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© 2020 Indian Journal of Medical Research, published by Wolters Kluwer - Medknow for Director-General, Indian Council of Medical Research
Prevalence of SARS-CoV-2 infection in India: Findings from the
national serosurvey, May-June 2020
Manoj V. Murhekar, Tarun Bhatnagar1, Sriram Selvaraju4, Kiran Rade10, V. Saravanakumar2,
Jeromie Wesley Vivian Thangaraj1, Muthusamy Santhosh Kumar1, Naman Shah14, R. Sabarinathan2, Alka Turuk11,
Parveen Kumar Anand16,*, Smita Asthana17,*, Rakesh Balachandar22,*, Sampada Dipak Bangar23,*, Avi Kumar Bansal19,*,
Jyothi Bhat27,*, Debjit Chakraborty28,*, Chethana Rangaraju29,*, Vishal Chopra32,*, Dasarathi Das35,*, Alok Kumar Deb28,*,
Kangjam Rekha Devi36,*, Gaurav Raj Dwivedi20,*, S. Muhammad Salim Khan37,*, Inaamul Haq37,*, M. Sunil Kumar33,*,
Avula Laxmaiah38,*, (Major) Madhukar39,*, Amarendra Mahapatra35,*, Anindya Mitra34,*, A.R. Nirmala30,*,
Avinash Pagdhune22,*, Mariya Amin Qurieshi37,*, Tekumalla Ramarao40,*, Seema Sahay24,*, Y.K. Sharma15,*,
Marinaik Basavegowdanadoddi Shrinivasa5,*, Vijay Kumar Shukla15*, Prashant Kumar Singh18,*, Ankit Viramgami22,*,
Vimith Cheruvathoor Wilson4,*, Rajiv Yadav27,*, C.P. Girish Kumar3, Hanna Elizabeth Luke6,
Uma Devi Ranganathan7, Subash Babu8, Krithikaa Sekar4, Pragya D. Yadav25, Gajanan N. Sapkal26,
Aparup Dasa,†, Pradeep Dasb,†, Shanta Duttac,†, Rajkumar Hemalathad,†, Ashwani Kumare,†, Kanwar Narainf,†,
Somashekar Narasimhaiahg,†, Samiran Pandah,†, Sanghamitra Patii,†, Shripad Patilj,†, Kamalesh Sarkark,†,
Shalini Singhl,†, Rajni Kantm,†, Srikanth Tripathyn,†, G.S. Totejao,†, Giridhara R. Babu31, Shashi Kant12, J.P. Muliyil9,
Ravindra Mohan Pandey13, Swarup Sarkar11, Sujeet K. Singh41, Sanjay Zodpey42, Raman R. Gangakhedkar11,
D.C.S. Reddy21 & Balram Bhargava@ for India COVID-19 Serosurveillance Group#
1ICMR School of Public Health, 2Division of Epidemiology & Bio-Statistics, 3Laboratory Division, ICMR-
National Institute of Epidemiology, 4Divisions of Epidemiology, 5Clinical Research, 6HIV/AIDS, 7Immunology,
8NIH-ICER (International Centers for Excellence in Research) Program, nICMR-National Institute for Research in
Tuberculosis, Chennai, 9Independent Consultant, Vellore, Tamil Nadu, 10WHO Country Oce for India, 11Division
of Epidemiology & Communicable Diseases, @Indian Council of Medical Research (DHR), Ministry of Health and
Family Welfare, 12Centre for Community Medicine, 13Department of Biostatistics, All India Institute of Medical
Sciences, New Delhi, 14Jan Swasthya Sahyog, Bilaspur, 15Directorate Health Services, Raipur, Chhattisgarh,
16Division of Bio-Statistics, oICMR-National Institute for Implementation Research on Non-Communicable
Diseases, Jodhpur, Rajasthan, Divisions of 17Epidemiology & Biostatistics, 18Preventive Oncology, lICMR-
National Institute of Cancer Prevention & Research, Noida,19Division of Epidemiology, jICMR-National JALMA
Institute for Leprosy & Other Mycobacterial Diseases, Agra, 20,mICMR-Regional Medical Research Centre,
Gorakhpur, 21Independent Consultant, Lucknow, Uttar Pradesh, 22Division of Clinical Epidemiology, kICMR-
National Institute of Occupational Health, Ahmedabad, Gujarat, 23Divisions of Epidemiology & Biostatistics,
24Social and Behavioural Research Sciences, hICMR-National AIDS Research Institute, 25Maximum Containment
Laboratory, 26Diagnostic Virology Group, ICMR-National Institute of Virology, Pune, Maharashtra, 27Division
