Epidemiology and transmission dynamics of COVID-19 in two Indian states
Epidemiology in southern India
By August 2020, India had reported several million cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with cases tending to show a younger age distribution than has been reported in higher-income countries. Laxminarayan et al. analyzed data from the Indian states of Tamil Nadu and Andhra Pradesh, which have developed rigorous contact tracing and testing systems (see the Perspective by John and Kang). Superspreading predominated, with 5% of infected individuals accounting for 80% of cases. Enhanced transmission risk was apparent among children and young adults, who accounted for one-third of cases. Deaths were concentrated in 50- to 64-year-olds. Incidence did not change in older age groups, possibly because of effective stay-at-home orders and social welfare programs or socioeconomic status. As in other settings, however, mortality rates were associated with older age, comorbidities, and being male.
Abstract
Although most cases of coronavirus disease 2019 (COVID-19) have occurred in low-resource countries, little is known about the epidemiology of the disease in such contexts. Data from the Indian states of Tamil Nadu and Andhra Pradesh provide a detailed view into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission pathways and mortality in a high-incidence setting. Reported cases and deaths have been concentrated in younger cohorts than would be expected from observations in higher-income countries, even after accounting for demographic differences across settings. Among 575,071 individuals exposed to 84,965 confirmed cases, infection probabilities ranged from 4.7 to 10.7% for low-risk and high-risk contact types, respectively. Same-age contacts were associated with the greatest infection risk. Case fatality ratios spanned 0.05% at ages of 5 to 17 years to 16.6% at ages of 85 years or more. Primary data from low-resource countries are urgently needed to guide control measures.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), has spread rapidly around the world since emerging in Wuhan, China, in late 2019 (1). Our current understanding of COVID-19 comes largely from disease surveillance and epidemiologic studies undertaken during the early phases of the pandemic in China (1–3) and in the high-income countries of Europe (4, 5) and North America (6–8). However, most confirmed cases of COVID-19 have now occurred in low- and middle-income countries (LMICs), where a substantial proportion of individuals may be at increased risk of severe outcomes and face barriers to accessing quality health services (9–11). Although multiple modeling studies have sought to assess how COVID-19 might affect individuals and communities in such settings (12–14), almost no primary studies of the transmission dynamics and clinical outcomes of COVID-19 in LMICs are available to validate these models and inform intervention strategies (15).
More than 1.3 billion people are at risk of SARS-CoV-2 infection in India, where concerns over COVID-19 have prompted large-scale containment strategies at the national, state, and local levels (16). The country’s first known COVID-19 case, documented on 30 January 2020, was an Indian national evacuated from China (17). Andhra Pradesh and Tamil Nadu are two states in the south of India whose 127.8 million residents collectively account for about 10% of the country’s total population. Although they are not the wealthiest states in India, Andhra Pradesh and Tamil Nadu are among the states with the largest health care workforces and public health expenditures per capita, and are known for their effective primary health care delivery models (18–20). Both states initiated rigorous disease surveillance and contact tracing early in response to the pandemic. Procedures include syndromic surveillance and SARS-CoV-2 testing for all individuals seeking care for severe acute respiratory illness or influenza-like illness at health care facilities; delineation of 5-km “containment zones” surrounding cases for daily house-to-house surveillance to identify individuals with symptoms; and daily follow-up of all contacts of laboratory-confirmed or suspected COVID-19 cases, with the aim of testing these individuals 5 to14 days after their contact with a primary case, irrespective of symptoms, to identify onward transmission (21, 22). We analyzed comprehensive surveillance and contact-tracing data from these programs in an effort to understand transmission dynamics and clinical outcomes of COVID-19 in South India, and to provide insights into control of SARS-CoV-2 in similar LMIC settings.
Expansion of SARS-CoV-2
In India, surveillance of COVID-19 was initiated with airport screening for severe acute respiratory infection, especially for travelers from China. Tamil Nadu further instituted thermal and clinical screening at land borders with other states on 4 March 2020. Nationwide, testing was initially prioritized for symptomatic individuals with history of travel or contact with a confirmed COVID-19 case within the previous 14 days, and was expanded to include all symptomatic individuals and asymptomatic contacts of confirmed cases in states between 20 and 28 March 2020. We detail the timeline of changes in surveillance practices at federal and state levels in the supplementary materials.
Tamil Nadu and Andhra Pradesh each recorded their first laboratory-confirmed COVID-19 cases on 5 March. Under-ascertainment of cases during March and early April was likely due to limited testing availability and testing algorithms. The proportion of tests yielding positive results peaked at 39.7% in Tamil Nadu and 33.5% in Andhra Pradesh on 30 and 31 March 2020, respectively, when the daily number of tests performed was low in the two states (range, 379 to 469 tests; Fig. 1). Throughout early April, increases in the number of tests performed daily coincided with a reduction in the proportion of tests yielding positive results. Our analyses include data collected through 1 August, at which time Tamil Nadu and Andhra Pradesh had identified 263,330 and 172,209 cases, respectively (table S1). (Because testing and contact tracing constitute routine public health activities, data collection was not governed by an institutional review board.)
Fig. 1 Incidence over time and across districts in Tamil Nadu and Andhra Pradesh.
(A to C) Red shading of regions on the choropleth map indicates higher incidence over each period: 1 March to 31 May 2020 (A), 1 to 30 June 2020 (B), and 1 to 31 July 2020 (C). Districts are plotted according to 2019 administrative boundaries and do not reflect the recent bifurcation of Tirunelveli, Villuppuram, Vellore, and Chengalpattu districts. (D) Cases detected each day in each state (points) and 7-day moving averages (lines). Cases are aggregated by testing date; data are plotted in blue and lavender for Tamil Nadu and Andhra Pradesh, respectively, for all figure panels. (E) Diagnostic tests conducted each day (top) and the proportion of tests yielding positive results (bottom) for the period March through May 2020, when districts reported comprehensive testing information to the state governments. Points and lines indicate daily counts and 7-day moving averages, respectively. The high proportion of positive tests from late March to mid-April, while case number remained relatively stable, may indicate a period during which cases were undercounted because of limited testing capacity. (F) Daily deaths in the two states. Points and lines indicate daily counts and 7-day moving averages, respectively. (G) Cumulative incidence (solid lines) and mortality (dashed lines) per 10,000 population.
