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A case–control study of factors associated with SARS-CoV-2 infection among healthcare workers in Colombia

• Merida Rodriguez-Lopez   ORCID: orcid.org/0000-0001-8245-0811 1 ,
• Beatriz Parra 2 ,
• Enrique Vergara 1 ,
• Laura Rey 1 ,
• Mercedes Salcedo 2 ,
• Gabriela Arturo 3 ,
• Liliana Alarcon 3 ,
• Jorge Holguin 1 , 3 &
• Lyda Osorio 2  

BMC Infectious Diseases volume  21 , Article number:  878 ( 2021 ) Cite this article

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Healthcare Workers (HCW) are repeatedly exposed to SARS-CoV-2 infection. The aim of this study was to identify factors associated with SARS-CoV-2 infection among HCW in one of the largest cities in Colombia.

We conducted a case–control study, where cases had a positive reverse transcription-polymerase chain reaction and controls had a negative result. Participants were randomly selected and interviewed by phone. Analyses were performed using logistic regression models.

A total of 110 cases and 113 controls were included. Men (AdjOR 4.13 95% CI 1.70–10.05), Nurses (AdjOR 11.24 95% CI 1.05–119.63), not using a high-performance filtering mask (AdjOR 2.27 95% CI 1.02–5.05) and inadequate use of personal protective equipment (AdjOR 4.82 95% CI 1.18–19.65) were identified as risk factors. Conversely, graduate (AdjOR 0.06 95% CI 0.01–0.53) and postgraduate (AdjOR 0.05 95% CI 0.005–0.7) education, feeling scared or nervous (AdjOR 0.45 95% CI 0.22–0.91), not always wearing any gloves, caps and goggles/face shields (AdjOR 0.10 95% CI 0.02–0.41), and the use of high-performance filtering or a combination of fabric plus surgical mask (AdjOR 0.27 95% CI 0.09–0.80) outside the workplace were protective factors.

This study highlights the protection provided by high-performance filtering masks or double masking among HCW. Modifiable and non-modifiable factors and the difficulty of wearing other protective equipment needs to be considered in designing, implementing and monitoring COVID-19 biosafety protocols for HCW.

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Introduction

Over 55 millions of people were infected worldwide by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in 2020 [ 1 ]. In previous coronavirus pandemic outbreaks, SARS-CoV-1 in 2003 and the Middle East Respiratory Syndrome in 2012, between 10 and 20% of infected people were Healthcare Workers (HCW) [ 2 , 3 ]. In the current pandemic, the prevalence among HCW varies between countries from 2 to 30% [ 4 ]. The Coronavirus Disease 2019 (COVID-19) caused by SARS-CoV-2, affects people’s lives and threatens their biological [ 5 , 6 ], physiological [ 7 , 8 ], family and social health [ 9 , 10 ]. HCW are repeatedly exposed to the virus leading to an increased risk of the disease [ 11 ] and sequelae [ 12 ] compared to the general population. Hence, COVID-19 could reduce the workforce availability to respond to this emergency.

The first case of SARS-CoV-2 in Colombia was reported in March 2020. Seven months later, the Colombian National Institute of Health has informed over 16,500 infected HCW, most of whom were associated to the workplace [ 13 ]. In a descriptive study of HCW in Cali, one of the largest cities in Colombia, 65% of infections were related to the workplace and the most affected were women and nursing assistants [ 14 ]. To date, there is scarce evidence in Latin America, concerning risk factors for the infection particularly among HCW, who are exposed to both, workplace and community transmission. Studies are mostly from Asia, Europe and North America [ 15 , 16 , 17 , 18 ]. They have focused on nurses and medical staff, and they have mainly evaluated the presence of symptoms and the exposures to occupational factors, including aerosol-generating procedures [ 15 ]. Cultural differences and availability of resources between countries and institutions, limit a direct extrapolation of previous findings. Less is known about the effect of factors related to potential community transmission or the risk among other hospital workers. Moreover, there is controversy abound the appropriate types of masks for HCW in community settings [ 19 ]. Therefore, the aim of this study was to determine the factors associated with SARS-CoV-2 infection among HCW in Cali, Colombia.

Subjects and methods

Study design.

We conducted a case–control study in HCW who served in health care institutions in Cali, Colombia. Participants were identified by merging the database of positive reverse transcription-polymerase chain reaction (RT-PCR) results with the routine surveillance system of COVID-19 (event code 346) or acute respiratory infections (event codes 345 and 348), who were reported with or without symptoms (as part of cluster investigations), between June 10 th and July 25 th , 2020. This time framework matches the first peak of the epidemic curve in Cali [ 20 ]. Cases and controls were randomly (simple random sampling without replacement) selected from those identified as HCW with a positive and negative test, respectively. The outcome status was confirmed with the database of epidemiological investigation of COVID-19 in health care facilities compiled by the local health authorities of Cali and during the telephone interview. This strategy ensures a representative sample of different health care institutions independently of size, patient type, care level, management or service provided. Sample size was estimated as 111 participants for each group with 80% power, 95% level of confidence, 18% of exposure among controls, Odds Ratio (OR) of 2.5, 1:1 allocation ratio, and 10% of withdrawal.

HCW were defined as those working in healthcare environments regardless of whether they were directly or indirectly involved in clinical activities such driving an ambulance or worked in a hospital or in homecare. Potential participants were contacted by phone and eligibility criteria were confirmed (18 years or older, not being pregnant or having a coagulopathy, and working in a health care institution that have the potential to assist COVID-19 patients, or being in contact before they had a RT-PCR test with infectious materials such as body fluids and contaminated surfaces and supplies). The study protocol was part of the public health research to face the pandemic and was revised by the Universidad Javeriana Cali Ethics Committee. Inform consent was obtained online for all participants.

Data collection

Data was collected by two trained researchers via telephone and using a structured questionnaire. The questionnaire included modifiable and non-modifiable factors: sociodemographic, clinical and lifestyle factors referred to six months before the test result, psychological factors referred to one month before the test. Occupational, exposure to COVID-19 cases, social behavior and personal protection equipment (PPE) factors referred to two weeks before the RT-PCR test. Feeling scared or nervous or having insomnia were evaluated by a five-point Likert scale, and further dichotomized as never or anytime. Height, weight, and compliance to recommended PPE use were self-reported. The exposure to a positive person was evaluated by the question: “To your knowledge, were you in contact with a person diagnosed with COVID-19, at least 2 weeks prior to the test?” A high-performance filtering mask was considered as the use of N95, P100 or M3. The frequency of use of each PPE at work were classified as always wearing them or not. Self-perception of the adequate use of PPE was evaluated as many times, sometimes or few times. The use of medicines for prophylaxis purposes included hydroxychloroquine and ivermectin. Vitamins, nutritional supplements, and hormonal contraceptives, usually taken for a long period were also included. Interviewers were blinded to the case status. At the end of each interview, blindness was broken to confirm the status of each participant as to prevent potential misclassification bias due to controls having a positive test after their report to the surveillance system.