of Communicable Diseases, aICMR-National Institute of Research in Tribal Health, Jabalpur, Madhya Pradesh,
28Division of Epidemiology, cICMR-National Institute of Cholera & Enteric Diseases, Kolkata, West Bengal,
Indian J Med Res Epub ahead of print
DOI: 10.4103/ijmr.IJMR_3290_20
Quick Response Code:
*All Nodal Ocers contributed equally (names given in alphabetical order)
All Institute Directors contributed equally (names given in alphabetical order)
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In India, the rst case of COVID-19 was reported
on January 30, 20201. As of June 20, 2020, 395,048
laboratory-conrmed cases and 12,948 deaths were
reported from India. There is a wide variation in the
reporting of cases across the States/Union Territories
and across the districts within each State2. The case
reporting is based on the testing of individuals by real-
time reverse transcription polymerase chain reaction
(RT-qPCR). Laboratory capacity for testing, health-
seeking behaviours and testing strategy in terms of
who gets tested, inuence the numbers reported.
Furthermore, the current testing criteria, which
prioritize the allocation of testing capacity, will miss
many asymptomatic and mild infections.
Knowledge about the true extent of infection
is critical for an eective public health response
29Division of Advocacy, Communication & Social Mobilisation, 30Lady Willingdon State TB Centre, Government
of Karnataka, 31Indian Institute of Public Health, gNational Tuberculosis Institute, Bengaluru, Karnataka,
32State TB Training & Demonstration Centre, Patiala, Punjab, 33State TB Training & Demonstration Centre
Thiruvananthapuram, Kerala, 34State TB Training & Demonstration Centre Ranchi, Jharkhand, 35,iICMR-
Regional Medical Research Centre, Bhubaneswar, Odisha, 36Division of Enteric Diseases, fICMR-Regional
Medical Research Centre, Northeast Region, Dibrugarh, Assam, 37Department of Community Medicine,
Government Medical College, Srinagar, Jammu & Kashmir, 38Division of Public Health Nutrition, dICMR-
National Institute of Nutrition, Hyderabad, Telangana, 39Division of Clinical Medicine, bICMR-Rajendra
Memorial Research Institute of Medical Sciences, Patna, Bihar, 40State TB Cell, Vijayawada, Andhra Pradesh,
eICMR-Vector Control Research Centre, Puducherry, 41National Centre for Disease Control &
42Indian Institute of Public Health, Delhi, India
Received August 1, 2020
Background & objectives: Population-based seroepidemiological studies measure the extent of SARS-
CoV-2 infection in a country. We report the ndings of the rst round of a national serosurvey, conducted
to estimate the seroprevalence of SARS-CoV-2 infection among adult population of India.
Methods: From May 11 to June 4, 2020, a randomly sampled, community-based survey was conducted
in 700 villages/wards, selected from the 70 districts of the 21 States of India, categorized into four strata
based on the incidence of reported COVID-19 cases. Four hundred adults per district were enrolled from
10 clusters with one adult per household. Serum samples were tested for IgG antibodies using COVID
Kavach ELISA kit. All positive serum samples were re-tested using Euroimmun SARS-CoV-2 ELISA.
Adjusting for survey design and serial test performance, weighted seroprevalence, number of infections,
infection to case ratio (ICR) and infection fatality ratio (IFR) were calculated. Logistic regression was
used to determine the factors associated with IgG positivity.
