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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 April 2024

Non-coresident family as a driver of migration change in a crisis: the case of the COVID-19 pandemic

• Unchitta Kan   ORCID: orcid.org/0000-0002-6215-4406 1 ,
• Jericho McLeod   ORCID: orcid.org/0000-0001-9807-2050 1 &
• Eduardo López   ORCID: orcid.org/0000-0003-3940-2608 1  

Humanities and Social Sciences Communications volume  11 , Article number:  503 ( 2024 ) Cite this article

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Changes in U.S. migration during the COVID-19 pandemic show that many moved to less populated cities from larger cities, deviating from previous trends. In this study, building on prior work in the literature showing that the abundance of family ties is inversely related to population size, we analyze these migration changes with a focus on the crucial, yet overlooked factor of extended family. Employing two large-scale data sets, census microdata and mobile phone GPS relocation data, we show a collection of empirical results that paints a picture of migration change affected by kin. Namely, we find that people migrated closer to family at higher rates after the COVID-19 pandemic started. Moreover, even controlling for factors such as population density and cost of living, we find that changes in net in-migration tended to be larger and positive in cities with larger proportions of people who can be parents to adult children (our proxy for parental family availability, which is also inversely related to population size). Our study advances the demography-disaster nexus and amplifies ongoing literature highlighting the role of broader kinship systems in large-scale socioeconomic phenomena.

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Introduction

The COVID-19 pandemic brought about a profound change in how people live and work. Among other things, the closure of facilities and the shift to remote work weakened the connection between people’s place of residence and work. Many individuals were no longer constrained to live in a particular city for economic opportunities, and some were incentivized by financial or personal reasons to relocate. It is not surprising then, that notable changes in U.S. domestic migration trends were observed after the pandemic started. Our analysis, as well as others (Coven et al. 2023 ; Haslag and Weagley, 2022 ), suggests that a substantial proportion of these deviations can be attributed to a modification in the destinations of people’s migration: more people moved to smaller (less populated) cities, especially from larger cities, and fewer people moved to large cities.

What characteristics of cities of different sizes attracted or repelled people during the pandemic? Less crowding, lower cost of living, measures introduced to control the pandemic, and even climate may have helped to explain the disruption of typical migration patterns and why people were moving to smaller cities. In this study, we investigate a factor that has been overlooked: extended family who reside elsewhere. We ask the question, can some of these migration changes be attributable to individuals relocating to be closer to kin?

Our question is motivated by a recent empirical finding that an individual is more likely to have kin ties located in smaller cities (McLeod et al. 2023 ), as well as lines of research that point to the critical role of kin ties (David-Barrett et al. 2023 ; Furstenberg, 2020 ; Mulder, 2018 ) and how they are particularly activated during crises (Reed et al. 2023 ; Völker, 2023 ). Importantly, COVID-19 was a major societal event that, at least temporarily, uncoupled geography and economic aspirations—the latter of which is in tension with extended family orientation (Miller, 1976 ). If family-driven migration was indeed happening at higher rates, flows to smaller cities would follow as a consequence of the spatial distribution of non-coresident family ties in the U.S. (McLeod et al. 2023 ).

In this paper, we investigate empirically the connection between changes in inter-city migration during the COVID-19 crisis, city population, and the role that non-coresident family played in both large-scale patterns and individual-level migration dynamics. Our analyses show these factors to be related, emphasizing that the spatial distribution of people’s non-coresident family is relevant in order to understand human migration. Here, for reasons explained below, our test of the influence of family on migration is for parental relationships. Our results point to important implications in the context of cities, and amplify ongoing literature which highlights the need for more research on the role of broader kinship in large-scale socioeconomic phenomena (David-Barrett, 2019 ; David-Barrett et al. 2023 ), especially non-household kin that is outside of the nuclear family (Furstenberg, 2020 ; Reed et al. 2023 ).

Non-coresident family as a driver of migration in a crisis

There are at least three lines of research that help us understand how non-household kin may influence migration decisions in normal times, and why it is plausible that people would relocate to be closer to them in times of crisis. The first line is referred to as the ‘family ties perspective’ (Mulder, 2007 , 2018 ). The second line is the literature which focuses on how social ties are activated in a crisis such as the COVID-19 pandemic (Reed et al. 2023 ; Völker, 2023 ). The third line is the well-known tension between personal economic and social achievements (Miller, 1976 ). We review each of these in turn.

Importance of kin

The ‘family ties perspective’ (Mulder, 2007 , 2018 ) argues that family ties need to be taken into account in migration analyses because of three crucial ingredients they offer: support, need for proximity, and uniqueness.

Family is central to social and support networks of people (Dunbar and Spoors, 1995 ; Plickert et al. 2007 ; Rözer et al. 2016 ; Wellman, 1979 ). While individuals can, and do, maintain ties with family via calls after moving away (David-Barrett et al. 2023 ; Lambiotte et al. 2008 ), family face-to-face interaction and transfer of practical or physical support cannot be fully replaced by virtual interaction; they require geographical proximity and thus incentivize migration.

Kin is fundamentally different from other types of relationships because it is given rather than chosen. Also, an important characteristic of kinship that is less prominent in other relationships is the feeling of responsibility that kin feels towards each other (Mulder, 2018 ). From transfers of resources to help with mundane tasks to emotional support to aversion of crises, people look to their kin. Parents and adult children are more than ten times as likely to give or receive major assistance compared to other types of relationships (Wellman, 1979 ). In 2012 alone, it was estimated that informal care, which includes kin-based intergenerational care, amounted to over one billion hours of unpaid work per week in the U.S. (Dukhovnov and Zagheni, 2015 ). People place greater importance on kin than non-kin when it comes to interpersonal contacts (David-Barrett et al. 2023 ), especially women in their child-rearing years; collectively, individuals may even prioritize family over themselves (Krys et al. 2023 ).

Research has also shown that merely having family in one’s social network can influence the composition of that network itself (Dunbar and Spoors, 1995 ; Rözer et al. 2016 ). At the same time, the composition of one’s support network can also become focused on kin ties in non-routine situations.