The earliest clusters of locally acquired cases emerged in March in Chennai and surrounding coastal districts of eastern Tamil Nadu. Of all districts, Chennai ultimately experienced the highest cumulative incidence of COVID-19, totaling 102,199 cases (204.6 per 10,000 population) by 1 August 2020. An outbreak beginning on 28 April caused 1142 cases by 15 May in the adjoining districts of Ariyalur, Cuddalore, Perambalur, and Villuppuram in Tamil Nadu; thereafter, few cases were identified in these districts until early June (fig. S1). Although limited in March and April, incidence in southern districts of Tamil Nadu surrounding Madurai increased during June and reached rates commensurate with incidence in the northern districts of Chennai, Kancheepuram, and Tiruvallur by 1 August, with one to four new positive detections per 10,000 population daily. Similar increases in incidence occurred throughout all districts of Andhra Pradesh in June, where the numerical and geographic extent of cases remained limited during April and May despite similar levels of testing relative to Tamil Nadu.
Statewide estimates of the time-varying reproduction number Rt, describing the number of secondary infections that each infected individual would be expected to generate (23), declined from a range of 1.7 to 3.0 in Tamil Nadu and 1.4 to 4.3 in Andhra Pradesh over the period 10 to 23 March to a range of 1.0 to 1.3 in both states by the third week of the initial countrywide lockdown (fig. S3). Expansions in testing over this same period, however, are likely to bias analyses of changes in Rt over time (24). Estimates of Rt held in the range 1.1 to 1.4 from 15 May onward within both states, although incidence trajectories differed over time by district (fig. S1), likely reflecting changes in both the uptake and enforcement of social distancing interventions as well as the effectiveness of contact-tracing efforts.
Contact tracing
Contact-tracing efforts in the states reached 3,084,885 known exposed contacts of confirmed cases by 1 August 2020 (table S2); individual-level epidemiological data on cases and contacts, as well as laboratory test results, were available from 575,071 tested contacts of 84,965 confirmed cases. Traced contacts tended to be younger and were more often female than their linked index cases (table S3). Additionally, test-positive individuals identified through contact tracing were, on average, 1.3 years (bootstrap 95% confidence interval, 1.1 to 1.5 years) younger and 4.5% (3.7 to 5.4%) less likely to be male than the overall population of COVID-19 cases in the two states (table S4). Because studies in other settings have shown the risk of symptomatic disease to be higher among older age groups and among males (25), these findings may indicate the identification of less-severe infections through active case finding.
The mean number of contacts tested per index case was 7.3 (interquartile range, 2 to 9), and 0.2% of index cases were linked to >80 tested contacts (range, 1 to 857; Fig. 2A). Numbers of contacts tested varied by district, and the geographic distribution of index cases included in our analyses did not necessarily reflect the geographic distribution of all reported cases (table S5). No positive contacts were identified for 70.7% of index cases for whom reliable contact-tracing data, including test results, were available (Fig. 2A). The distribution of the number of positive contacts linked to each index case was heavily right-skewed, and we estimated a negative binomial dispersion parameter for the distribution of the number of infected contacts traced to each index case of 0.51 (95% confidence interval, 0.49 to 0.52). On average, 9.2 contacts were tested for each index case with ≥1 contact identified, as compared to 5.7 contacts tested for each index case without positive contacts identified (two-sided bootstrap P < 0.001; fig. S4). Although our analysis is limited in that it does not necessarily capture all secondary infections (e.g., among contacts who were not reported), these observations are consistent with the presence of superspreading related to differences in individual contact patterns (26).
Fig. 2 Analyses of contact-tracing data for 575,071 tested contacts of 84,965 infected individuals from whom test results were available, together with individual-level detailed epidemiological data on exposed contacts and index cases.
(A) Left: Distribution of the number of contacts traced for each index case in Tamil Nadu and Andhra Pradesh, binning values ≥80 (0.2%). Right: Number of positive contacts traced from each index case. The inset shows the cumulative attributable proportion of secondary infections (y axis) associated with quantiles (x axis) of the distribution of the number of positive contacts traced per index case; percentiles 0 and 100 indicate index cases with the fewest and the most positive contacts identified, respectively. (B) Adjusted estimates from Poisson regression models addressing the proportion of female and male contacts with a positive result among those who were known to be exposed to female and male index cases; models further control for case and contact age groups (interacted) and for state. We stratify for high-risk and low-risk contacts, as defined in table S6. Points and lines indicate mean estimates and 95% confidence intervals. (C) Proportion of contacts with a positive test result stratified by case and contact age, for high-risk and low-risk contacts. At right, contour plots indicate the proportion of exposed contacts with a positive test result by case and contact age for all contacts and high-risk contacts on a choropleth scale; see table S8 for raw counts. Positive test results among tested, exposed contacts are interpreted as evidence of probable transmission from the index case. Also plotted are the age distributions of index cases for all infected contacts and for infected high-risk contacts.
Assuming that test-positive contacts were infected by the index case to whom they were traced, we estimated that the overall secondary attack rate (or risk of transmission from an index case to an exposed contact) was 10.7% (10.5 to 10.9%) for high-risk contacts, who had close social contact or direct physical contact with index cases without protective measures, and 4.7% (4.6 to 4.8%) for low-risk contacts, who were in the proximity of index cases but did not meet these criteria for high-risk exposure (tables S6 and S7). Data on exposure settings, available for 18,485 contacts of 1343 index cases, revealed considerable differences in transmission risk associated with differing types of interaction. Secondary attack rate estimates ranged from 1.2% (0.0 to 5.1%) in health care settings to 2.6% (1.6 to 3.9%) in the community and 9.0% (7.5 to 10.5%) in the household. Among 78 individuals with high-risk travel exposures—defined as close proximity to an infected individual in a shared conveyance for ≥6 hours—we estimated a secondary attack rate of 79.3% (52.9 to 97.0%).