Results: Total of 30,283 households were visited and 28,000 individuals were enrolled. Population-weighted
seroprevalence after adjusting for test performance was 0.73 per cent [95% condence interval (CI):
0.34-1.13]. Males, living in urban slums and occupation with high risk of exposure to potentially
infected persons were associated with seropositivity. A cumulative 6,468,388 adult infections
(95% CI: 3,829,029-11,199,423) were estimated in India by the early May. The overall ICR was between
81.6 (95% CI: 48.3-141.4) and 130.1 (95% CI: 77.0-225.2) with May 11 and May 3, 2020 as plausible
reference points for reported cases. The IFR in the surveyed districts from high stratum, where death
reporting was more robust, was 11.72 (95% CI: 7.21-19.19) to 15.04 (9.26-24.62) per 10,000 adults, using
May 24 and June 1, 2020 as plausible reference points for reported deaths.
Interpretation & conclusions: Seroprevalence of SARS-CoV-2 was low among the adult population in
India around the beginning of May 2020. Further national and local serosurveys are recommended to
better inform the public health strategy for containment and mitigation of the epidemic in various parts
of the country.
Key words Antibody - COVID-19 - ELISA - IgG - India - SARS-CoV-2 - seroepidemiology - seroprevalence - serosurveillance
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to COVID-19. Facility-based surveillance eorts,
though useful to understand the trend of infection in
sentinel populations, are not population representative.
Population-based seroepidemiological studies are
therefore, recommended to measure the extent
of spread of infection in an area and recommend
containment measures accordingly3,4. The WHO has
recommended three types of seroepidemiological
studies: (i) cross-sectional surveys, most appropriate
after the peak transmission is established; (ii) repeated
cross-sectional investigation in the same geographic
area to establish trends in an evolving pandemic; and
(iii) longitudinal cohort study with serial sampling of
the same individuals5. For India, being in the early
stages of the pandemic at the time of study, the Indian
Council of Medical Research (ICMR) adopted the
option of repeated cross-sectional surveys. The results
of the rst cross-sectional serosurvey conducted with
the objectives of estimating the seroprevalence for
SARS-CoV-2 infection among the adults in the general
population and determining the socio-demographic
factors associated with SARS-CoV-2 infection in the
country are described here.
Material & Methods
The details of this national serosurvey procedure
are given elsewhere6. Briey, the survey to estimate
the seroprevalence of SARS-CoV-2 infection in the
general population was conducted among individuals
aged 18 yr or more in selected representative 736
districts in India. Districts were categorized into four
strata according to the incidence of reported COVID-19
cases per million population (zero, low=0.1-<5,
medium=5-10, high=>10) as on April 25, 2020. At
least 15 districts were randomly selected from each
stratum (Supplementary Table).
The ICMR Central Ethics Committee on Human
Research approved the survey protocol. Written
informed consent was obtained from the participants,
and the test results were communicated to them.
Sampling design and sample size: A multistage cluster
sampling design was used. A sample size of 5,929
(rounded to 6,000) was calculated per stratum of
districts to estimate one per cent seropositivity, with
40 per cent relative precision, 95 per cent condence
interval (CI) and design eect of 2·5. Four hundred
individuals were selected from each district. In each
district, 10 clusters (village in rural areas and ward in
urban areas) were selected by probability proportion to
population size. In each cluster, four random locations
were selected. A random starting point was selected
from each location and all contiguous households were
visited until 10 eligible individuals were enrolled. One
adult was selected from each household following the
Troldahl-Carter-Bryant Grid method7.
Survey procedure: The survey was conducted from
May 11 to June 4, 2020. The survey team visited the
selected households and briefed them about the survey
objectives and process involved. After obtaining
written informed consent, information on basic
demographic details, exposure history to laboratory-
conrmed COVID-19 cases and symptoms suggestive
of COVID-19 in the preceding one month was collected
using an Open Data Kit application (https://getodk.
org/). Trained phlebotomists collected 3-5 ml of venous
blood from each participant. Serum was separated after
centrifugation in a local health facility and transported
to the laboratories in the designated ICMR institutes
under cold chain.
Laboratory procedure: Serum samples were tested
for the presence of IgG antibodies against COVID-19
using commercial ELISA (COVID Kavach-Anti-
SARS-CoV-2 IgG Antibody Detection ELISA, M/s
Cadila Healthcare Limited, Ahmedabad). The assay
detects IgG antibodies in the serum/plasma, which
bind to the SARS-CoV-2 virus whole cell antigen.