Crises and kin ties

In times of crises, kin ties may be particularly activated. For example, as formal care facilities shut down during the pandemic (Lee and Parolin, 2021 ), informal intergenerational care (e.g. day care of young children by grandparents or eldercare by adult children) was likely to be even more important. This may be evidenced in the study by Völker ( 2023 ), which indicated that while people’s social networks became smaller and focused on core ties after the COVID-19 pandemic started, the network of practical helpers of the elderly proportionally consisted more of their children; similarly, among individuals aged 18–35, parents made up a larger share of their practical helpers networks.

Simultaneously, during the pandemic, Reed et al. ( 2023 ) found an increase in communications with non-coresident kin. Tunçgenç et al. ( 2023 ) found family bonds to positively influence well-being, and that among close social ties, only family bonds were positively linked to engagement in health behaviors. These findings are in line with another study by Lee et al. ( 2023 ) showing the stability and strengthening of bonds with kin compared to other ties after the start of the pandemic.

The activation of kin ties in systemic crises is not a phenomenon specific to just COVID-19. Other examples dating decades earlier include Shavit et al. ( 1994 ) who have found similar results in the context of the Gulf War, and Hurlbert et al. ( 2000 ) in the context of hurricane Andrew.

Extended kin in migration decisions

While previous literature findings have established that non-household kin can influence migration decisions (see, e.g., Kan, 2007 ; Spring et al. 2017 ), kinship factors may be in tension with economic aspirations in the propensity to migrate (Miller, 1976 ). In other words, people may have to choose between being closer to economic opportunities or to extended family. However, it can be argued that COVID-19 is a unique case in that it is a systemic crisis that enabled more mobility, not less. At a time where individuals might look to their kin the most, the pandemic also decoupled geography and employment, allowing people to achieve proximity to their extended family to a greater degree than before.

Parental ties

Among kin ties, intergenerational ties are understood in the literature to be the “important arena of action in Western kinship systems” (Furstenberg, 2020 ). The majority of support from kin flows vertically (typically in the downward direction, i.e., from parents to children or grandchildren). That about 75% of people in the U.S. whose parents or children are still alive reside within 30 miles from one of them reflects this situation (Choi et al. 2020 ). Parents-in-law often act similarly to parents in terms of the support they provide (Compton and Pollak, 2014 ; Wellman and Wortley, 1989 ), and in many cultures they can be considered consequential in terms of one’s social network and cooperation (David-Barrett, 2023 ). While there is some modest evidence of horizontal transfer of time and resources between family members such as siblings (Wellman and Wortley, 1989 ; White, 2001 ), relatively little is known about contacts and exchanges between extended kin such as aunts, uncles, cousins, etc. (Furstenberg, 2020 ).

Aside from the clear evidence of their prominence, parental ties are a very compelling variable to study as a “pull factor” in pandemic-migration for the reason that migration is highly age- and life course-specific as well as context-dependent (Millington, 2000 ).

Individuals aged between 18 and 44 tend to have the highest propensity to migrate (Molloy et al. 2011 ; Rogerson and Kim, 2005 ). This propensity declines with age (Castro and Rogers, 1984 ), which may have to do with the accumulation of social capital (Kan, 2007 ); it also declines with the household family life cycle stage (Miller, 1976 ).

At the same time, individuals in the 18–44 age range may also be the most likely to need support from or give support to parental family. For example, dual-earner parents returning to live near their own parents for childcare assistance is an identified phenomenon in the literature (Bailey et al. 2004 ). Grandparents are known to be important providers of early childcare (Dukhovnov and Zagheni, 2015 ; Furstenberg, 2020 ; Mulder, 2018 ), and evidence suggests proximity to them increases labor force participation among mothers with young children (Compton and Pollak, 2014 ). In the other care direction, individuals in their late child-rearing period–who belong to the so-called “sandwich generation”–may also find themselves needing to care for their aging parents.

Therefore, if it is largely the individuals in their twenties, thirties, and forties who were migrating for familial reasons, it is likely that they would look to where their parents were. For this reason, we focus in this study primarily on parental family.

Materials and methods

Design of study.

Our study design seeks to provide evidence for the existence of a three-way relation between pandemic-migration, city population size, and non-coresident family. First, we analyze large-scale relocation data and show the increased migration flow to cities with smaller population after the pandemic started. Then, through three empirical investigations, we link migration to parental family as well as parental family to population; the first investigation is done at the individual level, while the other two are done at the city level.

In Investigation 1, we examine whether there are differences in individual-level migration rates to move towards parental family (or “return to home”) after the pandemic began in 2020. In Investigation 2, we construct a proxy variable that estimates the abundance of people who can be parents to adult children in each U.S. city and relate it to both city population and migration variables. In Investigation 3, we estimate an empirical model which tests whether cities with higher parental family availability saw a higher net population influx after the pandemic started.

Overall, Investigation 1 serves to validate our line of inquiry (linking migrational change to parental family) as well as our study assumptions, while Investigations 2 and 3 connect parental family availability to both city population and changes in net migration of cities. The empirical model controls for relevant migration factors and mitigates potential confounders. Put together, our study examines whether the trend to migrate to smaller cities is partially a consequence of people moving to be closer to family, coupled with the heterogenous spatial distribution of parental family ties in the U.S.

We now elaborate on these analyses and the methodology we employ, beginning with the data used in this study.

The two primary, large-scale data sets we employ in this study are the county-to-county relocation index aggregated from anonymized, opted-in mobile phone GPS data provided by the location intelligence company Spectus ( 2021 ), and the yearly U.S. Census American Community Survey Public Use Microdata Samples (ACS PUMS).

The Spectus data set has an advantage over administrative place-level migration data (e.g., from the U.S. Internal Revenue Service) due to its higher temporal resolution (weekly) as well as its ability to capture real time migration by algorithmically detecting new home location from mobile phone GPS instead of relying on individuals to report their change of address to governmental agencies (which could occur with a significant delay or not occur at all). Aggregated to the city level, our data encompass nearly one-fourth of all possible origin-destination city pairs in the U.S.