Whereas secondary attack rate estimates did not differ considerably with respect to the sex of cases and their contacts (Fig. 2B), analyses stratified by case and contact age identified the highest probability of transmission, given exposure, within case-contact pairs of similar age (Fig. 2C and table S8). These patterns of enhanced transmission risk in similar-age pairs were strongest among children aged 0 to 14 years and among adults aged ≥65 years, and may reflect differences in the nature of intragenerational and intergenerational social and physical interactions in India (27). Nonetheless, the greatest proportion of test-positive contacts within most age groups were exposed to index cases aged 20 to 44 years (Fig. 2C, fig. S5, and table S8). Serological surveys in other settings have demonstrated that case-based surveillance may lead to underestimation of SARS-CoV-2 infection prevalence among children (28, 29); therefore, it remains crucial to establish whether the role of children in transmission is underestimated in studies such as ours using case-based surveillance to identify index infections.
Mortality among COVID-19 cases
In a subcohort of 102,569 cases in Tamil Nadu and 22,315 cases in Andhra Pradesh who tested positive at least 30 days before the end of the study follow-up period, the overall case fatality ratio was 2.06% (1.98 to 2.14%; Fig. 3). Age-specific estimates ranged from 0.05% (0.012 to 0.11%) at ages 5 to 17 years to 16.6% (13.4 to 19.9%) at ages ≥85 years. Risk of death was higher among male cases than among female cases overall, and the magnitude of this difference widened in the oldest age groups. Higher mortality in older age groups and among males has similarly been observed in high-income settings (1–7, 30–32).
Fig. 3 Mortality among confirmed COVID-19 cases.
(A) Adjusted hazard ratios for mortality by 1 August 2020 estimated via Cox proportional hazards models including all confirmed cases. Exposures designated “Ref.” indicate the referent group for hazard ratio calculation. (B) Absolute case fatality risk estimates obtained via bootstrap resampling of individuals with confirmed infection by 1 July 2020. (C to E) Survival probabilities by age within this cohort over the 30-day period after testing, plotted for all cases (C), male cases (D), and female cases (E). Blue-to-red coloration aligns with younger-to-older age group, for strata as defined in the above tables. Age bins were selected on the basis of reporting of U.S. COVID-19 surveillance data (Fig. 4).
Half of the cases ascertained before death in Tamil Nadu and Andhra Pradesh succumbed within ≤6 days of testing (interquartile range, 3 to 12 days), and 1042 fatal cases (18.2% of 5733 observed) were identified either ≤24 hours before death or posthumously. Our estimates of time to death in Tamil Nadu and Andhra Pradesh are below what has been observed internationally: In the United States, median time to death from the date of hospital admission was 13 days (8), and the World Health Organization estimated that time to death after onset of symptoms could range from 2 to 8 weeks on the basis of data from China (33). Our observations likely indicate that a substantial proportion of patients in Tamil Nadu and Andhra Pradesh are diagnosed late in their disease course, although differences in patients’ health status, health care systems capacity, and approaches to end-of-life care may also contribute to variation in time to death.
In a survival analysis of the full cohort, mortality by 1 August 2020 was independently associated with older age, with stepwise increases in the adjusted hazard ratio of time to death for each successive age group besides children aged 0 to 4 years, consistent with our estimates of the case fatality ratio (Fig. 3). Additional predictors of mortality included being male [adjusted hazard ratio, 1.62 (1.52 to 1.73) compared with being female], receipt of a test early in the epidemic [0.87 (0.72 to 1.07) for being tested between 1 May and 30 June, 0.74 (0.61 to 0.91) for being tested between 1 July and 1 August, both relative to testing between 1 March and 30 April], and state of residence [1.08 (1.01 to 1.16) for residents of Tamil Nadu compared with those in Andhra Pradesh].
Among decedents in the two Indian states, the most prevalent comorbid conditions were diabetes (45.0%), sustained hypertension (36.2%), coronary artery disease (12.3%), and renal disease (8.2%; table S9). Although prevalence of any comorbidity was highest among decedents at older ages, this pattern differed across conditions; diabetes was most prevalent among decedents aged 50 to 64 years, and liver disease and renal disease were most prevalent in fatal cases at ages 0 to 17 years and 18 to 29 years, respectively. At least one comorbid condition was noted among 62.5% of fatalities, in comparison to 22% of fatalities in the United States as of 30 May 2020 (34).
Epidemiological comparison to high-income settings
Cases in Tamil Nadu and Andhra Pradesh showed a younger age distribution than cases reported in the United States as of 21 August 2020 (Fig. 4) (35). Comparison of cumulative COVID-19 incidence across ages showed that the observed differences surpassed expectations based on population age distributions alone, as signaled by the absence of parallel trends in age-specific incidence (table S10). Although lower across all age groups in Tamil Nadu and Andhra Pradesh in comparison to the United States, age-specific COVID-19 incidence increased sharply in both settings between the 5- to 17-year and the 18- to 29-year age groups. Whereas incidence declined steadily at ages older than 30 to 39 years in the two Indian states, incidence increased at ages of ≥65 years in the United States.
Fig. 4 Demographic comparison of populations, cases, and deaths for Tamil Nadu and Andhra Pradesh versus the United States.
(A) Age distribution of the population of Tamil Nadu and Andhra Pradesh (blue) against that of the U.S. population (purple) for comparison; underlying data are shown in table S10. Estimates are census extrapolations for the year 2020 in both settings. (B) Age distribution of cases. (C) Cumulative incidence of COVID-19 by age. (D) Age distribution of deaths. (E) Cumulative COVID-19 mortality by age. Data for the United States include all cases and deaths reported by 21 August 2020 (35).
In the two Indian states, only 17.9% of COVID-19 deaths occurring on or before 1 August 2020 were among individuals aged ≥75 years, compared with 58.1% of COVID-19 deaths in the United States (Fig. 4 and table S10). Age-specific COVID-19 mortality was lower in Tamil Nadu and Andhra Pradesh than in the United States, consistent with the lower reported incidence of disease. Although COVID-19 mortality trended upward across ages in the two Indian states, mortality plateaued at ages of ≥65 years, in contrast to observations in the United States where COVID-19 mortality reached 69.6 deaths per 10,000 individuals aged ≥85 years; this observation was consistent with the relatively lower incidence of disease at the oldest ages within the two Indian states.