Statistical analysis

Normality assumption was checked using Shapiro Wilk test. Then, study groups were described and compared using median (interquartile range) and relative frequencies for quantitative and qualitative variables, respectively. Body Mass Index (BMI) was estimated from self-reported weight and height and categorized as obese (≥ 30 kg/m 2 ), overweight (25 to < 30 kg/m 2 ) and not overweight nor obese (< 25 kg/m 2 ). Epidemiological weeks were calculated based on the date of the test result. To account for correlation among exposures in multiple analysis, new variables were defined. For example, the use of surgical caps, goggles/face shields, and gloves were grouped as single PPE. As HCW may have work in more than one hospital area, these were classified according to risk as “high-risk” if working in COVID-19-designated zones and any of emergency room, inpatient ward or intensive care unit (ICU), as “middle risk” if did not work in a COVID-19-designated zone but in emergency or ICU, and as “low risk” if did not work in COVID-19-designated zone nor emergency nor ICU.

Mann–Whitney U -test and Chi-Square or Fisher test were used for comparisons as appropriate. Multiple Logistic regression models were fitted using the backward strategy and the likelihood ratio test. A variable remained in the model when partial F had a P  ≤ 0.10, when confounding effect was observed, or by its clinical relevance on the outcome (i.e.; epidemiological weeks and hospital area). Model fit was evaluated by Hosmer and Lemeshow test. Calibration, specificity, and collinearity was also checked. The final model was selected considering the highest explicative ability measured by PseudoR 2 . Analyses were performed using Stata version 15 (StataCorp. LP, College Station,TX).

The flow diagram of the study population is shown in Fig.  1 . Among those contacted that met the eligibility criteria, 5% of cases and 14% of controls declined to participate, resulting in a final sample of 110 cases and 113 controls. RT-PCR was ordered because of symptoms in 59.2% of participants and the remaining 40.8% as part of contact tracing or institutional screening. At the time of the interview, all HCW reported to wear some type of facemask in both the institutional and community settings. Oral contraceptives were the most common type of hormonal contraception (82%). Differences between cases and control are shown in Tables 1 , 2 and 3 . Among females, the difference between cases and controls in the use of hormonal contraceptives was observed mainly in symptomatic women (OR = 2.05 95% CI 0.75–5.64).

Flowchart of the study participants

Modifiable and non-modifiable risk factors remained in the multivariate model as shown in Table 4 . The use of a high-performance mask or a combination of fabric and surgical mask outside the workplace showed a protective effect (AdjOR = 0.27 95% CI 0.09–0.80). Not wearing any of surgical caps, face shields/goggles or gloves (AdjOR = 0.10 95% CI 0.02–0.41) and feeling scared or nervous (AdjOR = 0.45 95% CI 0.22–0.91) were also protective. On the contrary, not always wearing high-performance mask within the workplace (AdjOR = 2.27 95% CI 1.02–5.05) and not using PPE properly (AdjOR = 4.82 95% CI 1.18–19.65) were positive associated with the infection. Male gender (AdjOR = 4.13 95% CI 1.70–10.05) and being nurse AdjOR = 11.24 95% CI 1.05–119.63) increased the risk, while college graduate AdjOR = 0.06 95% CI 0.01–0.53) and postgraduate education (AdjOR = 0.05 95% CI 0.005–0.47) reduced the risk of a positive RT-PCR.

This study identified modifiable and non-modifiable factors associated to a positive RT-PCR among HCW. Particularly, a greater protective effect of high-performance masks, or double masking outside the workplace was observed when compared to other types. Conversely, surgical caps, face shields/goggles and gloves were found to increase risk. Psychological factors that prevented being overconfident about SARS-CoV-2 transmission were protective. For non-modifiable factors, male gender increased the risk while higher level of education was protective.

Concerning face-masks, those HCW always-wearing high-performance filtering masks had a better protection when compared to those wearing them occasionally or wearing other types of facemasks. This protective effect is controversial in the literature, with results suggesting greater [ 21 ], similar [ 22 ] or even lower [ 23 ] protection compared to surgical masks. Different types of masks, manufacturer standards, and the evaluation of potential confounders may explain discordances between studies. In addition, there is not a clear recommendation for the type of mask that HCW need to wear outside the workplace [ 19 , 24 ]. In line with previous studies [ 25 , 26 ], our results suggest that fabric and surgical masks performed similarly, while wearing high-performance filtering masks or a combination of fabric plus surgical mask reduces the risk of infection compared to the use of surgical mask exclusively. Therefore, HCW could be advised to wear high-performance mask even when they are not directly taking care of COVID-19 patients, or in case of a shortage, low resource settings or high cost of high-performance masks, a combination of fabric plus surgical mask as an alternative.

Controversially, our study reported a greater risk among those who always wore face shields/goggles, gloves and surgical caps. In this regard, the evidence is limited [ 24 ] and the statistically significant protective effect disappears after covariates adjustment [ 27 ]. A false sense of safety resulting in self-contamination, sharing reusable PPE without appropriate disinfection protocols, or relaxing their use [ 28 , 29 , 30 ] could explain this result. In any case, emphasis needs to be given to the proper use of PPE during and after patient´s care, as previously stated [ 15 , 31 , 32 , 33 ].

Another modifiable psychological factor showing a protective effect was feeling scared or nervous. Despite the fact that we did not evaluate the source of stress, anxious individuals are less confident in their abilities to managing threated situations [ 34 ]. Therefore, they are more sensitive to feedback and to be hypervigilant in monitoring their surroundings and themselves which leads to strategic actions to avoid harm [ 35 ]. Whether this apparent protective effect will persist through the duration of the pandemic needs to be elucidated.

Non-​modifiable risk factors included sex, education and occupation.Our results support a greater risk of having a positive RT-PCR among men. The testosterone suppression effect on the innate immune responses [ 36 ], the differential expression of ACE2 between males and females [ 37 ], and a better compliance among women with biosafety measures [ 38 ] could explain the gender differences in COVID-19 susceptibility. Notably, we observed a differential but no significant risk among women according to the use of hormonal contraceptives, which requires further evaluation. The greater risk among less-educated adults compared to university graduated is consistent with a previous report [ 39 ]. Our study reports a greater risk among nurses when compared to nursing assistants; however, the precision of this estimation was low. Despite these factors are not modifiable, some strategies focusing on high risk groups could be implemented to reduce their risk, e.g. special training and monitoring for men and less educated groups.

To prevent misclassification bias, interviewers were masked to the participant´s case or control status. Although we did not quantify the possible effect of recall bias, phone questionnaires have been used in other pandemics [ 40 ] and are as valid as face-to-face interviews for collecting behavioural information [ 41 , 42 ]. Moreover, we expect recall bias to be non-differential given that the time between the RT-PCR results and the interview were similar between groups. Self-report of anthropometric measures has been found to be accurate in terms of weight classification [ 43 , 44 ]. The reasons for declining participation were similar between groups and were mainly related to availability (in terms of time), which made selection bias unlikely. Residual confounding could be present due to unmeasured variables such as quality of training, doffing practices, or the prevalence of the infection in the place of residence. In addition, residual confounding could be due to remaining differences in variables such as the type of hormonal contraceptives and the number of mask layers. Our results should not be extrapolated to the general population because health care workers are likely to behave differently regarding PPE use and risk of infection.

In conclusion, modifiable and non-modifiable factors were associated to SARS-CoV-2 infection among HCW, independent of the level of exposure. High-performance masks or double masking, adequate use of PPE and feeling scared or nervous were protective factors. In addition, gender, level of education along with occupational characteristics, were also associated with the risk of infection and need to be considered when planning public health and health care facilities prevention strategies.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Angiotensin Converting Enzyme

Adjusted Odds Ratio

Confidence Interval

Coronavirus Infection Disease 2019

Diabetes Mellitus

Health Care Worker

High Blood Pressure

Personal Protection Equipment

Intensive Care Unit

Reverse Transcription-Polymerase Chain Reaction

Severe Acute Respiratory Syndrome Coronavirus 2

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Acknowledgements

This work was supported by Pontificia Universidad Javeriana-Cali and Universidad del Valle. The content is solely the responsibility of the authors and does not necessarily represent the official view of Pontificia Universidad Javeriana or Universidad del Valle.