Our second large-scale data set, ACS PUMS, contains individual-level information on a subsample of the U.S. Census ACS respondents, including the demographics of each person in the household, their place of birth, and their current place of residence. The microdata sample also includes survey questions about migration in the past year, such as whether a respondent has moved and the location from which they moved. For our study, we obtain PUMS yearly samples for the years 2016 to 2021 (corresponding to six separate samples) from IPUMS USA (Ruggles et al. 2023 ) which integrates and harmonizes PUMS data across all survey years. Across the six years, our IPUMS USA sample contains in total N  = 18,694,272 person-records from N  = 8,247,978 households.

Supplementarily, we obtain city population sizes, as well as control variables used in our empirical model from the ACS aggregated estimates (U.S. Census Bureau, 2020 ) and the U.S. Bureau of Economic Analysis ( 2020 ).

Calculations of inter-city migration from Spectus data

In the raw form, the Spectus data measures the weekly relocation flows between pairs of counties in the U.S. as an aggregated index called the Relocation Index. To calculate the Relocation Index r h k ( t ) during week t between county h (old county) and county k (new county), Spectus uses the following formula:

Home location is detected using an algorithm that identifies persistent night-time GPS location. For movers, this algorithm detects a change in the home location. Note that the Spectus data preserves the privacy of the users as they are aggregated at the county level. Our data spans from January 2019 to December 2020.

Our study is focused on inter-city migration, and we take core-based statistical areas (CBSAs) to be the geographic unit of study. CBSAs are urban areas delineated by the U.S. Office of Management and Budget, and can be either micropolitan areas (with population between 10,000 and 49,999 people) or metropolitan areas (with population of at least 50,000 people). We estimate dyadic relocation flows at the CBSA level by performing additional aggregations on the county-level Spectus data. In particular, to estimate the number R i j ( t ) of moves (as opposed to an index) from city i to city j during week t , we use the formula

where the sums are over the counties that have a geographic correspondence to the appropriate CBSAs (i.e., counties that are part of the CBSAs), p h is the population of county h , and π h is the estimated device sampling rate for county h . We calibrate π h by calculating and comparing yearly Spectus flow at the county level to the average yearly county-to-county migration flow derived from the 5-year, 2016–2019 migration data published by the U.S. Census Bureau ( 2021 ).

We also calculate the number R i j ( θ ) of relocations from city i to city j spanning over a certain time period as

We use θ  = 0 to indicate the period between April 2019 and December 2019, which we take to be the baseline period in this study, and θ  = 1 to indicate the period between April 2020 and December 2020, the pandemic comparison period. Because we are only concerned with inter-city migration and not intra-city flows, we set R i i ( t ) and R i i ( θ ) to 0.

In total, there are 211,902 origin-destination city pairs for the baseline period and 192,946 origin-destination city pairs for the comparison period; both periods comprise 926 unique origin CBSAs and 926 unique destination CBSAs, i.e. all CBSAs within non-territory U.S. The smallest city in the data set is Lamesa, TX (with population around 13,000 people) and the largest city is the New York metropolitan area (with population around 19.3 million people).

Analysis of trend to move to smaller cities during COVID-19

To illustrate the trend to move to smaller cities during COVID-19–the phenomenon which motivated this study–we define \(z({P}^{{\prime} };P,\theta )\) , the probability that movers from origin CBSAs whose log-population sizes fall into the log-population bin P  + Δ P would relocate to a destination CBSA whose log-population falls into the \({P}^{{\prime} }+\Delta P\) bin during period θ (where Δ P is the bin size). Binning is employed to ease interpretation of the results and to manage fluctuations from the sparsity of samples of city population sizes, and we use log-population instead of raw population due to the skewed nature of city sizes in the U.S. (Ioannides and Skouras, 2013 ). Using the migration quantity derived from the Spectus data (Eq. ( 3 )), we calculate \(z({P}^{{\prime} };P,\theta )\) as follows:

where the notation ∑ i ∈ P indicates summation over cities i whose log-population sizes belong to the bin P  + Δ P .

To capture the changes in z after the pandemic started, we calculate the ratio \(z({P}^{{\prime} };P,\theta =1)/z({P}^{{\prime} };P,\theta =0)\) for all \({P}^{{\prime} },P\) .

Investigation 1: micro-level analysis of return-to-home movers

As a first investigation to examine potential links between non-coresident family and migration change during the COVID-19 pandemic, we employ the IPUMS USA microdata samples to study and compare the micro-level behavior of movers across the years 2016 to 2021. We examine whether there are differences in migration rates to move towards parental family after the pandemic began in 2020. In this exercise, we do not yet relate migration to specific cities or city sizes due to limitations in the data (namely, the PUMS variables of interest are only available at the levels of U.S. state and Census Public Use Microdata Areas). However, this exercise validates our line of inquiry (linking migrational change to kin) as well as study assumptions that will be relevant to our next investigation.

We classify three types of family-driven, “return-to-home” movers in the IPUMS USA data to analyze their patterns: (1) individuals moving into their parents’ households; (2) individuals moving back to their native state from elsewhere but not joining their parents’ household; and (3) family household units moving back to their native state from elsewhere. We call these types of moves Type 1, 2, and 3, respectively (see Table 1 ).

Type 1 movers are relatively simple to interpret: these are individuals who moved back in with their parents. For some adults and households that are a family unit, however, moving in with their parents may not be an option. Instead, they may choose to relocate to be within the locality of their parents. Precise information about the residence of one’s parents is highly identifiable and not publicly available. Therefore, we follow the literature (Compton and Pollak, 2014 ) and use one’s native state as a proxy variable for proximity to or presence of family ties. The residence of one’s parents is often the home in which one grew up, which is in turn often located in one’s state of birth. We refer to such returns to place of nativity performed by individuals as Type 2 moves, and we refer to such moves performed by family household units as Type 3 moves.

As a technical point which applies to both Type 1 and 2 movers, we consider individual movers to be persons who had migrated in the past year at the time of the PUMS survey, who were either a household of one person or a person who resided in a household in which not everyone had moved. If a household was labeled in the survey as a family household, was larger than one person, and every householder had moved, then we consider the household to be a family household unit mover (which is relevant to identifying Type 3 movers).