Discussion
Our findings, based on comprehensive surveillance and contact-tracing data from the Indian states of Tamil Nadu and Andhra Pradesh, provide insight into the epidemiology of COVID-19 in resource-limited populations. Our analysis suggests substantial variation in individuals’ likelihood of transmitting: No secondary infections were linked to 71% of cases whose contacts were traced and tested. Although the role of children in transmission has been debated (36, 37), we identify high prevalence of infection among children who were contacts of cases around their own age; this finding of enhanced infection risk among individuals exposed to similar-age cases was also apparent among adults. School closures and other nonpharmaceutical interventions during the study period may have contributed to reductions in contact among children. Nonetheless, our analyses suggest that social interactions among children may be conducive to transmission in this setting. Last, our analyses of fatal outcomes reveal an overall case fatality ratio of 2.1%. Even though our estimates of age-specific case fatality ratios are similar to those in other settings, such comparisons are limited by uncertainty in the proportion of infections ascertained as cases (30, 38). Lower relative incidence of COVID-19 among older adults in Tamil Nadu and Andhra Pradesh has contributed to stark differences in the overall case fatality ratio and age distribution of decedents relative to observations in the United States and other high-income countries (32).
Several factors may contribute to our observation of limited COVID-19 incidence and mortality among older adults in Tamil Nadu and Andhra Pradesh. Imperfect surveillance systems may have contributed to under-ascertainment of cases among older adults, although this circumstance is unexpected given strong public and clinical awareness of COVID-19 and the predisposition of older adults to severe disease. Case-based surveillance may likewise underestimate attack rates among younger adult age groups in high-income settings (28, 29). It is plausible that stringent stay-at-home orders for older Indian adults, coupled with delivery of essentials through social welfare programs and regular community health worker interactions, contributed to lower exposure to infection within this age group in Tamil Nadu and Andhra Pradesh. Our finding may also reflect survivorship bias if older adults in India are at disproportionately low risk for SARS-CoV-2 infection relative to the general population—for instance, as a result of higher socioeconomic status (39). Life expectancy at birth is 69 years in India, versus 77 years in China, 79 years in the United States, and 83 years in Italy and South Korea (40); as such, socioeconomic factors distinguishing individuals who survive to old age from the general population are likely more pronounced in India than in higher-income settings with longer average life expectancies (41, 42).
Prospective testing of a large sample of exposed individuals through integrated active surveillance and public health interventions in Tamil Nadu and Andhra Pradesh provided an opportunity to characterize secondary attack rates as a function of both case and contact age, identify risk factors for transmission, and account for deaths outside of health care settings—a limitation of mortality surveillance in other settings (30, 43, 44). However, several limitations should be considered. The contact-tracing data analyzed included only 20% of all reported cases as index cases and represented only 19% of all contacts traced; case-finding efforts further varied by district and over time within Tamil Nadu and Andhra Pradesh. Contacts who complete testing and supply personal information to tracing teams may not have been representative of the full population. Another limitation was the lack of data on timing of exposure and onset of symptoms in relation to testing dates; this necessitated assumptions about the identification of true index cases. More robust temporal data would reduce the dependence on such assumptions, provide greater insight into the directionality of transmission, and reduce risk for misclassification of infection status among contacts with positive or negative results at the time of testing (45, 46). The lack of temporal data also prevented us from estimating several epidemiologic parameters of interest. Current estimates of both the incubation period (~4 to 6 days) and the serial interval (~3 to 5 days) come from China (1, 47–51). Several factors can modify the incubation period of respiratory viral infections, including the route of acquisition, the infectious dose, and the period of exposure to infected cases (52). The serial interval between successive infections is expected to be lower in high-transmission settings. Data allowing estimation of these parameters for SARS-CoV-2 in LMICs are needed to inform quarantine policies and other epidemic response efforts. Some true positives might have been misclassified as a result of imperfect test sensitivity, particularly among contacts tested as few as 5 days after exposure to a confirmed case. Imperfect test sensitivity has been attributed to inadequate sample collection procedures and low viral load in the upper respiratory tract, particularly for presymptomatic or asymptomatic cases (53). This limitation could lead to an overall underestimate of transmission risk within case-contact pairs. Finally, although comorbidity data collected as part of COVID-19 mortality surveillance revealed clinical and epidemiological attributes of fatal cases, the fact that such data were not collected for all diagnosed cases prevented inference of the contribution of comorbidities to fatal outcomes.
Surveillance and contact tracing are critical components of an effective public health response to COVID-19 (54, 55). In our study, data generated by these activities within two states of South India provided key insights into the local epidemiology and transmission dynamics of SARS-CoV-2 without competing with emergency response activities for limited resources—a high priority in many LMICs where health workers and diagnostic equipment are already in short supply (15). Similar studies are necessary to inform the successful adoption of epidemic control measures in low-resource settings globally.
Acknowledgments
We are indebted to the work of the Governments of Tamil Nadu and Andhra Pradesh as well as health care workers and field workers engaged in outbreak response in these settings. Permission for analysis and publication of the data included in this report was granted by the Governments of Tamil Nadu and Andhra Pradesh. Funding: J.A.L. received support from the Berkeley Population Center (National Institute of Child Health and Human Development grant P2CHD073964). R.L. received support from NSF grant CCF-1918628 to the Center for Disease Dynamics, Economics & Policy, and from U.S. Centers for Disease Control and Prevention grant 16IPA16092427 to Princeton University. Author contributions: Conceptualization, R.L., B.W., J.A.L.; methodology, B.W., J.A.L.; software, J.A.L.; formal analysis, J.A.L.; investigation, S.R.D., K.G., C.M.B., S.N., K.S.J.R., J.R.; resources, S.R.D., K.G., C.M.B., S.N., K.S.J.R., J.R.; data curation, R.L., J.A.L.; writing—original draft, B.W., J.A.L.; writing—review and editing, R.L., B.W., S.R.D., K.G., C.M.B., S.N., K.S.J.R., J.R., J.A.L.; visualization, J.A.L. Competing interests: K.G. is the principal secretary to the Government of Tamil Nadu for the Animal Husbandry, Dairying and Fisheries Department. C.M.B. is the principal secretary to the Government of Tamil Nadu for the Department of Backward Classes, Most Backward Classes, and Minorities Welfare. K.S.J.R. is the special chief secretary to the Government of Andhra Pradesh for the Department of Health, Family Welfare, and Medical Education. J.R. is the principal secretary to the Government of Tamil Nadu for the Department of Health and Family. K.G., C.M.B., and J.R. are members of the Team for Epidemic Monitoring, Interventions and Standardizing Health Care Protocols, Government of Tamil Nadu.All other authors declare no competing interests. Data and materials availability: De-identified data and code for replication of the analyses are available at the corresponding author’s GitHub page, https://github.com/joelewnard/covid-india (56). This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
Supplementary Material
Summary
Materials and Methods
Figs. S1 to S5
Tables S1 to S10
MDAR Reproducibility Checklist
Resources
References and Notes
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De-identified data and code for replication of the analyses: https://doi.org/10.5281/zenodo.4003365.