This work was supported by Pontificia Universidad Javeriana-Cali and Universidad del Valle.

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MR-L: Conception and design of the study, data collection, funding acquisition, statistical analysis, data interpretation, writing of the article and review and approval of the final version. LO: conception and design of the study, data collection, funding acquisition, statistical analysis, data interpretation, review and approval of the final version BP: Conception and designed of the study, funding acquisition, data collection and interpretation, review and approval of the final version. MS: Conception and designed of the study, data collection and interpretation, review and approval of the final version. EV & LR: Data collection, review and approval of the final version. GA: Conception and designed of the study, data collection and interpretation, review and approval of the final version. LA & JH: Data collection and interpretation, review and approval of the final version. All authors read and approved the final manuscript.

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Rodriguez-Lopez, M., Parra, B., Vergara, E. et al. A case–control study of factors associated with SARS-CoV-2 infection among healthcare workers in Colombia. BMC Infect Dis 21 , 878 (2021). https://doi.org/10.1186/s12879-021-06581-y

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Received : 21 April 2021

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Published : 27 August 2021

DOI : https://doi.org/10.1186/s12879-021-06581-y

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• Published: 11 February 2021

Methodological quality of COVID-19 clinical research

• Richard G. Jung   ORCID: orcid.org/0000-0002-8570-6736 1 , 2 , 3   na1 ,
• Pietro Di Santo 1 , 2 , 4 , 5   na1 ,
• Cole Clifford 6 ,
• Graeme Prosperi-Porta 7 ,
• Stephanie Skanes 6 ,
• Annie Hung 8 ,
• Simon Parlow 4 ,
• Sarah Visintini   ORCID: orcid.org/0000-0001-6966-1753 9 ,
• F. Daniel Ramirez   ORCID: orcid.org/0000-0002-4350-1652 1 , 4 , 10 , 11 ,
• Trevor Simard 1 , 2 , 3 , 4 , 12 &
• Benjamin Hibbert   ORCID: orcid.org/0000-0003-0906-1363 2 , 3 , 4  

Nature Communications volume  12 , Article number:  943 ( 2021 ) Cite this article

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The COVID-19 pandemic began in early 2020 with major health consequences. While a need to disseminate information to the medical community and general public was paramount, concerns have been raised regarding the scientific rigor in published reports. We performed a systematic review to evaluate the methodological quality of currently available COVID-19 studies compared to historical controls. A total of 9895 titles and abstracts were screened and 686 COVID-19 articles were included in the final analysis. Comparative analysis of COVID-19 to historical articles reveals a shorter time to acceptance (13.0[IQR, 5.0–25.0] days vs. 110.0[IQR, 71.0–156.0] days in COVID-19 and control articles, respectively; p  < 0.0001). Furthermore, methodological quality scores are lower in COVID-19 articles across all study designs. COVID-19 clinical studies have a shorter time to publication and have lower methodological quality scores than control studies in the same journal. These studies should be revisited with the emergence of stronger evidence.

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Introduction.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic spread globally in early 2020 with substantial health and economic consequences. This was associated with an exponential increase in scientific publications related to the coronavirus disease 2019 (COVID-19) in order to rapidly elucidate the natural history and identify diagnostic and therapeutic tools 1 .

While a need to rapidly disseminate information to the medical community, governmental agencies, and general public was paramount—major concerns have been raised regarding the scientific rigor in the literature 2 . Poorly conducted studies may originate from failure at any of the four consecutive research stages: (1) choice of research question relevant to patient care, (2) quality of research design 3 , (3) adequacy of publication, and (4) quality of research reports. Furthermore, evidence-based medicine relies on a hierarchy of evidence, ranging from the highest level of randomized controlled trials (RCT) to the lowest level of case series and case reports 4 .

Given the implications for clinical care, policy decision making, and concerns regarding methodological and peer-review standards for COVID-19 research 5 , we performed a formal evaluation of the methodological quality of published COVID-19 literature. Specifically, we undertook a systematic review to identify COVID-19 clinical literature and matched them to historical controls to formally evaluate the following: (1) the methodological quality of COVID-19 studies using established quality tools and checklists, (2) the methodological quality of COVID-19 studies, stratified by median time to acceptance, geographical regions, and journal impact factor and (3) a comparison of COVID-19 methodological quality to matched controls.

Herein, we show that COVID-19 articles are associated with lower methodological quality scores. Moreover, in a matched cohort analysis with control articles from the same journal, we reveal that COVID-19 articles are associated with lower quality scores and shorter time from submission to acceptance. Ultimately, COVID-19 clinical studies should be revisited with the emergence of stronger evidence.

Article selection

A total of 14787 COVID-19 papers were identified as of May 14, 2020 and 4892 duplicate articles were removed. In total, 9895 titles and abstracts were screened, and 9101 articles were excluded due to the study being pre-clinical in nature, case report, case series <5 patients, in a language other than English, reviews (including systematic reviews), study protocols or methods, and other coronavirus variants with an overall inter-rater study inclusion agreement of 96.7% ( κ  = 0.81; 95% CI, 0.79–0.83). A total number of 794 full texts were reviewed for eligibility. Over 108 articles were excluded for ineligible study design or publication type (such as letter to the editors, editorials, case reports or case series <5 patients), wrong patient population, non-English language, duplicate articles, wrong outcomes and publication in a non-peer-reviewed journal. Ultimately, 686 articles were identified with an inter-rater agreement of 86.5% ( κ  = 0.68; 95% CI, 0.67–0.70) (Fig.  1 ).

A total of 14787 articles were identified and 4892 duplicate articles were removed. Overall, 9895 articles were screened by title and abstract leaving 794 articles for full-text screening. Over 108 articles were excluded, leaving a total of 686 articles that underwent methodological quality assessment.

COVID-19 literature methodological quality

Most studies originated from Asia/Oceania with 469 (68.4%) studies followed by Europe with 139 (20.3%) studies, and the Americas with 78 (11.4%) studies. Of included studies, 380 (55.4%) were case series, 199 (29.0%) were cohort, 63 (9.2%) were diagnostic, 38 (5.5%) were case–control, and 6 (0.9%) were RCTs. Most studies (590, 86.0%) were retrospective in nature, 620 (90.4%) reported the sex of patients, and 7 (2.3%) studies excluding case series calculated their sample size a priori. The method of SARS-CoV-2 diagnosis was reported in 558 studies (81.3%) and ethics approval was obtained in 556 studies (81.0%). Finally, journal impact factor of COVID-19 manuscripts was 4.7 (IQR, 2.9–7.6) with a time to acceptance of 13.0 (IQR, 5.0–25.0) days (Table  1 ).

Overall, when COVID-19 articles were stratified by study design, a mean case series score (out of 5) (SD) of 3.3 (1.1), mean NOS cohort study score (out of 8) of 5.8 (1.5), mean NOS case–control study score (out of 8) of 5.5 (1.9), and low bias present in 4 (6.4%) diagnostic studies was observed (Table  2 and Fig.  2 ). Furthermore, in the 6 RCTs in the COVID-19 literature, there was a high risk of bias with little consideration for sequence generation, allocation concealment, blinding, incomplete outcome data, and selective outcome reporting (Table  2 ).