To identify Type 1 movers, we look at individual movers who had migrated within the same state or between states in the past year at the time of the survey (i.e., whose ‘ MIGRATE1 ’ variable values are either 2 or 3 in the encoding of the IPUMS USA data). For each person in the sample, IPUMS USA includes variables that identify the mother (‘ MOMLOC ’) and father (‘ POPLOC ’) of that person if they live in the same household. (These variables are calculated probabilistically by IPUMS USA as they are not present in the regular ACS PUMS.) For each individual mover, if at least one parent was present in their current household and the parent(s) had not also moved in the past year, we classify that individual as a Type 1 mover.

To identify Type 2 movers, we look at IPUMS USA individual movers who had migrated from a different state in the past year at the time of the survey (‘ MIGRATE1 ’ = 2). If their current state of residence (‘ STATEFIP ’) was the same as their state of birth (‘ BPL ’), we label them as Type 2 movers (i.e., individual movers moving back to place of nativity from elsewhere). We note that, strictly speaking, Type 1 moves are not mutually exclusive from Type 2 because one’s parental household may be in one’s native state (so Type 2 moves may contain some Type 1 moves). However, for our study, we exclude Type 1 moves from Type 2 moves.

Finally, we classify family households (‘ HHTYPE ’ is 1, 2, or 3) larger than one person whose every member had migrated in the past year to be Type 3 movers if their migration destination (current state of residence) was the place of birth of at least one householder.

We introduce the quantity λ m ( t ), where m   ∈  {1, 2, 3} indexes the type of movers, to capture the rates with which their respective types of migration occur in each year t . Table 1 provides a summary (who and where moved) of these move types and their corresponding λ m ( t ). For notational brevity, we assume the dependence on t in λ m ( t ) is implicit and write λ m interchangeably. We calculate λ 1 and λ 2 for each year by dividing the number of Type 1 and Type 2 movers, respectively, by the total number of individual movers. We calculate λ 3 by dividing the number of Type 3 movers by the total number of household movers. Our calculations are with consideration to sampling weights (see Section “Discussion” of the publication by U.S. Census Bureau ( 2021 ) for ACS PUMS sampling weights). Explicitly, we use the formula

to calculate λ 1 and λ 2 , where u denotes persons in the IPUMS USA sample s ( t ) in year \(t,{{\mathbb{1}}}_{{{{\rm{ind}}}}}\) and \({{\mathbb{1}}}_{m}\) are binary variables ( = 1 if a condition is met and 0 if it is not) indicating whether u is an individual mover and Type m mover, respectively. The variable w u corresponds to the person sampling weight (‘ PERWT ’) of u .

For λ 3 , we use

where q denotes households in the yearly IPUMS USA sample, \({{\mathbb{1}}}_{{{{\rm{hh}}}}}\) and \({{\mathbb{1}}}_{3}\) are binary variables indicating whether q is a family household mover and Type 3 mover, respectively, and w q corresponds to the household sampling weight (‘ HHWT ’) of q .

We note that for the 2020 sample, IPUMS USA uses experimental sampling weights published by U.S. Census Bureau to address data collection and quality issues associated with the COVID-19 pandemic. Although the results for 2020 should still be interpreted with caution, we include them because they nevertheless provide valuable information.

Investigation 2: city-level analysis of parental family availability, population size, and net migration

If people were to migrate back to parental family after the COVID-19 shock, then we should see also larger flows to cities which have a greater abundance of parental family households. McLeod et al. ( 2023 ) refer to this abundance as availability . For our study, we require measures of parental family availability and net migration at the city level, described in this section.

Constructing parental family availability proxy variable from IPUMS USA

As mentioned earlier, there is considerable scarcity of data linking people to the location of their parents. Therefore, in this exercise we introduce a proxy variable v i which estimates the stock of households in each city i whose householder(s) can be parents to adult children, capturing the notion of parental family availability.

Motivated by the literature and by methodological reasons, we design our proxy variable v i as follows. Because PUMS data do not contain information on kin who do not reside in the same household as the sample individuals, we cannot directly infer the city in which an individual’s parents may be located. At the same time, based on the literature we discussed in Section “Parental ties” (Castro and Rogers, 1984 ; Kan, 2007 ; Miller, 1976 ; Millington, 2000 ; Molloy et al. 2011 ; Mulder, 2018 ; Rogerson and Kim, 2005 ), we assume that relocating individuals tend to generally be in their twenties, thirties, or forties, because these demographic groups tend to have the highest propensities to migrate. (We validate this assumption using statistics of the age of “return-to-home” movers in our previous analysis). With this assumption, we expect that their parents would be at least a generation older. Therefore, we base our parental household estimation on certain age and marriage criteria.

Explicitly, within the IPUMS USA 2019 sample, we identify households in the sample that satisfy the following criteria:

Family households in which the head of household or their spouse (if married and spouse is present) is at least 50 years old, or

Non-family households in which the head of household is at least 50 years old and is either married but no spouse present, separated, divorced, or widowed.

The specific age of 50 was chosen based on the literature finding that “the vast majority of American parents who are older than the age of 50 provide support to children and grandchildren” (Furstenberg, 2020 ). Summing the household sampling weights (‘ HHWT ’) of these households estimates the total number of such households in the sample.

The most detailed geographic identifier in the ACS PUMS is the Public Use Microdata Area (PUMA) which consists of one or more contiguous counties and census tracts. Because our goal is to analyze migration patterns at the city level, we apply a PUMA-to-CBSA geo-allocation mapping algorithm to obtain city estimates of the parental family proxy variable. The mapping algorithm relies on the geographic correspondence file between PUMAs and CBSAs obtained from the Geocorr application maintained by the Missouri Census Data Center ( 2018 ). In this correspondence file, each entry is a PUMA–CBSA intersection along with an allocation factor which represents the proportion of the population living in this intersection out of the entire PUMA. Using these factors, we allocate the weighted total number of households satisfying the above criteria in each PUMA to each CBSA that intersects with it. Finally, we divide this number in each CBSA by the weighted total number of households in the CBSA to obtain the share of parental family households v i . We relate this proxy variable to both city population size and migration quantities described below.