Information & Authors
Information
Published In
Science
Volume 370 • Issue 6517 • 6 November 2020
Pages: 691 - 697
PubMed: 33154136
History
Received: 11 July 2020
Accepted: 23 September 2020
30 September 2020
Copyright
Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors
Funding Information
National Science Foundation: CCF-1918628
Centers for Disease Control and Prevention: 16IPA16092427
Children and SARS-CoV-2: Key to spread?
We read with great interest the study by Laxminarayan et al. analyzing 84,965 SARS-CoV-2 infected cases captured in two Indian states and their 575,071 traced contacts. The authors suggest that children transmit the virus to a greater extent to their same-aged contacts than adults, based on a high secondary attack rate (SAR) for same-aged contacts of 26% for children aged 0-4 years (23 same-aged contacts out of only 89 contacts in this age group; table S8). However, the SAR for same-aged contacts in older children and adolescents (5-17 years) was only 11% (390/3419) and, therefore, identical to the median SAR for same-aged contacts in the adult age groups (range 7-71, Table S8) (1).
Furthermore, analysis of detailed data provided in Table S8, showed similar overall SAR in children/adolescents (7.6%) and adults (7.2%), but SAR affecting secondary cases >65 years (being at higher risk for severe infections) turned out to be much lower for index cases <18 years (6.1%) compared to adult index cases (11.8%) (1).
As reported by many other studies (2-5), children and adolescents were much less often afflicted by SARS-CoV-2 in the analyzed cohort. Consequently, secondary infections were traced back to adult index cases in 92.3% (33.966 infected contacts), while only 7.7% (2.825 infected contacts) of secondary infections have been transmitted by children or adolescents (1).
Despite this, the lead author emphasized the epidemiological role of children and the title of an official commentary referred to "children as key to spread" (6), which is neither supported by the data presented, nor by many other studies (2-5).
While further data on this issue are clearly needed, in the interim we strongly suggest not to overstate the role of children, and consequently childcare facilities and schools, in the ongoing COVID-19 pandemic.
1. R. Laxminarayan et al., Epidemiology and transmission dynamics of COVID-19 in two Indian states. Science 370, 691-697 (2020).
2. W. D. Carroll et al., European and United Kingdom COVID-19 pandemic experience: The same but different. Paediatr Respir Rev 35, 50-56 (2020).
3. F. Götzinger et al., COVID-19 in children and adolescents in Europe: a multinational, multicentre cohort study. Lancet Child Adolesc Health 4, 653-661 (2020).
4. R. M. Viner et al., Susceptibility to SARS-CoV-2 Infection Among Children and Adolescents Compared With Adults: A Systematic Review and Meta-analysis. JAMA Pediatr, (2020).
5. Z. Wu, J. M. McGoogan, Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases From the Chinese Center for Disease Control and Prevention. Jama, (2020).
6. K. Morgan. (Princeton University, Princeton Environmental Institute, https://www.princeton.edu/news/2020/09/30/largest-covid-19-contact-traci...), vol. 2020, accessed November 22nd 2020.
Children and adolescents play a minor role in the transmission of SARS-CoV-2
We read with interest Laxminarayan et al.'s study of COVID-19 cases and their contacts in two Indian states (1).
The authors highlight the significant role of children in transmission. However, this conflicts with the data presented (Table S8). The study included only 37,322 index cases aged below 18 years, but 241,613 middle-aged adults (30-49 years of age). The proportion of contacts of children and adolescents found to be SARS-CoV-2-infected was 7.6% (2,825/37,322), which is similar to the 7.3% (7,683/241,613) transmission rate observed in middle-aged adults [two-tailed Fisher's exact test: p=0.09; relative risk: 1.03 (95%CI: 0.99-1.07)]. These figures show that children: (i) contributed little to transmission of SARS-CoV-2; and (ii) were not transmitting SARS-CoV-2 more 'efficiently' than middle-aged adults.
There are also substantial limitations to the study that warrant consideration. Importantly, it is likely a significant proportion of contacts were falsely classified as 'uninfected'. Firstly, some contacts were tested within 5 days post-exposure, well within the average incubation period of SARS-CoV-2 infection (2); consequently, a substantial proportion will have developed COVID-19 subsequently. Secondly, most PCR-based SARS-CoV-2 assays have a sensitivity of only 80-90% (3), meaning up to 1 in 5 contacts would have had false-negative test results. Thirdly, no information is provided about the assays or associated quality control measures used for those SARS-CoV-2 tests done in private laboratories.
Another key limitation is that only a fraction of known contacts were tested, potentially introducing considerable selection bias (Table S2). In Andhra Pradesh, only 44.2% (789,583/1,786,479) of contacts were tested, with results available for only 38.1% (680,950/1,786,479).
Therefore, despite its limitations, the study indicates that children play a minor role in perpetuating the ongoing COVID-19 pandemic. This aligns with data from several pediatric and epidemiological studies showing that children far more commonly acquire SARS-CoV-2 from their parents than from their siblings or peers (4-6).
References
1. R. Laxminarayan et al., Epidemiology and transmission dynamics of COVID-19 in two Indian states. Science (in press): doi: 10.1126/science.abd7672 (2020).
