learning and pattern recognition methods as used in CA are usually statistical learning paradigms and hence prone to error. The probability of error needs to be (a) estimated, known as confidence measure estimation, and (b) communicated to users of CA services in the COVID-19 context to assure trustability of these methods. One step further is that results should ideally be explainable. However, eXplainable AI (XAI) itself is at this time a young discipline, but provides an increasing method inventory allowing for interpretation of results ( 48 ).

4.7. Ethics

Many of the above suggested use-cases come at massive ethical responsibility and burden, which can often only be justified in times of global crisis as the current one. This includes mostly many of the above sketched applications of CA for monitoring. Assuring privacy at all times will be crucial to benefit only the goal of fighting COVID-19 spread without opening doors for massive miss-use. At the same time, balancing between individual interests and the beneficence of groups and societies will need to be carefully considered.

In addition, apart from responsible research, it needs to be assured that the data are representative of all users in all use-cases avoiding potential algorithmic bias. Indeed, the suggested CA algorithms could function better for some parts of the population, because algorithms were trained with data from only one subculture due to different access to resources/technologies. As an example, this could create an asymmetry in the detection of symptoms of subparts of a population (inter- and intra-countries). Deploying the same solution at scale would favour certain social groups and disfavour others ( 49 ).

Next, one must assure that common points of reference for comparison across studies are given, the aim of an audio task is well-decided upon, results are interpretable, and communicated to all, including in particular communication of potential limitations ( 50 ). Further concerns in this context will discuss legal and societal implications. All of these cannot be discussed here—rather, we can provide pointers for the interested reader as starting points ( 50 – 55 ).

While the technology seems ripe for application, one may ask if we should use it? Or, are the ethical questions that rise from these technologies enough to pause the development of the suggested CA techniques at scale and think on the ethical solutions first? And, do we have enough ethical knowledge today to put enough constraints on the suggested CA applications to make them secure/ethical? These questions touch upon many actions that have been taken during the ongoing pandemic, but of course, this will not justify risking massive personal data leakage or restrictions of personal rights due to missclassifications by AI or more specifically CA. Decisions will need to be made individually per use-case and potential CA solution carefully weighing benefit against risk.

5. Limitations

Following the described advantages and the potential uses of these technologies in the case of COVID-19, we now provide a critical thinking about their limitations and discussion about their usability in the described use-cases.

First, as to the tasks described in Table 1 , in a non-negligible number of cases the data used for the experiments are still simulated. Hence, the extrapolation from this to real-world scenarios is far from being trivial. Also, in all use-cases, we assumed that there is access to ideal sound recordings so one can track a person at home and in public spaces. Today, cities and homes are not equipped with microphone networks, so smartphones are the preferred choice. On the one hand, these recordings are potentially extremely noisy, reverberated, and marked by package loss, which limits the applicability of the previous research; on the other hand, most use-cases assume that the smartphone is constantly listening, and that AIs are able to detect, for example, if a person is eating (even before wondering if they are eating with appetite or not). Furthermore, it may appear difficult to see how we can envision the application for locating and detecting sound sources from recordings made by smartphones.

Few or none of the technologies mentioned are fully operational today, so we can use them effectively for the proposed objectives (as opposed to computer vision which is already commonly used). And if time is indeed a challenge in this period, it seems that the time necessary to exploit CA efficiently for the COVID-19 task could be the biggest challenge, as software development, deployment, maintenance, testing including medical such, and alike are usually very demanding in time.

Further, it has been noticed that only some specific elements are directly related to COVID-19 (such as pre-diagnosis of COVID-19 from breathing, coughing, or speech). In particular, for the various paralinguistis recognition tasks, these would otherwise further include lung sounds as compared to others diseases such as common cold/influenza. An indication on the degree of immediate relation to COVID-19 is provided in Table 1 . On the other hand, many of the introduced aspects bear interest even from monitoring of cold/influenza and other respiratory or even related viral diseases perspective.

Also, in many of the cases described, another simpler means can certainly be used instead to arrive at the same information. For example, bio-signals allow a more direct and more efficient measurement of a person's state of health and its evolution; smartphones and GPS tracking are very effective in locating individuals. Hence, a multi-modal combination of audio and other modalities appears very promising depending on the individual requirements and settings.

In Table 3 , we hence investigate whether the suggested CA applications would work better than those that are already implemented at scale or in high technology-readiness state. On a similar line, we indicate where the mentioned CA techniques could complement the current monitoring methods particularly well. We provide the most common alternative methods for the three major use-cases risk-assessment, diagnosis, and monitoring as introduced in section 3. Other alternatives are recently developed and need to be related in a similar fashion to CA ( 56 ), once being ready for usage at scale. Also note that the table merely presents a coarse indication. It will depend on the detailed usage which approach is to be preferred. For example, the indicated equality in effort for contact tracing by bluetooth or alike vs. CA is a coarse estimate as, on the one hand, a high cost for development, advertisement, and distribution of such solutions is required. On the other hand, a centralised service based on CA will also come at a high effort: CA population tracking could be extremely expensive in terms of resources and development. Indeed, application development, server infrastructure, data analysis centers, data encryption, storage, and anonymisation as well as all the costs of maintaining these services could easily add up transforming CA solutions in over-priced servers difficult to maintain and without the certainty that they will detect large numbers of COVID-19 cases.