A Distribution of COVID-19 case series studies scored using the Murad tool ( n  = 380). B Distribution of COVID-19 cohort studies scored using the Newcastle–Ottawa Scale ( n  = 199). C Distribution of COVID-19 case–control studies scored using the Newcastle–Ottawa Scale ( n  = 38). D Distribution of COVID-19 diagnostic studies scored using the QUADAS-2 tool ( n  = 63). In panel D , blue represents low risk of bias and orange represents high risk of bias.

For secondary outcomes, rapid time from submission to acceptance (stratified by median time of acceptance of <13.0 days) was associated with lower methodological quality scores for case series and cohort study designs but not for case–control nor diagnostic studies (Fig.  3A–D ). Low journal impact factor (<10) was associated with lower methodological quality scores for case series, cohort, and case–control designs (Fig.  3E–H ). Finally, studies originating from different geographical regions had no differences in methodological quality scores with the exception of cohort studies (Fig.  3I–L ). When dichotomized by high vs. low methodological quality scores, a similar trend was observed with rapid time from submission to acceptance (34.4% vs. 46.3%, p  = 0.01, Supplementary Fig.  1B ), low impact factor journals (<10) was associated with lower methodological quality score (38.8% vs. 68.0%, p  < 0.0001, Supplementary Fig.  1C ). Finally, studies originating in either Americas or Asia/Oceania was associated with higher methodological quality scores than Europe (Supplementary Fig.  1D ).

A When stratified by time of acceptance (13.0 days), increased time of acceptance was associated with higher case series score ( n  = 186 for <13 days and n  = 193 for >=13 days; p  = 0.02). B Increased time of acceptance was associated with higher NOS cohort score ( n  = 112 for <13 days and n  = 144 for >=13 days; p  = 0.003). C No difference in time of acceptance and case–control score was observed ( n  = 18 for <13 days and n  = 27 for >=13 days; p  = 0.34). D No difference in time of acceptance and diagnostic risk of bias (QUADAS-2) was observed ( n  = 43 for <13 days and n  = 33 for >=13 days; p  = 0.23). E When stratified by impact factor (IF ≥10), high IF was associated with higher case series score ( n  = 466 for low IF and n  = 60 for high IF; p  < 0.0001). F High IF was associated with higher NOS cohort score ( n  = 262 for low IF and n  = 68 for high IF; p  = 0.01). G No difference in IF and case–control score was observed ( n  = 62 for low IF and n  = 2 for high IF; p  = 0.052). H No difference in IF and QUADAS-2 was observed ( n  = 101 for low IF and n  = 2 for high IF; p  = 0.93). I When stratified by geographical region, no difference in geographical region and case series score was observed ( n  = 276 Asia/Oceania, n  = 135 Americas, and n  = 143 Europe/Africa; p  = 0.10). J Geographical region was associated with differences in cohort score ( n  = 177 Asia/Oceania, n  = 81 Americas, and n  = 89 Europe/Africa; p  = 0.01). K No difference in geographical region and case–control score was observed ( n  = 37 Asia/Oceania, n  = 13 Americas, and n  = 14 Europe/Africa; p  = 0.81). L No difference in geographical region and QUADAS-2 was observed ( n  = 49 Asia/Oceania, n  = 28 Americas, and n  = 28 Europe/Africa; p  = 0.34). In panels A – D , orange represents lower median time of acceptance and blue represents high median time of acceptance. In panels E – H , red is low impact factor and blue is high impact factor. In panels I – L , orange represents Asia/Oceania, blue represents Americas, and brown represents Europe. Differences in distributions were analysed by two-sided Kruskal–Wallis test. Differences in diagnostic risk of bias were quantified by Chi-squares test. p  < 0.05 was considered statistically significant.

Methodological quality score differences in COVID-19 versus historical control

We matched 539 historical control articles to COVID-19 articles from the same journal with identical study designs in the previous year for a final analysis of 1078 articles (Table  1 ). Overall, 554 (51.4%) case series, 348 (32.3%) cohort, 64 (5.9%) case–control, 106 (9.8%) diagnostic and 6 (0.6%) RCTs were identified from the 1078 total articles. Differences exist between COVID-19 and historical control articles in geographical region of publication, retrospective study design, and sample size calculation (Table  1 ). Time of acceptance was 13.0 (IQR, 5.0–25.0) days in COVID-19 articles vs. 110.0 (IQR, 71.0–156.0) days in control articles (Table  1 and Fig.  4A , p  < 0.0001). Case-series methodological quality score was lower in COVID-19 articles compared to the historical control (3.3 (1.1) vs. 4.3 (0.8); n  = 554; p  < 0.0001; Table  2 and Fig.  4B ). Furthermore, NOS score was lower in COVID-19 cohort studies (5.8 (1.6) vs. 7.1 (1.0); n  = 348; p  < 0.0001; Table  2 and Fig.  4C ) and case–control studies (5.4 (1.9) vs. 6.6 (1.0); n  = 64; p  = 0.003; Table  2 and Fig.  4D ). Finally, lower risk of bias in diagnostic studies was in 12 COVID-19 articles (23%; n  = 53) compared to 24 control articles (45%; n  = 53; p  = 0.02; Table  2 and Fig.  4E ). A similar trend was observed between COVID-19 and historical control articles when dichotomized by good vs. low methodological quality scores (Supplementary Fig.  2 ).

A Time to acceptance was reduced in COVID-19 articles compared to control articles (13.0 [IQR, 5.0–25.0] days vs. 110.0 [IQR, 71.0–156.0] days, n  = 347 for COVID-19 and n  = 414 for controls; p  < 0.0001). B When compared to historical control articles, COVID-19 articles were associated with lower case series score ( n  = 277 for COVID-19 and n  = 277 for controls; p  < 0.0001). C COVID-19 articles were associated with lower NOS cohort score compared to historical control articles ( n  = 174 for COVID-19 and n  = 174 for controls; p  < 0.0001). D COVID-19 articles were associated with lower NOS case–control score compared to historical control articles ( n  = 32 for COVID-19 and n  = 32 for controls; p  = 0.003). E COVID-19 articles were associated with higher diagnostic risk of bias (QUADAS-2) compared to historical control articles ( n  = 53 for COVID-19 and n  = 53 for controls; p  = 0.02). For panel A , boxplot captures 5, 25, 50, 75 and 95% from the first to last whisker. Orange represents COVID-19 articles and blue represents control articles. Two-sided Mann–Whitney U-test was conducted to evaluate differences in time to acceptance between COVID-19 and control articles. Differences in study quality scores were evaluated by two-sided Kruskal–Wallis test. Differences in diagnostic risk of bias were quantified by Chi-squares test. p  < 0.05 was considered statistically significant.

In this systematic evaluation of methodological quality, COVID-19 clinical research was primarily observational in nature with modest methodological quality scores. Not only were the study designs low in the hierarchy of scientific evidence, we found that COVID-19 articles were associated with a lower methodological quality scores when published with a shorter time of publication and in lower impact factor journals. Furthermore, in a matched cohort analysis with historical control articles identified from the same journal of the same study design, we demonstrated that COVID-19 articles were associated with lower quality scores and shorter time from submission to acceptance.