2. Q. Bi et al., Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infect Dis 20, 911-919 (2020).
3. D. Jarrom et al., Effectiveness of tests to detect the presence of SARS-CoV-2 virus, and antibodies to SARS-CoV-2, to inform COVID-19 diagnosis: a rapid systematic review. BMJ Evid Based Med (in press); doi: 10.1136/bmjebm-2020-111511 (2020).
4. F. Götzinger et al., COVID-19 in children and adolescents in Europe: a multinational, multicentre cohort study. Lancet Child Adolesc Health 4, 653-661 (2020).
5. J. Ehrhardt et al., Transmission of SARS-CoV-2 in children aged 0 to 19 years in childcare facilities and schools after their reopening in May 2020, Baden-Wurttemberg, Germany. Euro Surveill 25, (2020).
6. European Centre for Disease Prevention and Control, COVID-19 in children and the role of school settings in COVID-19 transmission. Published 6th August 2020. Available at: https://www.ecdc.europa.eu/en/publications-data/children-and-school-sett.... Accessed 28th November 2020.
RE: Time varying basic reproductive number computed during COVID-19, especially during lockdowns could be questionable
While the epidemiological conclusions found in by Laxminarayan et al. [1] are supported by their data, the estimates of time-varying basic reproductive numbers raise some methodological issues that need further discussion. Limitations associated with computing time-varying basic reproductive rates are generally unavoidable, however, inappropriate interpretations, especially during lockdowns in the ongoing COVID-19 pandemic, have key implications for controlling the epidemic.
Suppose a certain number of infections at a time generate secondary infections, and these secondary infections could be treated as primary infections which in turn generate further secondary infections and so on. At each stage of the infection process, the number of individuals tested through contact tracing or through other criteria for testing individuals may not capture all the infected individuals that could arise under-reporting due to undiagnosed infections [2, 3, 4]. This leads to under-reporting of the true level of infections. Lockdowns add further difficulties in contact tracing and testing. The degree of under-reporting due to mis-diagnosis could also be varying over a lockdown period. Such limitations also apply to Laxminarayan et al. [1] study. Moreover, the authors also noted that "Expansions in testing over this period are likely to bias in computing time-varying basic reproductive rates…" Thus, it also is important to realize that heterogeneity may exist in the data that could have masked the reproductive measures due to the computation of state-level parameters. Tamil Nadu, where reproductive rates was found to be in the range 1.0 to 1.4, is a good example. When infections rise or decline in this sort of aggregated manner then a geometric growth model to compute the basic reproductive rate would be better rather than traditional arithmetic means of average secondary infections due to a primary infected case. This sort of inherent variability requires greater consideration in estimating epidemiological metrics (such as the basic reproductive numbers over time), as they have important implications for public health mitigation and planning.
References:
1. Laxminarayan R et al, Science, 30 Sep 2020: eabd7672
DOI: 10.1126/science.abd7672
2. Gibbons,C., et al. (2014). Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods. BMC Public Health. 14. DOI: 10.1186/1471-2458-14-147
3. Krantz S.G., Polyakov P., Rao A.S.R.S (2020). True Epidemic Growth Construction Through Harmonic Analysis, Journal of Theoretical Biology, 494, 7 June, 110243. https://doi.org/10.1016/j.jtbi.2020.110243
4. Krantz, S., & Rao, A. (2020). Level of underreporting including underdiagnosis before the first peak of COVID-19 in various countries: Preliminary retrospective results based on wavelets and deterministic modeling. Infection Control & Hospital Epidemiology, 41(7), 857-859. doi:10.1017/ice.2020.116
Acknowledgements: We thank Dr. Natasha Martin, University of California San Diego, and Dr. Chris T. Bauch, University Waterloo for providing useful comments on our original draft and pointing us to critical literature.
Secondary infection rate in COVID-19 contacts depends on testing policy used
Article by Laxminarayana R et al (1) has brought out important aspects related to epidemiology and transmission of COVID-19 in two south Indian states. Authors have also compared secondary infection rates between high risk and low risk contacts. However, I would like to bring out that this comparison has a major limitation which authors have not mentioned in their paper i.e. different testing policy for high risk and low risk contacts in India. All high risk contacts (symptomatic as well as asymptomatic) were being tested while only symptomatic individuals were tested among low risk contacts, as per Government of India's policy (2).
Secondary attack rate of diseases which have significant proportion of asymptomatic infections depend on strategy being employed to identify secondary cases. Hence, difference in testing strategy used in India to identify secondary cases among high risk and low risk contacts of COVID-19 cases could have brought significant bias in this comparison.
Secondly, data of less than 2% of contacts tested in Tamil Nadu state was available for analysis (1). Hence, these limitations of the study should also be considered while understanding the dynamics of COVID-19 transmission from this study.
References:
1. Ramanan Laxminarayan, Brian Wahl, Shankar Reddy Dudala, K. Gopal, Chandra Mohan, S. Neelima, K. S. Jawahar Reddy, J. Radhakrishnan, Joseph A. Lewnard. Epidemiology and transmission dynamics of COVID-19 in two Indian states. Science 10.1126/science.abd7672 (2020).
2. Indian Council of Medical Research. Strategy for COVID-19 testing in India (Version 4). April 09, 2020. https://www.icmr.gov.in/pdf/covid/strategy/Strategey_for_COVID19_Test_v4...