Table 3 . Nine key aspects: Promising complimentarity of CA with other methods, tentative advantages (“+”) vs. disadvantages (“-”) or equality (“0”) of using audio as the modality as compared to other more established methods used at scale in the medical and related setting.

As to the alternatives to CA by use-case, for risk assessment, this is currently mainly achieved by the named contact tracing apps run on one's smart phone by bluetooth or 5G methods ( 57 ). When active on the phones of two individuals in sufficiently close proximity such as less than 2 m for a minimum set time of more than 15 min, the contacts are stored (usually only locally in anonymous ways). Users that report COVID-19 positive diagnosis are informed back to the service in anonymous forms. Such an approach has been used already for other infectious diseases (e.g., for HIV or tuberculosis). Another increasingly used method is thermal camera based body temperature measurement ( 58 ) often in the context of access, which has also been vastly used before, e.g., at airports. A single thermal camera can be used together with subject tracking to assess many individuals by a single device.

For diagnosis, the common present alternatives used at scale are upper respiratory samples such as regarding reverse transcription-polymerase chain reaction (RT-PCR), and blood samples ( 58 ), or, chest X-ray. A mobile health alternative is found by intelligent heart rate analysis such as from wrist-worn devices ( 59 ).

Monitoring of spread can alternatively, for example, be fulfilled by analysing wastewater ( 60 ).

Monitoring of social distancing is realisable also by video-based tracking ( 61 ) of individuals or chip-based such as via 5G, bluetooth, or NFC and related technologies, as used, e. g., in factories ( 57 ) usually requiring each participant to be equipped, accordingly. Monitoring of social distancing effects is largely related to affective, behavioural, and social computing in more general, for which there exist a range of other modalities—mainly physiology, text, or video ( 51 ). For monitoring of social distancing treatment and recovery, mainly the usage of medical testing and mere human assessment form major alternative options.

How CA or CA combinations would compare (e.g., in terms of false positives/false negative rates or detection time) to the already implemented medical and alternatives systems in society will need to be broken down in detail. For instance, questions such as do we have any clues to think that CA will be more robust in terms of diagnostic than the current medical monitoring system will need careful further investigation.

Besides such more technical questions, practical questions on acceptance will also largely dominate the usefulness of CA methods for COVID-19. The tracing application experience has in some countries shown that the population was not fully ready to give away their data. Similar of even bigger societal challenges and limitations of the deployment of CA applications in the context of COVID-19 at scale need to be expected.

6. Discussion

In this short overview, we provided pointers toward what CA could potentially contribute to the ongoing fight against the world-wide spread of the SARS-CoV-2 virus known as “corona crisis” and the COVID-19 infection triggered by it. We have summarised a number of potentially useful audio analysis tasks by means of AI that have already been demonstrated feasible. We further elaborated use-cases how these could benefit in this battle, and shown challenges arising from real-life usage. The envisioned use-cases included automated audio-based individual risk assessment, audio-only-based diagnosis directly from speech or (forced) cough-sounds and by symptom frequency and symptom development histograms over time in combination with machine learning, and several contributions to monitoring of COVID-19 and its effects including spread, social distancing, and treatment and recovery besides use-cases for audio generation. At the time of writing, it seems that what matters most is a rapid exploitation of this largely untapped potential. Obviously, in this short overview, not all possibilities could be included, and many further potential use-cases may exist. We also showed key limitations, but others will exist. Further, the authorship is formed by experts on CA, digital health, and clinicians having worked with COVID-19 infected patients over the last months—further insights from other disciplines will be highly valuable to add. The corona crisis demands for common efforts on all ends—we truly hope computer audition can add a significant share to an accelerated success of the crisis' defeat.

Author Contributions

BS wrote the manuscript. All authors contributed ideas and input and read over the manuscript.

This work was further partially supported by the Zhejiang Lab's International Talent Fund for Young Professionals (Project HANAMI), P. R. China, the JSPS Postdoctoral Fellowship for Research in Japan (ID No. P19081) from the Japan Society for the Promotion of Science (JSPS), Japan, and the Grants-in-Aid for Scientific Research (No. 19F19081 and No. 17H00878) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. We acknowledge funding from the EU's HORIZON 2020 Grant No. 115902 (RADAR CNS).