The present study demonstrates comparative differences in methodological quality scores between COVID-19 literature and historical control articles. Overall, the accelerated publication of COVID-19 research was associated with lower study quality scores compared to previously published historical control studies. Our research highlights major differences in study quality between COVID-19 and control articles, possibly driven in part by a combination of more thorough editorial and/or peer-review process as suggested by the time to publication, and robust study design with questions which are pertinent for clinicians and patient management 3 , 6 , 7 , 8 , 9 , 10 , 11 .

In the early stages of the COVID-19 pandemic, we speculate that an urgent need for scientific data to inform clinical, social and economic decisions led to shorter time to publication and explosion in publication of COVID-19 studies in both traditional peer-reviewed journals and preprint servers 1 , 12 . The accelerated scientific process in the COVID-19 pandemic allowed a rapid understanding of natural history of COVID-19 symptomology and prognosis, identification of tools including RT-PCR to diagnose SARS-CoV-2 13 , and identification of potential therapeutic options such as tocilizumab and convalescent plasma which laid the foundation for future RCTs 14 , 15 , 16 . A delay in publication of COVID-19 articles due to a slower peer-review process may potentially delay dissemination of pertinent information against the pandemic. Despite concerns of slow peer review, major landmark trials (i.e. RECOVERY and ACTT-1 trial) 17 , 18 published their findings in preprint servers and media releases to allow for rapid dissemination. Importantly, the data obtained in these initial studies should be revisited as stronger data emerges as lower quality studies may fundamentally risk patient safety, resource allocation and future scientific research 19 .

Unfortunately, poor evidence begets poor clinical decisions 20 . Furthermore, lower quality scientific evidence potentially undermines the public’s trust in science during this time and has been evident through misleading information and high-profile retractions 12 , 21 , 22 , 23 . For example, the benefits of hydroxychloroquine, which were touted early in the pandemic based on limited data, have subsequently failed to be replicated in multiple observational studies and RCTs 5 , 24 , 25 , 26 , 27 , 28 , 29 , 30 . One poorly designed study combined with rapid publication led to considerable investment of both the scientific and medical community—akin to quinine being sold to the public as a miracle drug during the 1918 Spanish Influenza 31 , 32 . Moreover, as of June 30, 2020, ClinicalTrials.gov listed an astonishing 230 COVID-19 trials with hydroxychloroquine/plaquenil, and a recent living systematic review of observational studies and RCTs of hydroxychloroquine or chloroquine for COVID-19 demonstrated no evidence of benefit nor harm with concerns of severe methodological flaws in the included studies 33 .

Our study has important limitations. We evaluated the methodological quality of existing studies using established checklists and tools. While it is tempting to associate methodological quality scores with reproducibility or causal inferences of the intervention, it is not possible to ascertain the impact on the study design and conduct of research nor results or conclusions in the identified reports 34 . Second, although the methodological quality scales and checklists used for the manuscript are commonly used for quality assessment in systematic reviews and meta-analyses 35 , 36 , 37 , 38 , they can only assess the methodology without consideration for causal language and are prone to limitations 39 , 40 . Other tools such as the ROBINS-I and GRADE exist to evaluate methodological quality of identified manuscripts, although no consensus currently exists for critical appraisal of non-randomized studies 41 , 42 , 43 . Furthermore, other considerations of quality such as sample size calculation, sex reporting or ethics approval are not considered in these quality scores. As such, the quality scores measured using these checklists only reflect the patient selection, comparability, diagnostic reference standard and methods to ascertain the outcome of the study. Third, the 1:1 ratio to identify our historical control articles may affect the precision estimates of our findings. Interestingly, a simulation of an increase from 1:1 to 1:4 control ratio tightened the precision estimates but did not significantly alter the point estimate 44 . Furthermore, the decision for 1:1 ratio in our study exists due to limitations of available historical control articles from the identical journal in the restricted time period combined with a large effect size and sample size in the analysis. Finally, our analysis includes early publications on COVID-19 and there is likely to be an improvement in quality of related studies and study design as the field matures and higher-quality studies. Accordingly, our findings are limited to the early body of research as it pertains to the pandemic and it is likely that over time research quality will improve over time.

In summary, the early body of peer-reviewed COVID-19 literature was composed primarily of observational studies that underwent shorter peer-review evaluation and were associated with lower methodological quality scores than comparable studies. COVID-19 clinical studies should be revisited with the emergence of stronger evidence.

A systematic literature search was conducted on May 14, 2020 (registered on June 3, 2020 at PROSPERO: CRD42020187318) and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Furthermore, the cohort study was reported according to the Strengthening The Reporting of Observational Studies in Epidemiology checklist. The data supporting the findings of this study is available as Supplementary Data  1 – 2 .

Data sources and searches

The search was created in MEDLINE by a medical librarian with expertise in systematic reviews (S.V.) using a combination of key terms and index headings related to COVID-19 and translated to the remaining bibliographic databases (Supplementary Tables  1 – 3 ). The searches were conducted in MEDLINE (Ovid MEDLINE(R) ALL 1946–), Embase (Ovid Embase Classic + Embase 1947–) and the Cochrane Central Register of Controlled Trials (from inception). Search results were limited to English-only publications, and a publication date limit of January 1, 2019 to present was applied. In addition, a Canadian Agency for Drugs and Technologies in Health search filter was applied in MEDLINE and Embase to remove animal studies, and commentary, newspaper article, editorial, letter and note publication types were also eliminated. Search results were exported to Covidence (Veritas Health Innovation, Melbourne, Australia) and duplicates were eliminated using the platform’s duplicate identification feature.

Study selection, data extraction and methodological quality assessment

We included all types of COVID-19 clinical studies, including case series, observational studies, diagnostic studies and RCTs. For diagnostic studies, the reference standard for COVID-19 diagnosis was defined as a nasopharyngeal swab followed by reverse transcriptase-polymerase chain reaction in order to detect SARS-CoV-2. We excluded studies that were exploratory or pre-clinical in nature (i.e. in vitro or animal studies), case reports or case series of <5 patients, studies published in a language other than English, reviews, methods or protocols, and other coronavirus variants such as the Middle East respiratory syndrome.

The review team consisted of trained research staff with expertise in systematic reviews and one trainee. Title and abstracts were evaluated by two independent reviewers using Covidence and all discrepancies were resolved by consensus. Articles that were selected for full review were independently evaluated by two reviewers for quality assessment using a standardized case report form following the completion of a training period where all reviewers were trained with the original manuscripts which derived the tools or checklists along with examples for what were deemed high scores 35 , 36 , 37 , 38 . Following this, reviewers completed thirty full-text extractions and the two reviewers had to reach consensus and the process was repeated for the remaining manuscripts independently. When two independent reviewers were not able reach consensus, a third reviewer (principal investigator) provided oversight in the process to resolve the conflicted scores.

First and corresponding author names, date of publication, title of manuscript and journal of publication were collected for all included full-text articles. Journal impact factor was obtained from the 2018 InCites Journal Citation Reports from Clarivate Analytics. Submission and acceptance dates were collected in manuscripts when available. Other information such as study type, prospective or retrospective study, sex reporting, sample size calculation, method of SARS-CoV-2 diagnosis and ethics approval was collected by the authors. Methodological quality assessment was conducted using the Newcastle–Ottawa Scale (NOS) for case–control and cohort studies 37 , QUADAS-2 tool for diagnostic studies 38 , Cochrane risk of bias for RCTs 35 and a score derived by Murad et al. for case series studies 36 .