RE: : 452 Doctors Fighting COVID-19 already Dead In India till 30 september 2020
COVID-19 disease appears to have been associated with significant mortality amongst doctors and health care workers globally. I in this article tries to explore the various risk factors associated with this occupational risk of medical faternity , especially focusing on India. The novel Coronavirus SARS-CoV-2 outbreak has created a significant impact on the daily life and health care systems across the world including India [[1], [2], [3]]. COVID-19 has caused a huge burden and loss to the world where doctors bearing the brunt of physical burnout, mental stress, occupational risks of getting themselves infected with increased risk of morbidity and mortality, being the most front-line workers with little recognition from government,,laws, society in respect to compensation, free treatment , lodging in rental home and neighbourhood . Currently India is the third worst affected country in the world with more than 6,312,584 confirmed cases and above 98,708 deaths attributed to COVID-19 till 30th September 2020[4]. It has been observed that COVID-19 related mortality in the general population has been slightly lower in the South Asian subcontinent [5]. Concerns have been raised since nearly 452 doctors have succumbed to COVID-19 so far with a significant number of healthcare professionals affected as well not counted. The mortality of these doctors has made a dent in an already compromised health care system due to poor doctor patient ratio. The Indian Medical Association (IMA) National COVID-19 registry data suggests more than 1800 doctors have been infected with SARS-CoV-2 virus, where 76% of them are above the age of 50 years(6) . Doctors faces multiple challenges while they wanted dealing with this pandemic-specially limited personal protective equipment (PPE), training for doffing , their transport to hospitals and back home , proper rest, and rotation duty but worst of all loss of their own life and their family members sufferings in absence of him/her in this world due to coronavirus infection [7,8]. IMA issued a 'Red Alert' and requested the health authorities to ensure adequate safety of all doctors along with support from state sponsored free medical treatment and life insurance facilities to all involved in the coronavirus containment efforts [9]. I aimed here to explore the burden, the risk factors and lessons that can be learnt to protect these front line workers. As on date April 15, 2020; countries with the most reported physician deaths were from Italy 44%, Iran 15%, Philippines 8%, Indonesia 6%, China 6%, Spain 4%, USA 4%, and UK (11/278; 4%) [10]. Even though there is no global platform for assessing the mortality among doctors due to COVID-19, reported literature in the national media has raised concern Doctors in India account for 0.5% of the total deaths in India due to Covid-19. There have been so far 452 reported deaths among doctors in India due to COVID-19 it self over the last 6 months until 30 th September 2020 after reporting of the first COVID-19 case on January 30, 2020 . The percentage of death amongst doctors when infected by covid 19 is 17.7% when in west Bengal it is 11% till 1st October 2020 as i calculated. Developed nations in Europe, however, have had worse figures. Italy reported its 100th COVID-19 casualty amongst doctors back in April 2020 [11]. The reasons could be unpreparedness of these countries in terms of PPE, delayed implementation of social distancing and infection prevention strategies, and late lock down in the early phase of the pandemic, viral over load , attempt to earn more money during the lock down period have identified several risk factors that are associated with increased mortality amongst the healthcare workers and doctors.
• Age and Gender- Yoshida et al reported 120 deaths of medical doctors up to April 3rd, 2020 in the early months of COVID-19 [12]. Out of them 94 were between 50 and 99 years of age with a median age of 65 years and 108 were males. No reason for this disparity has been described in that article as I noted . The results of other meta-analysis showed that 60% of the COVID-19 patients were male the reasons not known [13]. The reason for a disproportionate mortality in the male gender is still unclear to me. Lack of hand hygiene may be a causative factor for increased prevalence of COVID-19 infection in males . Social roles of females in Asian countries like India such as cooking, house cleaning etc may sensitize females to having a different perspective towards hand hygiene. Increased concentration of Angiotensin-converting enzyme 2 (ACE-2) may be high in males as compared to females, and reluctance to seek proper and timely medical care and even lower rates of hand washing absolutely has been quoted to be few of the reasons [14]. As observed about the deaths amongst the general population most of fatality were seen among elderly male doctors who are given in duty [15]. The reasons postulated are these senior physicians had re-started to work during the earlier part of the pandemic when protection may have been insufficient, or they had associated manyco-morbidities and acquire high viral load during examination of patients what i think
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Medical Speciality -Physicians,Gynaecologists, Eye specialists, pathologists and from almost all the fields have already succumbed to COVID-19 in west Bengal and in India [11]. Frontline doctors those deal with COVID-19 patients seem to bear these brunt of mortality rates. Deaths were noted to be more common among general practitioners( Family Physicians in self clinic) or physicians, suggesting a higher risk of deaths among doctors who may have repeated encounters with asymotomatic COVID-19 patients or whose status not well known [15]. Physicians from certain fields of medicine such as anaesthesiology, dentistry and otorhinolaryngology, orthopaedics , Genecology , pathology, microbiology are more prone to acquire COVID-19 as their work involves intubation, oral/nasal or Cesarnian operation & other aerosol generating procedures, testing of swab and sputum , body fluids, stool,, cytology sample & doing FNAC or during grossing , which may place them at an increased risk
Lack of training to deal with COVID-19- The novel SARS-CoV-2 is predominantly a respiratory illness spread by droplet transmission and by air borne transmission in closed space, small room, chambers. The COVID-19 pandemic spread to the European countries at a time when most of the countries were unprepared. At the outset there was lack of training with no established standardized guidelines for PPE and disinfection to deal with the new viral pandemic. The spurt of cases and delay in lockdown to halt the spread of viral transmission also left the medical community overburdened with constrained resources [16]. Doctors and health care workers on the coronavirus frontline thus were exposed and that may explain some of the casualties in the early phase of the COVID-19 pandemic [17]. Subsequently Centers for Disease Control and Prevention (CDC) and other National Public health agencies guidelines have strengthened necessary infection prevention and control strategies including standards for personal protective equipment (PPE) to protect both patients and health care workers [18,19 ].
*** Before COVID-19, measures such as the use of quality PPE, physical distancing measures of six feet distances, continuous use of surgical / n95 masks by Health Care Professionals (HCPs) in hospitals were not also universally followed by medical professionals in all over state . This was true even during the H1N1 pandemic, where such wide scale preventive measures were not utilized ubiquitously. During the early period of the COVID-19 pandemic, doctors and other HCPs were not aware of the need for stringent practice of these preventive measures, which later proved to be highly effective against disease transmission. In their commentary, Xiang and colleagues describe how many doctors in China, unaware of the virus or the precautionary measures against acquiring the virus, got infected while attending their patients. Once the highly infectious nature of the virus was established, suitable precautionary measures were put into place by the Chinese authorities. The rest of the world took cue from the experience in China and pre-emptively guided their medical professionals towards learning preventive measures. However, issues such as insufficient time to train every HCP, interpersonal variations in learning new techniques and the unparalleled and novel nature of the disease burden may have led to inadequate awareness and precautionary measures at least during the early parts of the outbreak [20].