City-level net migration

From our Spectus inter-city flows R i j ( t ) and R i j ( θ ), defined in Eqs. ( 2 ) and ( 3 ) respectively, we derive two net migration quantities: y i ( t ), and y i ( θ ).

The quantity y i ( t ) is a measure of net in-migration of a city and captures the inflow per outflow of city i during week t . It is given by

where the numerator gives the total flow into a city i from all other cities during week t and the denominator gives the total flow out of a city i to all other cities during week t . Notice that y i ( t ) > 1 implies that the migration inflow exceeds the migration outflow of city i during week t (i.e., positive net influx). Consequently, y i ( t ) captures the direction and magnitude of the net migration flow of a city.

We also calculate the corresponding quantity spanning the comparison time period, y i ( θ ), using

Similar to the approach in Section “Analysis of trend to move to smaller cities during COVID-19”, we can calculate the ratio y i ( θ  = 1)/ y i ( θ  = 0) to capture the changes in the net in-migration between the two time periods. We relate both y i ( t ) and y i ( θ  = 1)/ y i ( θ  = 0) to our parental family proxy v i .

Investigation 3: empirical model

Finally, we estimate an empirical model to help control for other factors that may have been at play in pandemic-migration. We use a difference-in-differences (DiD) strategy with a continuous treatment variable (namely, v i ). DiD is an econometric model used to estimate the effect of a treatment by comparing the outcomes in the treated and untreated groups between two time periods (Lee, 2016 ); a continuous treatment is used to model increasing intensity of treatment instead of splitting units into treated and untreated groups. Applied to our study, with our outcome being changes in net in-migration, we can model parental family availability as a continuous treatment and take the two time periods to be before and after the COVID-19 shock.

Our model, which pools data from both before and during the pandemic, can be written as

where the dependent variable is the log-ratio of the net flux into a city (see Eq. ( 8 )) after and before the pandemic started, measuring the changes in migration. C denotes scalar control variables, indexed by a . Notice that the city-specific differences that existed in the dependent variable across cities before the COVID-19 shock are accounted for by the denominator in the log-ratio, constituting the city fixed effect.

Our coefficient of interest, β , measures whether higher parental family availability v would lead to higher net flux after the pandemic started. The coefficient γ is city-independent and accounts for the effects that COVID-19 alone had on the dependent variable, constituting the time fixed effect. Finally, the city-dependent control variables C a are included because we cannot ignore the possibility that the changes in migration behavior may have also been influenced by other factors whose importance may vary after the pandemic started. Using population density as an example, individuals may experience a higher desire to move to less dense places during the pandemic to avoid being infected, in which case the ρ coefficient for population density will be negative. We include control variables that are relevant to relocation decisions: population size and density, median home value; median income; employment level (number of jobs per person); and the share of single family homes (SFH) in the city (home ownership is a major aspiration in the American life, and with work-from-home policies, households may have had more flexibility to seek a location where SFH were more available). All control variables except the share of SFH are in natural log scale.

Increased migration from large to small cities

We first present the changes in the probability to relocate to cities with log-population bin \({P}^{{\prime} }\) from an origin city with log-population bin P . In Fig. 1 , we visualize the ratio \(z({P}^{{\prime} };P,\theta =1)/z({P}^{{\prime} };P,\theta =0)\) as a function of P and \({P}^{{\prime} }\) (see Section “Analysis of trend to move to smaller cities during COVID-19” and Eq. ( 4 )). We select b  = 10 equisized population bins to achieve granularity without significant sparsity (however, we find qualitatively consistent results for b  = 5,...,10). Figure 1a reveals that there is a considerable increase in the probability for movers from large U.S. cities to migrate to small cities after the pandemic started (red region in the bottom right corner of Fig. 1a ). At the same time, people from large cities were also less likely to migrate to another large city.

Panel a shows the changes in the probability z to relocate between (binned) city sizes before and during COVID-19 and suggests that movers from large cities were more likely to relocate to small cities after the pandemic started than during the 2019 baseline period (red region in the bottom right corner). Panel b shows a binned scatterplot of our parental family availability proxy v in relation to log-population (blue, left vertical axis) and net in-migration changes (grey, right vertical axis) after COVID-19 shock. The dots represent the mean vertical axis values given the horizontal axis bins (with 50 discrete, equidistant bins along the v -axis in total); error bars represent the 95% CI of the means. The grey line and blue curves are fitted regression lines for the means (linear and order-2 polynomial, respectively) with the shaded regions corresponding to the 95% CI of the regression estimates. The grey vertical axis is \(\log \left[y(\theta =1)/y(\theta =0)\right]\) , which measures how much more (or less) of an attractor the cities in each bin became after the pandemic started (larger positive values indicate that on average the cities saw larger inflow per outflow after the pandemic started). Panel c shows a time-series of the average inflow per outflow of cities (log scale) grouped by quintiles of parental family availability v (shaded regions correspond to the 95% CI of the means), suggesting proportionally high increases in net in-migration to cities in the high v -quintiles after the COVID-19 shock in April 2020 compared to the corresponding time in the prior year.

In numbers, our estimates derived from the Spectus data (see Section “Calculations of inter-city migration from Spectus data”) indicate cities that were smaller than 500,000 in population had an influx of almost 52,000 people from cities that had over 500,000 in population in excess of what was observed during the baseline period in 2019. In total, between April 2020 and December 2020, these cities saw an increase in their net in-migration by 80% as compared to the same period in 2019 (60,000 versus 100,000). In other words, the excess influx from cities larger than 500,000 in population accounts for 95% of the increase in the net in-migration to these cities whose population is smaller than 500,000. Meanwhile, the top 10 largest cities saw twice as much net out-migration between April 2020 and December 2020 as compared to the same period in 2019 (− 82,000 versus − 45,600).