Identification of historical control from identified COVID-19 articles

Following the completion of full-text extraction of COVID-19 articles, we obtained a historical control group by identifying reports matched in a 1:1 fashion. From the eligible COVID-19 article, historical controls were identified by searching the same journal in a systematic fashion by matching the same study design (“case series”, “cohort”, “case control” or “diagnostic”) starting in the journal edition 12 months prior to the COVID-19 article publication on the publisher website (i.e. COVID-19 article published on April 2020, going backwards to April 2019) and proceeding forward (or backward if a specific article type was not identified) in a temporal fashion until the first matched study was identified following abstract screening by two independent reviewers. If no comparison article was found by either reviewers, the corresponding COVID-19 article was excluded from the comparison analysis. Following the identification of the historical control, data extraction and quality assessment was conducted on the identified articles using the standardized case report forms by two independent reviewers and conflicts resolved by consensus. The full dataset has been made available as Supplementary Data  1 – 2 .

Data synthesis and statistical analysis

Continuous variables were reported as mean (SD) or median (IQR) as appropriate, and categorical variables were reported as proportions (%). Continuous variables were compared using Student t -test or Mann–Whitney U-test and categorical variables including quality scores were compared by χ 2 , Fisher’s exact test, or Kruskal–Wallis test.

The primary outcome of interest was to evaluate the methodological quality of COVID-19 clinical literature by study design using the Newcastle–Ottawa Scale (NOS) for case–control and cohort studies, QUADAS-2 tool for diagnostic studies 38 , Cochrane risk of bias for RCTs 35 , and a score derived by Murad et al. for case series studies 36 . Pre-specified secondary outcomes were comparison of methodological quality scores of COVID-19 articles by (i) median time to acceptance, (ii) impact factor, (iii) geographical region and (iv) historical comparator. Time of acceptance was defined as the time between submission to acceptance which captures peer review and editorial decisions. Geographical region was stratified into continents including Asia/Oceania, Europe/Africa and Americas (North and South America). Post hoc comparison analysis between COVID-19 and historical control article quality scores were evaluated using Kruskal–Wallis test. Furthermore, good quality of NOS was defined as 3+ on selection and 1+ on comparability, and 2+ on outcome/exposure domains and high-quality case series scores was defined as a score ≥3.5. Due to a small sample size of identified RCTs, they were not included in the comparison analysis.

The finalized dataset was collected on Microsoft Excel v16.44. All statistical analyses were performed using SAS v9.4 (SAS Institute, Inc., Cary, NC, USA). Statistical significance was defined as P  < 0.05. All figures were generated using GraphPad Prism v8 (GraphPad Software, La Jolla, CA, USA).

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The authors can confirm that all relevant data are included in the paper and in Supplementary Data  1 – 2 . The original search was conducted on MEDLINE, Embase and Cochrane Central Register of Controlled Trials.

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Acknowledgements

This study received no specific funding or grant from any agency in the public, commercial, or not-for-profit sectors. R.G.J. was supported by the Vanier CIHR Canada Graduate Scholarship. F.D.R. was supported by a CIHR Banting Postdoctoral Fellowship and a Royal College of Physicians and Surgeons of Canada Detweiler Travelling Fellowship. The funder/sponsor(s) had no role in design and conduct of the study, collection, analysis and interpretation of the data.

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These authors contributed equally: Richard G. Jung, Pietro Di Santo.

Authors and Affiliations

CAPITAL Research Group, University of Ottawa Heart Institute, Ottawa, Ontario, Canada

Richard G. Jung, Pietro Di Santo, F. Daniel Ramirez & Trevor Simard

Vascular Biology and Experimental Medicine Laboratory, University of Ottawa Heart Institute, Ottawa, Ontario, Canada

Richard G. Jung, Pietro Di Santo, Trevor Simard & Benjamin Hibbert

Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada

Richard G. Jung, Trevor Simard & Benjamin Hibbert

Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada

Pietro Di Santo, Simon Parlow, F. Daniel Ramirez, Trevor Simard & Benjamin Hibbert

School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada

Pietro Di Santo

Faculty of Medicine, University of Ottawa, Ontario, Canada

Cole Clifford & Stephanie Skanes

Department of Medicine, Cumming School of Medicine, Calgary, Alberta, Canada

Graeme Prosperi-Porta

Division of Internal Medicine, The Ottawa Hospital, Ottawa, Ontario, Canada

Berkman Library, University of Ottawa Heart Institute, Ottawa, Ontario, Canada

Sarah Visintini

Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, Bordeaux-Pessac, France

F. Daniel Ramirez

L’Institut de Rythmologie et Modélisation Cardiaque (LIRYC), University of Bordeaux, Bordeaux, France

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA

Trevor Simard

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R.G.J., P.D.S., S.V., F.D.R., T.S. and B.H. participated in the study conception and design. Data acquisition, analysis and interpretation were performed by R.G.J., P.D.S., C.C., G.P.P., S.P., S.S., A.H., F.D.R., T.S. and B.H. Statistical analysis was performed by R.G.J., P.D.S. and B.H. The manuscript was drafted by R.G.J., P.D.S., F.D.R., T.S. and B.H. All authors approved the final version of the manuscript and agree to be accountable to all aspects of the work.

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Jung, R.G., Di Santo, P., Clifford, C. et al. Methodological quality of COVID-19 clinical research. Nat Commun 12 , 943 (2021). https://doi.org/10.1038/s41467-021-21220-5

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Evaluation and prediction of COVID-19 in India: A case study of worst hit states

In this manuscript, system modeling and identification techniques are applied in developing a prognostic yet deterministic model to forecast the spread of COVID-19 in India. The model is verified with the historical data and a forecast of the spread for 30-days is presented in the 10 most affected states of India. The major results suggest that our model can very well capture the disease variations with high accuracy. The results also show a steep rise in the total cumulative cases and deaths in the coming weeks.

1. Introduction

The advent and spread of 2019 novel coronavirus (SARS-CoV-2) has posed a global health crisis with a sharp rise in cases and deaths since its first detection in Wuhan, China, in December 2019. The infection causes illness ranging from common cold to extreme respiratory disease and death [1] . Currently, the prime epidemiological risk factor for 2019 novel coronavirus disease includes close contact with infected individuals with an incubation period of 2–14 days [2] . The case mortality rate is projected to range from 2 to 3% [3] . Various drugs are being assessed in line with previous researches into therapeutic treatments for SARS and MERS, however, there is no robust evidence for any significantly improved clinical outcome [4] . Apparent risk of acquiring the disease has led many governments to institute a variety of control procedures like quarantine, isolation and lock-down measures. Despite rigorous global containment measures, the frequency of the novel coronavirus disease continues to rise, with over 4.5 million confirmed cases and over 300,000 deaths worldwide as on 17 th May, 2020 [5] . Although countries around the world have enhanced capacity building of the laboratory systems and response procedures, yet, there is a need for proper disease surveillance systems. Comprehending the initial transmission of the virus and analyzing the effectiveness of control measures are crucial in assessing the prospects for continued transmission in newer locations. This necessitates tracking the course of the pandemic to be able to foresee its emergence for a better response.