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Lack of adequate PPE in the early phase of the pandemic- The most effective preventive strategy against COVID-19 is a stringent and effective use of personal protective equipment (PPE). In early April, when the outbreak was exponentially increasing in magnitude in Europe, concerns regarding a lack of PPE were voiced by health care professionals across multiple countries in the continent [21,22]. Soon, the lack of numbers of PPE was managed by development of effective reuse methods and an increase in the production capacity of PPE.
***** Role of Race and Ethnicity and mortality in doctors: Race and Ethnicity has been shown to have a significant bearing on the course of COVID-19 disease. It has been acknowledged that there is disproportionate mortality and morbidity amongst black, Research should be done on this issues
******Other factors- Certain factors that i as author consider which are attributed to death in front line clinicians include age over 50 years, lack of adequate PPE, inadequate technique of donning and doffing, non-disclosure by patients of their exposure to possible COVID-19, Unkown patients status about covid 19 test, excessive working hours and poor doctor-patient ratio in this country . Certain co morbidities including advancing age, diabetes mellitus, cardiovascular diseases, chronic lung disease , Renal Disease on dialysis or immunocompromised states are also contributory factors .
******COVID-19 risk and strategies to protect health care professionals in India
India is a country with a large population where most of the patients(73%)seek treatment in government hospitals. The outpatients of these government hospitals have been flooded with patients after the lock down period is over. So It becomes been difficult to organise testing for COVID-19 of all patients visiting these hospitals by even antigen test . This could be another cause of exposure to the doctors from these patients in OPD and in Indoor patients . Most of the elderly doctors above 45 yrs-50 yrs having co-morbidities continued to do works in hospital wards , OPDs, in OTS , in Laboratories & doing private practice for silver coins even after taking proper precautions succumbs to death in various states across India. In west Bengal , the percentage of doctors' death is >11% amongst the infected doctors, which is seventeen times more than the Indian national average of common population death . IN india Percent of Doctors death is 17.7% amongst covid -19 infected doctors. One of the reasons for more doctors' death in the west Bengal state is doctor's works here possibly for more than longer days( one month at a stress in covid or SARI Hospitals) than the other states as the quarantine protocol of 15 days often may not be followed in managing covid 19 patients and due to large number of vacant posts of doctors as human resources for covid 19 ; the elderly and co morbid doctors aged doctors are forced to perform their duty and got no relief both in government and private set up or attempted earning more and more money during covid times to maintain family needs after lock down . Those who did not do private practice or did online consultation or vedio consultation were found saved of getting infected
• As such lack of COVID-19 safe facilities, resources, availability of appropriate PPE and lack of uniform application of infection prevention strategies remain cause of concerns and an occupational risk for health care professionals in India.
• However, cues have been taken with a stringent lock down, creation of dedicated COVID facilities, indigenous production of PPE and sanitisers, enforced central health guidelines and protocols. Training of the health care workers on use of PPE and prevention of spread of infection has been carried out [23].
• By the time India saw a substantial growth in COVID-19 positive patients, the production and import of PPE had already been ramped up in the country. However, even with this increased capacity, a shortage of PPE is expected in the country considering the number of population. The country's premier medical institute (All India Institute of Medical Science (AIIMS) Delhi, often issued timely guidelines on the reuse of PPE [24 ]. These measures may have contributed significantly towards reducing the effect of PPE shortfall.
• All the health care workers and doctors should screen for Tuberculosis and a major comorbidity such as diabetes before start practicing or doing Covid-19 duties. As history of latent or active tuberculosis is an important risk factor for acquiring COVID-19 infection [25,26].
Conclusion .
The significant mortality amongst doctors and other health care workers involved in the coronavirus frontline has been so serious concerning. There are various regional differences among countries and various risk-factors which lead to variable burden, Concerted efforts to understand the factors highlighted in the article, mitigating confounding factors, risk assessments and adequate protection of health care professionals is the need of the hour to support them in this public health crisis. This should not become one more unintended consequence of the COVID-19 pandemic
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RE: Why super spreaders spread the way they do?
This largest contact tracing study has confirmed the possibility that a small number of severe acute corona virus 2 ( SARS-CoV-2) infected persons lead the transmission cycle and children play a vital part in disease transmission. (1) A small review of reports on secondary attack rate observed a rate as high as 35% among individuals exposed in superspreading events. ( 2) Super spreaders increase the transmission of illness not due to change in the behavior of virus they are carrying, but due to the living environment ( household and community) and their social behavior during pre-symptomatic and early symptomatic phase of their illness. Having observed the dynamics of transmission in Chennai (Tamilnadu) among contacts of SARS-CoV-2 patients at hospital and community setting during the same period as the study, I put forward few case examples. In May and June 2020, the illness was more prevalent in two locations in Chennai ( Tondiarpet and Royapuram) where substantial number of people live as joint family ( of at least 5 people) in house smaller than 300 square feet with shared toilet for about 3 families.(3) We have seen multiple instances when the index case visited around 5 neighboring houses ( each with around 5 persons) per day during the pre-symptomatic period and during first or second day of illness. Since most were staying at home due to lockdown restrictions in May and June 2020, the contact time of persons with index case was longer and occurred at poorly ventilated small size homes leading to higher secondary attack rate. The data for which is likely to be found in the study data set. Similar high secondary attack rate was found in affluent homes with living area more than 2000 square feet. While the reason for the former example was clear, the explanation for transmission in the later situation was unclear. Possible reasons we thought were use of centralized air condition and contact with persons visiting home for assistance in domestic work. This could not be prevented, despite the prompt case identification strategy followed by Chennai corporation workers who did daily house to house enquiry about symptoms and effectively identified cases. Future epidemiology studies should focus on the explanation of factors which make these super spreaders lead the transmission cycle. The characteristics which could make super spreading SARS-CoV-2 infected persons could be increased tendency for socialization, poor mask compliance, preferences for physical intimacy, loud speaking, addressing gatherings, moderating house hold and non-household gatherings, etc. This information will facilitate revisions in preventive strategy especially when countries are preparing to open public places, schools and colleges.
Conflict of Interest: None to declare
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