If it is the case that part of this migration change is due to by family-driven migration, the increased migration to smaller cities may have followed as a consequence of the uneven distribution of family availability across the U.S. (McLeod et al. 2023 ), where people in larger cities are less likely to have non-coresident family living nearby (hence would migrate elsewhere towards family). And because of the population distribution of cities in the U.S., in which the total number of people living across all cities with population size in a bin around P decays with P (Ioannides and Skouras, 2013 ), there is a bias in the direction of more extended family being located in cities with progressively smaller P . To see if this is a valid line of inquiry (i.e., to check the premise), we determine if individuals or households made the decision to “move back home” to be closer to family at a higher rate once the pandemic started in comparison to the pre-pandemic period.

Micro-level dynamics of “moving back home”

We analyze the rates λ m ( t ), where m   ∈  {1,2,3}, at which the three types of return-to-home migration we classified in the IPUMS USA microdata occurred in each year t between 2016 and 2021 (for methodology, see Investigation 1, Section “Investigation 1: micro-level analysis of return-to-home movers”). Table 1 provides a summary of these move types and their corresponding λ m ( t ). If parental households became a more important “insurance” destination under the COVID-19 crisis, we should expect to see a spike in λ 1 in the year 2020. Similarly, we should see increases in λ 2 and λ 3 after 2019.

In Fig. 2a , we indeed observe a jump in the Type 1 migration rate λ 1 in 2020. However, this rate dropped back to pre-pandemic levels in 2021. A possible interpretation of this result is that individuals who were able to (and perhaps needed to) move back in with their parents did so promptly after the pandemic started.

Panel a shows the rate for individual movers to move into their parents’ households ( λ 1 , Type 1 movers in the microdata); Panel b shows the rate for individual movers to move back to their place of birth (POB) from elsewhere but not joining their parents’ household ( λ 2 , Type 2 movers). Panel c shows λ 3 , the rates for Type 3 moves, i.e., family household units moving back to their native place from elsewhere.

Analyzing the demographics of Type 1 movers in our IPUMS USA samples, we find that they tend to be young (median age of 25) and have low income (mean income of $25,266). More than half of them did not have a college degree, and about 60% were employed. Interestingly, the mean and median income of movers, as well as the percentage of college degree holders, were higher in 2020 and 2021 than in pre-pandemic years.

On the other hand, λ 2 , capturing the rate for Type 2 moves in which individuals moved to place of birth from elsewhere, saw a decrease in 2020 but an increase in 2021 (Fig. 2b ). While not attempting to provide an explanation, a possible interpretation is that those individuals who did not have the option to move in with parents waited until 2021 to move back to their native state. Compared to Type 1 movers, Type 2 movers tended to be older (median age 29) and with a higher mean income. Similar to Type 1 movers, the mean and median income of Type 2 movers, as well as the proportion of college degree holders, were higher in 2020 and 2021 than in pre-pandemic years.

In Fig. 2c , we observe an increase in λ 3 in 2020, and even a larger one in 2021, indicating that the pattern for family households to move back to their native states from elsewhere (Type 3 moves) increased in prevalence in 2020 and continued to do so in 2021. Plausibly, relocating an entire family household requires more logistical planning and “wait-and-see”, which could help explain the continued increase in the rate to move back to native place.

An interesting temporal-demographic dynamic we observe is that households that performed Type 3 moves in 2021 tended to have slightly older householders compared to prior years. Moreover, compared to prior years, a smaller proportion of family movers in 2020 and 2021 had eldest children who were younger than 5 or between 5 and 10 years old, but a slightly larger proportion of them had eldest children who were in their teens. Since this indicates that, proportionally, Type 3 movers comprised slightly more of householders who are at the end of their child-rearing years, it may provide a modest support for the possibility that eldercare was somewhat a more prominent driver of family-related migration at the time.

Finally, we note that the demographics of movers here conform to our expectations (that they tend be inbetween their twenties and forties), which also helps to validate study assumptions that we rely on when constructing our family proxy variable v in our next analysis.

Cities with greater parental family availability observed larger positive changes in net in-migration

Up to now, we have shown that people moved more to smaller places and that moves towards place of origin also increased. In this section, we show in more detail that the parental family availability variable v i is both negatively correlated with population size and positively correlated with an increase in net in-migration during the pandemic in comparison to the baseline pre-pandemic period (Investigation 2, see Section “Investigation 2: city-level analysis of parental family availability, population size, and net migration”).

Before analyzing v i , we explore how it relates to the only other known systematic quantity about distribution of family in the U.S. Namely, we check how v i relates to ϕ i , another estimate constructed by McLeod et al. ( 2023 ) of the probability that an individual living in city i reports having non-household family nearby. We note that ϕ is not employed more broadly in this article because it is only available for 258 CBSAs, whereas our proxy v is calculated for all CBSAs in the U.S.

Following the methodology in McLeod et al. ( 2023 ), we perform a modal regression of v i as a function of ϕ i . Modal regression identifies the typical behavior of a random variable as a function of some independent variable using a smoothing kernel, picking up functional relationships that traditional regressions may otherwise miss (Chen et al. 2016 ). The method constructs 2-d kernel density estimates (KDE) (Hastie et al. 2009 ) from our set of data points ( ϕ i ,  v i ). We use a Gaussian smoothing kernel and a bandwidth calculated using the Silverman method (Silverman 1986 ) for the kernel density estimator. Then, conditioned on each value on the horizontal axis ( ϕ ), it calculates the conditional density of the KDE along the vertical axis ( v ), and extracts the local mode of the conditional density. These local modes of v are displayed as the white curve in Fig. 3 (values of KDE are visualized using a color scale). Figure 3 shows that our parental family availability proxy v is monotonically related to ϕ , i.e. cities with larger ϕ also tend to have larger values of v .

A heatmap showing normalized density of our parental availability proxy variable v conditioned on the general family availability ϕ obtained from McLeod et al. ( 2023 ) (see Section “Cities with greater parental family availability observed larger positive changes in net in-migration” for discussion and methodology).