Conflict of Interest

BS and DS were employed by the company audEERING GmbH.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This manuscript has been released as a pre-print in a shorter version at ( 46 ). We express our deepest sorrow for those who left us due to COVID-19; they are not numbers, they are lives. We further express our highest gratitude and respect to the clinicians and scientists, and anyone else these days helping to fight against COVID-19, and at the same time help us maintain our daily lives.

1. ^

2. ^ based on , , , all assessed on March 20, 2020.

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Keywords: corona virus, SARS-CoV-2, COVID-19, computer audition, machine listening, computational paralinguistics

Citation: Schuller BW, Schuller DM, Qian K, Liu J, Zheng H and Li X (2021) COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis. Front. Digit. Health 3:564906. doi: 10.3389/fdgth.2021.564906

Received: 22 May 2020; Accepted: 03 February 2021; Published: 29 March 2021.

Reviewed by:

Copyright © 2021 Schuller, Schuller, Qian, Liu, Zheng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Björn W. Schuller,

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Health Technologies and Innovations to Effectively Respond to the COVID-19 Pandemic

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When the COVID-19 pandemic began to spread, universities and research institutes all over the world mobilised to direct their efforts to finding a cure for the illness and to mitigate its effects. As societies closed down to prevent the spread of COVID-19, associated problems surfaced and became the focus of scientific efforts as well. Now that many countries are ‘opening up’ again, without a verified therapy or vaccine for the disease, new research is needed to accommodate a safe return to economic and social activity. During each of these phases, Computer Science Research has played a considerable role. Below some of our colleagues discuss their research discipline and the role that it has played and will continue to play in addressing this pandemic and related phenomena.

Computational Biology

Colm Ryan  

All modern biology is to some extent dependent on computers and SARS-CoV-2 biology is no different. For example, whenever a genome is sequenced computers are used to assemble the genome and make sense of the resulting data. The first SARS-CoV-2 virus genomes from Irish patients were sequenced and assembled in UCD . Computational analyses of these genomes revealed that one of first strains in Irish patients was identical to those found in Wuhan, while other early strains in Irish patients were more similar to a group of strains found elsewhere in Europe, and these came from patients with a history of travel to Northern Italy. Computational approaches are also being used to try and identify existing drugs that might be repurposed to help fight SARS-CoV-2 infection. Perhaps less exciting, but no less important, than the computational tools for analysing genomes and finding new drugs is the extensive computational infrastructure that helps share data (e.g. genomes, protein structures, scientific publications) among researchers. For example the COVID-19 Data Portal has been set up as a pan-European effort to rapidly share information among researchers worldwide.  

Software Engineering

John Murphy  

Governments and public health authorities around the world are working on contact tracing app solutions to help curb the spread of COVID-19 pandemic. These would record the interactions of an infected person in the days prior to their development of symptoms, and help authorities to instruct these people to self isolate for a period of time. To work effectively people would need to share their location information and their health status with a centralized database. These kinds of information are high on the list of concerns that people normally have around their privacy. As a result many users will not agree to use these apps in sufficient numbers to be useful. 

There are other apps and devices that can be used to keep strict social distance and some of these are already on the market. These might work without collecting or transferring any sensitive data. It is possible that newer technologies like Blockchain could help keep high standards of privacy. It will be the job of researchers to not only create these technologies that are in line with people’s need for privacy, but also to assess and critique each other’s work to make sure that these technologies are transparent in their collection or use of data. This is our best chance to allay the fears of the general public and encourage enough people to use these technologies such that they can start to be effective.

Computer Science Ethics

Abeba Birhane  

While most of us are clear as to the benefits of applying computational tools to the search for diagnostics and mitigating the spread of COVID–19, we need to remember that any computational solution to matters of a social nature, even a pandemic, need the utmost caution. All COVID–19 related work has societal implications and the potential to directly impact individual people. For example, digital contact tracing solutions pose fundamental privacy rights   and open the door for potentially irreversible surveillance systems;  predictive systems used on COVID–19 patients raise serious concerns including racial bias. Historically, algorithmic prediction in the medical sciences has suffered from lack of understanding of how seemingly ‘neutral’ metrics are racially biased , and the resulting technology has carried this bias through, and in some cases compounded it. Researchers need to constantly look for signs that their solutions might be echoing or magnifying social problems such as inequality and strive to acknowledge this and minimise its effects.

While computer scientists may be eager to contribute, work that is unaware of social and ethical ramifications could do more harm than good. The social world is immensely complex, often defying simple solutions. Although technological solutions might appear as magical shortcuts, it is crucial to remember technology is not a substitute for a well-functioning national health system or human expertise. Furthermore, not all problems have technological solutions and the more we veer into the social sphere, the truer this becomes. Well-balanced computational research considers its societal implications and who is likely to be negatively impacted, as well as being clear about what the technology can and cannot do. 