Prospective studies on modeling and forecasting of the epidemic have been carried out to provide analytical predictions on the size and end phase of the spread. Wu et al. [6] have used a susceptible exposed infectious recovered (SEIR) meta-population model to simulate the epidemic across all major cities in China. Early dynamics of transmission and control of COVID-19 within and outside Wuhan has also been studied using a stochastic transmission dynamic model [7] . Another study used the SEIR compartmental model to predict the feasibility for conducting the summer Olympics of 2020 in Japan [8] . Similarly, Abdullah et al. [9] presented a stochastic SIR model to predict the spread of COVID-19 in Kuwait. A classical SEIR type mathematical model is also presented by Mandal et al. [10] to study the qualitative dynamics of COVID-19 in India. Further work has been carried out by Ndairou et al. [11] , with special focus on the transmissibility of super-spreader individuals in Wuhan, China.

Besides the above mentioned compartmental models, some other methods have been used to model and forecast the COVID-19 spread. For example, in Tomar and Gupta [12] , a data-driven estimation method like long short-term memory (LSTM) is used for the prediction of total number of COVID-19 cases in India for a 30-days ahead prediction window. In addition to this, global epidemic and mobility model (GLEAM), an agent-based mechanistic model has also been used for daily forcasts of COVID-19 activity [13] . Harun, et al. [14] have used Box-Jenkins (ARIMA) and Brown/Holt linear exponential smoothing methods to estimate and forecast the number of COVID-19 cases in the G8 countries. Furthermore, Al-qaness et al. [15] have incorporated a modified version of flower pollination algorithm (FPA) coupled with the salp swarm algorithm (SSA) to forecast the number of cases of COVID-19 for ten days in China.

As on 17 th May 2020, India has observed a total cases of 90,927 with 2872 deaths [16] , [17] . The very first case was reported on 30 th January 2020, in a coastal state of Kerela (southern India) when a student returned from Wuhan, China. Subsequently, the number of positive cases in India rose rapidly due to the arrival of many passengers via airways [18] . An overview of the spread of COVID-19 in India is shown in Fig. 1 . It can be easily seen that the virus has spread to entire country with the worst hit states being Maharashtra (30,706 cases), Gujarat (10,988), Tamil Nadu (10,588), Delhi (9333), Rajasthan (4960), and Madhya Pradesh (4789). Figs. 2 and ​ and3 show 3 show the trend of rising new cases and deaths in India.

Heat map of COVID-19 in Indian (as of 17 May 2020).

(Top:) cumulative cases in India till 17 May 2020, (bottom:) daily new cases till 17 May 2020.

(Top:) cumulative deaths in India till 17 May 2020, (bottom:) daily new deaths till 17 May 2020.

This manuscript demonstrates a control-theoretic, data-driven estimation technique to derive a time-series model from the historical data collected from [5] , [16] up-to 17 th May 2020. The model is then used for the prediction of the total number of cases and deaths in most affected states of India for the next 30 days. The paper is sectioned as follows: Section 2 describes the system identification method employed. Section 3 presents the predicted cases and deaths along-with some discussions. Finally, conclusions are presented in Section 4 .

2. Data driven forecasting of COVID-19 in India

To estimate the spread of COVID-19 in India, we used a predictive error minimization (PEM) based system identification technique to identify a discrete-time, single-input, single-output (SISO) model [19] , [20] , [21] . Different models were identified for different states based on the data collected. The models were then verified on the testing data and upon validation, the models were used to predict the total number of cases and deaths for the next 30-days in the 10 worst hit states in India.

2.1. Model development

The discrete-time, identified model can be realized in the state-space from given as:

where the y ( t ) represents total number of cases or deaths of a particular area which is proportional to system state vector x ( t ) ∈ R n , u ( t ) is the time series input and T s is the sampling interval. Here, the unknowns to be identified are A ∈ R n × n , K ∈ R n × 1 and C ∈ R 1 × n which are in canonical form. Also, n is the dimension of the state-space model.

The identification problem can thus be posed as to selecting a model set M ( θ ) (indexed by a finite dimensional parameter vector θ) and evaluating a member from the set which best describes the recorded input-output relation according to a given criterion. One such criteria is given by Ljung [22] which is defined as :

where ϵ ( t , θ ) = ( y 0 − y ^ 0 , … , y N − y ^ N ) is referred as the prediction error, l ( . ) is a scalar measure of fit, z ( t ) = [ y T ( t ) , u T ( t ) ] and N is length of data-set. Typical choices of l ( t , θ , ϵ ) can be seen in Ljung [22] .

The identified model thus minimizes the 1-step ahead prediction and the error ϵ ( t , θ ) between the measured y ( t ) and predicted values y ^ ( t ) is used to make the future prediction about the system. The prediction error identification estimate is thus given as:

Here, we have taken:

and the least-square problem has been solved iteratively via the Levenberg-Marquardt method [23] , [24] , [25] .

The choice of model structure and its size is of crucial importance as it dictates the quality of long-term prediction and parameter estimation. The selection of model size n was made on the basis of the decay of the Hankel singular values of the system (1) [26] , [27] .

3. Results and discussions

Fig. 4 , Fig. 5 , Fig. 6 , Fig. 7 , Fig. 8 , Fig. 9 , Fig. 10 , Fig. 11 , Fig. 12 , Fig. 13 show the dynamics of the forecasted response for the most infected states of India along-with a 10-step predicted response comparison with the validation data. Further results are presented in Table 1 . As seen from Table 1 , Maharashtra has recorded the highest number of COVID-19 cases accounting for 36% of the total country’s caseload. It has also witnessed the sharpest rise in COVID-19 deaths with Mumbai being the epicenter of the pandemic in India. The constant influx of tourists, reliance on public transportation and population destiny have cumulatively made the metropolitan city hospitable for corona virus. Even though the state is conducting more tests, the violation of physical distancing rules by individuals particularly in containment zones result in the mixing of infected with healthy population. Moreover, unlike other red zones of Maharashtra, Mumbai faces shortage of ICU beds and dedicated COVID-19 hospitals. According to the prediction made herein, it would be inevitable that Mumbai and its suburbs would continue to see an upsurge in the number of cases and deaths for at least up to 17 th June 2020.

(Top): 30-day prediction for number of cases in Maharashtra, (bottom): 30-day prediction for the number of deaths in Maharashtra. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Top): 30-day prediction for number of cases in Gujarat, (bottom): 30-day prediction for the number of deaths in Gujarat. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Top): 30-day prediction for number of cases in Tamil Nadu, (bottom): 30-day prediction for the number of deaths in Tamil Nadu. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Top): 30-day prediction for number of cases in Delhi, (bottom): 30-day prediction for the number of deaths in Delhi. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Top): 30-day prediction for number of cases in Rajasthan, (bottom): 30-day prediction for the number of deaths in Rajasthan. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Top): 30-day prediction for number of cases in Madhya Pradesh, (bottom): 30-day prediction for the number of deaths in Madhya Pradesh. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Top): 30-day prediction for number of cases in Uttar Pradesh, (bottom): 30-day prediction for the number of deaths in Uttar Pradesh. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Top): 30-day prediction for number of cases in Andhra Pradesh, (bottom): 30-day prediction for the number of deaths in Andhra Pradesh. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Top): 30-day prediction for number of cases in Punjab, (bottom): 30-day prediction for the number of deaths in Punjab. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Top): 30-day prediction for number of cases in Telangana, (bottom): 30-day prediction for the number of deaths in Telangana. Red line shows the start of prediction window, dark blue:  ± 3 std. deviation, light blue:  ± 5 std. deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

COVID-19 scenario in worst hit states of India upto 17 May 2020 along-with predicted values.