Now, relating v i to city log-population size in Fig. 1b (blue curve, left vertical axis), we find that parental family availability exhibits a decaying trend with city population (i.e., the share of households whose householders can be parents to adult children are larger in small cities than in large cities), which is consistent with the finding in McLeod et al. ( 2023 ) for general family availability ϕ .

Grouping cities by quintiles of parental family availability v , we see a notable increase in y i ( t ), a measure of net flux into city i , for those cities in the top three quintiles right after COVID-19 broke out in the U.S. when compared to the corresponding time period the year prior (Fig. 1c ). A similar pattern is not seen in the bottom v -quintile.

In Fig. 1b (grey line), we observe: (a) an increasing relationship between v i and \(\log \left[{y}_{i}(\theta =1)/{y}_{i}(\theta =0)\right]\) , and (b) \(\log \left[{y}_{i}(\theta =1)/{y}_{i}(\theta =0)\right] > 0\) for cities with greater levels of parental availability. This indicates that, for those cities with greater parental family availability v , their net influx tended to be larger during the pandemic compared to before. (By definition, the larger y i ( θ ) is, the larger the inflow per outflow; hence, y i ( θ  = 1)/ y i ( θ  = 0) > 1 indicates that inflow per outflow was larger during the pandemic than before).

These results are consistent with our proposition that kin partially drove the migration changes that we see at the population level. However, other population effects may be at play. For example, lower population density and cost of living may be driving people to move to smaller cities which tends to coincide with larger v i . To perform a more in-depth test of the situation, we next estimate the empirical model, described in Section “Investigation 3: empirical model”, with these factors as control variables.

Empirical model

We show the results of our empirical model (Eq. ( 9 )) in Table 2 . In all of the models with different sets of control variables, our coefficient of interest, β , is positive and statistically significant (bold numbers in Table 2) . This strongly indicates that cities with greater parental family availability, v , saw a larger increase in inflow per outflow. The result from Fig. 1b suggested that having greater parental family availability led a city to one of three scenarios: (i) in-migration was larger during the pandemic period compared to before; or (ii) out-migration was lower during the pandemic period; or (iii) a combination of the two previous scenarios. The regression results suggest that these scenarios would happen to a greater extent for cities with larger v compared to cities with smaller v . The complete model, Model (4), estimates that a city that is 10 percentage points larger in v than another city would see a 6.5% larger (positive) change in inflow per outflow after the pandemic started.

The coefficients of the controls in each model align with our intuition of the general relocation behavior during the pandemic. For example, we expected that both population (Models (2) and (3)) and median home value (Models (3) and (4)) would have a significant negative effect on the dependent variable as movers sought less populated and cheaper destinations. We also find that the share of SFH has a positive effect on the dependent variable.

Overall, our empirical results support the proposition that kin ties played a role in the shift in migration to smaller cities during the COVID-19 pandemic. To the best of our knowledge, such an attempt to connect pandemic-migration to non-coresident family has not been done. Our study adds to both the migration literature and the family ties perspective by showing that while socioeconomic and physical factors such as population density and cost of living may have been at play in pandemic-migration, the picture would be incomplete if family ties are neglected (Table 2) . The migrational mechanism that this study casts light upon may help in migration modeling–for example, family ties or place of nativity for subpopulations could be incorporated in models such as the generalized gravity model for human migration (Park et al. 2018 ).

Qualitatively, if the migration decision process is thought of as “a hierarchically ordered set of values” or priorities (Miller, 1976 ), our study suggests that family became more highly ranked against other factors in a systemic crisis such as the COVID-19 pandemic. The fact that we see increased out-migration from large cities and increased migration towards family or place of nativity after the pandemic started supports previous literature findings (Miller, 1976 ) that economic aspirations and extended family proximity are in tension. At the same time, this would suggest that the comparative success of large cities (see, e.g., the literature on the scaling of productivity and innovation with city size Bettencourt et al. 2007 ) may come at a social and personal cost to individuals who have moved to these cities: they may have needed to replace relatively distant kin with local non-kin in their social network due to the cost of maintaining distant relationships, losing much of the remarkable support family provides–see also David-Barrett ( 2019 ) who explains this phenomenon.

Beyond these contributions, our study advances the emerging study of the demography-disasters nexus. As is argued by Karácsonyi et al. ( 2021 ), perhaps even more important than enumerating death tolls, “the key to understanding impacts [of disasters] and avoiding them in the future is to understand the relationships between disasters and population change, both prior to and after a disaster.” Our observations linking city population to certain trends in population realignment show how the heterogeneity in the location of extended family across the U.S. is a source of vulnerability for cities. This heterogeneity, which existed prior to the pandemic, may be due to differences in demographic, socioeconomic, or infrastructural factors. Better social or institutional support for those lacking local non-coresident family could potentially help to mitigate the effects. On the other hand, this pandemic-migration may also contribute to irreversible changes in talent availability, real estate usage, and the growth of certain industries. In this regard, future research may focus on understanding these consequences in the long run.

At the destination cities, the prioritization of face-to-face interactions with family during the lockdown stages of the pandemic suggested by survey data (see Feehan and Mahmud, 2021 ) might have led to elevated transmission risks in these smaller cities and, when looked at together with other factors, may help to explain why these cities experienced comparatively worse epidemiological outcomes than in large, dense cities in later waves (Cheng et al. 2020 ; Koh et al. 2020 ; Pew Research Center, 2022 ). These epidemiological consequences can be long-lasting if we consider, e.g., the increased prevalence of long-COVID.

Our study is not without limitations. Most notably, part of our results rely on proxy variables of family because other large-scale data are not available that allow us to directly construct networks of movers and their family ties that also contain detailed geographic information. To this end, we combine multiple analyses at different levels in this study to provide more robust evidence of the effects of family on pandemic-migration. The lack of better data about extended family location is compatible with well-justified needs for individual privacy. At the same time, it does suggest that better sources of data that explore the spatial distribution of family across the U.S. are needed. The last systematic study of extended family across the U.S. was the now-discontinued National Survey of Families and Households; other access-controlled surveys such as the Panel Study on Income Dynamics are helpful albeit on a smaller scale. Our results are mostly with respect to parental family, but the effects could be larger if we include other extended kin—here, again, new data would enable us to gain more insights (Furstenberg, 2020 ).