Geospatial Research

Gavin McArdle and Michela Bertolotto  

Epidemics and pandemics are spatial and temporal in nature as the spread of the  disease occurs within a geographic space and time. Geospatial and Spatio-Temporal research concerns the development and application of computational techniques to store, process, analyse and report the relationships that exist in spatial and temporal datasets. The task is complex as it requires both the temporal and spatial correlations to be accounted for. Any technologies introduced to help with pandemic understanding and response needs to account for space and time.

Given the current COVID-19 pandemic, there has been an increased interest in the use of geospatial and spatio-temporal analysis techniques. In data collection, applications which monitor the symptoms and duration of the disease, must also be spatially enabled to effectively report on interactions between people/phones. The analysis of data can help us understand the effects of conditions on the spread of the  virus (e.g. pollution). Human movement modelling and simulation including trajectory   analysis can help us to understand and predict the spread of COVID-19 while Social Network Analysis (modelling and prediction) can detect and predict new outbreaks. Finally, intuitive map-based interfaces and dashboards can present and disseminate data and information to experts and the public.

Speech, Audio, Visual Signal Processing

Andrew Hines  

Although many of us were extremely grateful to be able to carry on our work and social lives through video-conferencing while remaining at home, it quickly became apparent that online meetings, face-to-face chats with family and virtual beer gardens were not the same as the real thing. People found that they were more tired after a day of online meetings than they would be after a day’s work in the office. Researchers who specialise in Quality of Experience (QoE) research can help explain why this is and what we can do in the short term to lessen the effects of ‘Zoom fatigue’, while at the same time, they are designing models and tools to improve these technologies and make them less taxing to use.

Quality of Experience researchers work to improve our listening and viewing experience. QoE measures the degree of delight or annoyance that a user has with an application, and works through the entire technological system to improve this. QoE researchers create hierarchies of listening or seeing needs. These hierarchies don’t just reflect the physicality of listening or seeing, they also need to be culturally informed and responsive to the recipient’s environment. By arranging them in a hierarchy, researchers can prioritise particular features in cases where it is not possible to optimise all aspects of experience, such as reduced capacity in a network. Recent advances have led to real-time tools to improve the quality of YouTube videos While they work hard to make all of our online experiences as enjoyable as possible, QoE researchers can already share their expertise on how our senses respond to our imperfect solutions, and what steps we can take now to make our online activities less taxing.

Cyber Security

Anca Jurcut  

One feature which has been much commented on is that the world has reached a level of technology that allows millions of us to work and study from home. People who are ill or at risk can remain in their houses and access food, healthcare and almost all of life’s necessities and comforts from home. What has not been appreciated by many, however, is that this shift has altered the global cyber security landscape, and this has been seen as an opportunity for cyberciminals. In recent weeks, Europol , the United States Department of Homeland Security, and the UK National Cyber Security Centre have warned that cyber attacks have increased and are expected to continue to do so.

A work-from-home environment pushes the defence line outside of an organisation. Setting up remote working services could pose a potential security risk when combined with possible human-error-enabled security failures. For example, in specific conditions and crisis situations such as the pandemic of coronavirus (SARS-CoV-2),  e-learning systems became crucial for the smooth performing of teaching and other educational processes. In such scenarios, the availability of e-learning ecosystem elements is crucial and the DDoS (Distributed Denial of Service) attacks should be prevented. 

Data Science

Barry Smyth

One of the features of the COVID-19 pandemic has been the wealth of information and datasets that have been released to the public about everything from the latest academic research to regional case counts, death rates, and other relevant statistics. This has been both a blessing and a curse, as much of this data is quite raw, and appears to compare ‘like for like’, when in fact different countries had different methods of data collection and different chronologies of the disease. But with the release of such a large amount of data, data scientists have been able to get to work on it, exploring many aspects of the pandemic in the hope of at least better understanding its effect on the world, but perhaps also in the hope that such analyses will reveal help us to respond more effectively going forward.

Initially researchers began by looking at simple case counts and fatality statistics, with a view to charting and comparing how different countries were responding. But from there, and as more data have been released, it has been possible to explore many other aspects of the pandemic, and to use a wider range of data to corroborate or check initial figures. For example, I was one of the first people to use data to estimate all-cause mortality to get a better sense of the impact of COVID-19 on death rates, beyond the official figures; the results suggested a potential under-counting of COVID-19 deaths at the time. More recently my work has looked at using a combination of mobility data and transmission rate estimates to evaluate and compare the different lockdown  strategies that countries have employed. The hardest-hit countries tended to start their lockdowns later and more gradually than countries that have registered fewer deaths per capita. There is also evidence that those countries that were cautious in lifting their lockdowns have controlled their post-lockdown outbreak more effectively than countries that lifted restrictions sooner.

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