Gujarat has recorded the second highest COVID-19 mortality rate in the country in spite of reporting its first case as late as March 20. The COVID-19 mortality rate of Ahmedabad city is 6.8%, which is double the national average. Officials acknowledge that while Gujarat had its guard up sufficiently fast, there was a delay in testing. Even by mid of March, the daily average was as less as 15 tests per day, going up to 200/day by the end of March. According to the data driven identification scheme employed herein, the mortality rate in Gujarat may increase as high as 15.2% up to 17 th June 2020.

Tamil Nadu, although being the third worst hit Indian state in terms of COVID-19 cases has witnessed the least number of mortalities with 1 among 143 positive cases succumbing to the disease (see Fig. 6 ). This is attributed to its credibility as a trusted medical center of the country. Chennai has the highest medical tourism in India with the state’s average being above the national average in the health sector. This may be the reason that the predictable mortality rate of Tamil Nadu projected in this study is least among the rest of the states in consideration (see Table 1 ).

As per our prediction based on data up to 17th May 2020, Delhi along with other states would continue to see marginal surge in the number of COVID-19 cases owing to the relaxations in lock-down measures. The impact of removing the curbs will be more evident by the mid of June 2020. The under-funding of the healthcare system, paucity of testing labs, violations of the lock-down protocols and inadequate quarantine facilities arranged by states and union territories are the biggest hurdles in combating the spread.

4. Conclusions

The study concerns the spread of COVID-19 in India. A control-theoretic approach is used to develop an epidemic model to simulate and predict the disease variations in 10 most affected states of India. Results depict a rapid increase in the number of cases in the coming days. However, it is pertinent to mention that the future estimation provided, is subject to certain system parameters and can vary based on the external inputs like lock-down measures, social-distancing, vaccine/drug development, rapid testing, etc. Information provided by our model could help establish a realistic assessment of the situation for the time-being and in the near future in order to apply the appropriate public health measures.

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.

Acknowledgments

The Doctoral fellowship of Author 1 and 2 from Ministry of Human Resource Development (MHRD/2017PHAELE006/009), New Delhi, India, is duly acknowledged. Author 1 would like to thank Asiya Batool for fruitful discussions.

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Mortality in Patients Hospitalized for COVID-19 vs Influenza in Fall-Winter 2023-2024

• 1 Clinical Epidemiology Center, VA St Louis Health Care System, St Louis, Missouri

In the first year of the COVID-19 pandemic, risk of death in people hospitalized for COVID-19 was substantially higher than in people hospitalized for seasonal influenza. 1 , 2 The risk of death due to COVID-19 has since declined. In fall-winter 2022-2023, people hospitalized for COVID-19 had a 60% higher risk of death compared with those hospitalized for seasonal influenza. 3 New variants of SARS-CoV-2 have continued to appear, including the emergence of JN.1, the predominant variant in the US since December 24, 2023. 4 This study evaluated the risk of death in a cohort of people hospitalized for COVID-19 or seasonal influenza in fall-winter 2023-2024.

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Xie Y , Choi T , Al-Aly Z. Mortality in Patients Hospitalized for COVID-19 vs Influenza in Fall-Winter 2023-2024. JAMA. Published online May 15, 2024. doi:10.1001/jama.2024.7395

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Objective: To assess risk factors for persistence vs improvement and to describe clinical characteristics and diagnostic evaluation of subjects with post-acute sequelae of COVID-19/post-COVID-19 syndrome (PCS) persisting for more than one year. Design: Nested population-based case-control study. Setting: Comprehensive outpatient assessment, including neurocognitive, cardiopulmonary exercise, and laboratory testing in four university health centres in southwestern Germany (2022). Participants: PCS cases aged 18 to 65 years with (n=982) and age and sex-matched controls without PCS (n=576) according to an earlier population-based questionnaire study (six to 12 months after acute infection, phase 1) consenting to provide follow-up information and to undergo clinical diagnostic assessment (phase 2, another 8.5 months [median] after phase 1). Main outcome measures: Relative frequencies of symptoms and health problems and distribution of symptom scores and diagnostic test results between persistent cases and controls. Additional analysis included predictors of changing case or control status over time with adjustments for potentially confounding variables. Results: At the time of clinical examination (phase 2), 67.6% of the initial cases (phase 1) remained cases, whereas 78.5% of the controls continued to report no health problems related to PCS. In adjusted analyses, predictors of improvement among cases were mild acute index infection, previous full-time employment, educational status, and no specialist consultation and not attending a rehabilitation programme. Among controls, predictors of new symptoms or worsening with PCS development were an intercurrent secondary SARS-CoV-2 infection and educational status. At phase 2, persistent cases were less frequently never smokers, had higher values for BMI and body fat, and had lower educational status than controls. Fatigue/exhaustion, neurocognitive disturbance, chest symptoms/breathlessness and anxiety/depression/sleep problems remained the predominant symptom clusters, and exercise intolerance with post-exertional malaise for >14 h (PEM) and symptoms compatible with ME/CFS (according to Canadian consensus criteria) were reported by 35.6% and 11.6% of persistent cases, respectively. In adjusted analyses, significant differences between persistent cases and stable controls (at phase 2) were observed for neurocognitive test performances, scores for perceived stress and subjective cognitive disturbances, symptoms indicating dysautonomia, depression and anxiety, sleep quality, fatigue, and quality of life. In persistent cases, handgrip strength, maximal oxygen consumption, and ventilator efficiency were significantly reduced. However, there were no differences in measures of systolic and diastolic cardiac function, in the level of pro-BNP blood levels or other laboratory measurements (including complement activity, serological markers of EBV reactivation, inflammatory and coagulation markers, cortisol, ACTH and DHEA-S serum levels). Screening for viral persistence (based on PCR in stool samples and SARS-CoV-2 spike antigen levels in plasma in a subgroup of the cases) was negative. Sensitivity analyses (pre-existing illness/comorbidity, obesity, PEM, medical care of the index acute infection) revealed similar findings and showed that persistent cases with PEM reported more pain symptoms and had worse results in almost all tests. Conclusions: This nested population-based case-control study demonstrates that the majority of PCS cases do not recover in the second year of their illness, with patterns of reported symptoms remaining essentially similar, nonspecific and dominated by fatigue, exercise intolerance and cognitive complaints. We found objective signs of cognitive deficits and reduced exercise capacity likely to be unrelated to primary cardiac or pulmonary dysfunction in some of the cases, but there was no major pathology in laboratory investigations. A history of PEM >14 h which was associated with more severe symptoms as well as with more objective signs of disease may be a pragmatic means to stratify cases for disease severity.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was funded by the Baden-Wuerttemberg Federal State Ministry of Science and Art (grant number MR/S028188/1).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical approval was obtained from the Ethics Committee of the University of Freiburg, Engelberger Strasse 21, D-79106 Freiburg/Germany (#21/1484_1), the Ethics Committee of the Medical Faculty of Heidelberg University, Alte Glockengiesserei 11/1, D-69115 Heidelberg/Germany (#S-846/2021), the Ethics Committee at the Medical Faculty of the Eberhard-Karls-University and at the University Hospital of Tuebingen, Gartenstrasse 47, D-72074 Tuebingen/Germany (#845/2021BO2), and the Ethic Committee of the University of Ulm, Oberberghof 7, D-89081 Ulm/Germany (#337/21).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

All data produced in the present study are available upon reasonable request to the authors.

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