To summarize, in this study we present coherent empirical evidence, using multiple sources of data, that the increased preference to migrate to smaller cities may be partly driven by the increased migration towards non-coresident family, coupled with the heterogeneous distribution of family ties in the U.S. in which people are more likely to have family ties located in smaller cities (McLeod et al. 2023 ). On a larger scale, our study amplifies ongoing literature highlighting the role of broader kinship systems (not limited to just the nuclear family) in macro-level socioeconomic phenomena (David-Barrett, 2019 ; David-Barrett et al. 2023 ; Furstenberg, 2020 ; Reed et al. 2023 ).

Data availability

The U.S. Census, IPUMS USA, Geocorr, and BEA data sets used in this study are publicly available and can be downloaded from the respective organizations’ websites. The Spectus data were used under licence for the current study and are not publicly available. The code used to perform analyses of publicly available data during the current study is available at the archived online repository https://doi.org/10.5281/zenodo.10935991 (Kan, 2024 ).

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Acknowledgements

We thank Spectus for providing the relocation data, as well as Brennan Lake and Éadaoin Ilten for interfacing on behalf of Spectus. We are also grateful to Professors Noel D. Johnson, David W. S. Wong, and Sam G. B. Roberts for their helpful comments.

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Unchitta Kan, Jericho McLeod & Eduardo López

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Conceptualization: UK, EL; Methodology: UK, JM, EL; Software: UK, JM; Investigation: UK; Supervision: EL; Writing- original draft: UK; Writing- review and editing: UK, JM, EL.

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We accessed our GPS-based relocation data under a strict agreement with Spectus; the agreement precludes attempts to de-anonymize or disaggregate the data. Spectus reviewed our current study prior to journal submission. While the IPUMS USA data are publicly available, we follow Census Bureau principles by using these data solely for statistical purposes and not attempting to disaggregate or identify any individual within the data samples.

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Kan, U., McLeod, J. & López, E. Non-coresident family as a driver of migration change in a crisis: the case of the COVID-19 pandemic. Humanit Soc Sci Commun 11 , 503 (2024). https://doi.org/10.1057/s41599-024-03020-6

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Ex-assistant principal charged with child neglect in case of boy who shot teacher

The Associated Press

Signs stand outside Richneck Elementary School in Newport News, Va., Jan. 25, 2023. Denise Lavoie/AP hide caption

Signs stand outside Richneck Elementary School in Newport News, Va., Jan. 25, 2023.

NEWPORT NEWS, Va. — A former assistant principal at a Virginia elementary school has been charged with felony child neglect more than a year after a 6-year-old boy brought a gun to class and shot his first-grade teacher .

A special grand jury in Newport News found that Ebony Parker showed a reckless disregard for the lives of Richneck Elementary School students on Jan. 6, 2023, according to indictments unsealed Tuesday.

Parker and other school officials already face a $40 million negligence lawsuit from the teacher who was shot, Abby Zwerner. She accuses Parker and others of ignoring multiple warnings the boy had a gun and was in a "violent mood" the day of the shooting.

Criminal charges against school officials following a school shootings are quite rare, experts say. Parker, 39, faces eight felony counts, each of which is punishable by up to five years in prison.

The Associated Press left a message seeking comment Tuesday with Parker's attorney, Curtis Rogers.

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'say something' tip line in schools flags gun violence threats, study finds.

Court documents filed Tuesday reveal little about the criminal case against Parker, listing only the counts and a description of the felony charge. It alleges that Parker "did commit a willful act or omission in the care of such students, in a manner so gross, wanton and culpable as to show a reckless disregard for human life."

Newport News police have said the student who shot Zwerner retrieved his mother's handgun from atop a dresser at home and brought the weapon to school concealed in a backpack.

Zwerner's lawsuit describes a series of warnings that school employees gave administrators before the shooting. The lawsuit said those warnings began with Zwerner telling Parker that the boy "was in a violent mood," had threatened to beat up a kindergartener and stared down a security officer in the lunchroom.

The lawsuit alleges that Parker "had no response, refusing even to look up" when Zwerner expressed her concerns.

When concerns were raised that the child may have transferred the gun from his backpack to his pocket, Parker said his "pockets were too small to hold a handgun and did nothing," the lawsuit states.

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With gun control far from sight, schools redesign for student safety.

A guidance counselor also asked Parker for permission to search the boy, but Parker forbade him, "and stated that John Doe's mother would be arriving soon to pick him up," the lawsuit stated.

Zwerner was sitting at a reading table in front of the class when the boy fired the gun, police said. The bullet struck Zwerner's hand and then her chest, collapsing one of her lungs. She spent nearly two weeks in the hospital and has endured multiple surgeries as well as ongoing emotional trauma, according to her lawsuit.

Parker and the lawsuit's other defendants, which include a former superintendent and the Newport News school board, have tried to block Zwerner's lawsuit.

They've argued that Zwerner's injuries fall under Virginia's workers' compensation law. Their arguments have been unsuccessful so far in blocking the litigation. A trial date for Zwerner's lawsuit is slated for January.

Prosecutors had said a year ago that they were investigating whether the "actions or omissions" of any school employees could lead to criminal charges.

What schools can (and can't) do to prevent school shootings

Howard Gwynn, the commonwealth's attorney in Newport News, said in April 2023 that he had petitioned a special grand jury to probe if any "security failures" contributed to the shooting. Gwynn wrote that an investigation could also lead to recommendations "in the hopes that such a situation never occurs again."

It is not the first school shooting to spark a criminal investigation into school officials. For instance, a former school resource officer was acquitted of all charges last year after he was accused of hiding during the Parkland school massacre in 2018.

Chuck Vergon, a professor of educational law and policy at the University of Michigan-Flint, told The AP last year that it is rare for a teacher or school official to be charged in a school shooting because allegations of criminal negligence can be difficult to prove.

More often, he said, those impacted by school shootings seek to hold school officials liable in civil court.

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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.