The manufacturer reported no cross-reactivity with
other viruses in the serum from real-time RT-qPCR-
conrmed patients of inuenza A (H1N1) pdm09,
inuenza A (H3N2), human coronavirus OC43,
rhinovirus, respiratory syncytial virus, inuenza B,
parainuenza type 4, hepatitis B virus, hepatitis C virus,
as well as serum with IgG antibodies against dengue
and chikungunya. The sensitivity and specicity of the
assay were 92.4 and 97.9 per cent, respectively8.
Testing procedures were followed as per the
manufacturer’s instructions. For each plate, samples
with optical density (OD) value more than the cut-o
value and positive/negative (P/N) ratio more than 1.5
were considered as positive. Samples with OD value of
10 per cent ± ranges of the cut-o were considered to
be indeterminate. The P/N ratio was dened as the ratio
of average OD value of the positive control divided by
the average OD of the negative control. The cut-o
OD value was calculated as the average OD value of
negative control +0.2.
Serum samples with indeterminate results were
repeat tested with COVID Kavach ELISA. Those
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with indeterminate results on repeat testing also were
considered as negative. All serum samples showing
positive results with COVID Kavach ELISA were
serially tested with Euroimmun SARS-CoV-2 ELISA
(IgG) (Euroimmun AG, Germany). This kit uses S1
domain of the spike protein of SARS-CoV-2 expressed
recombinantly in the human cell line HEK 293 and
has a sensitivity and specicity of 93.8 and 99.6 per
cent, respectively, as per the kit insert9. Additional data
submitted for the registration to the U.S. Food and
Drug Administration (FDA) describe the specicity
of 100 per cent (95% CI: 95.4-100) in an independent
clinical validation study (n=80) and 99.5 per cent (95%
CI: 99.1-99.9) among pre-COVID banked adult serum
samples (n=1195)10. For quality assurance, one per cent
of negative serum samples were randomly selected
from each stratum and tested with COVID Kavach-
Anti-SARS-CoV-2 IgG Antibody Detection ELISA.
A positive infection was dened as an adult
whose serum sample was found to be positive upon
testing with Euroimmun ELISA subsequent to being
positive by COVID Kavach ELISA. It is assumed
that seropositive status indicates prior infection with
Data analysis: The frequency of characteristics of
the survey participants was described. The reported
occupations were categorized into high and low risk
considering the potential risk of exposure to known or
unknown COVID-19 case. The serial sensitivity and
specicity of our sequential testing were calculated
using the following formulae:
Serial sensitivity=sensitivity of Kavach×sensitivity of
Serial specicity=specicity of Kavach+(1−specicity of
Kavach)×specicity of Euroimmun.
The serial sensitivity and specicity calculated
using the sequential testing of positive results were
86.67 and 99.99 per cent, respectively, and were used
to adjust the seroprevalence11.
The seroprevalence of SARS-CoV-2 infection
along with the 95 per cent CI was estimated for each
of the four strata using appropriate sampling weights
and taking into account the sampling strategy used
for the survey. Sampling weights were calculated
as a product of inverse probabilities of selection of
districts in the stratum, selection of clusters in each
district and selection of individuals in each cluster.
The stratum seroprevalence and 95 per cent CI were
calculated using the survey data analysis module in
the STATA software (StataCorp LLC, TX, USA). The
nal prevalence estimates were adjusted for the serial
IgG test characteristics12,13. The estimates across the
strata were pooled to calculate the overall national
prevalence with 95 per cent CI14. The adjusted stratum-
specic seroprevalence was applied to the total adult
population in each stratum, projected for the year
2020 using 2011 census data (https://censusindia., to
estimate the number of infections in each stratum and
overall infections14.
Factors associated with IgG seropositivity: Individuals
who were seropositive for SARS-CoV-2 infection
were compared with those who were seronegative to
identify socio-demographic factors associated with
IgG positivity using logistic regression analysis. Odds
ratio (OR) with 95 per cent CIs were calculated with
the adjustment of each factor for its known confounders,
if any.
Estimated infection ratios (IFR): The published
literature indicates that the IgG antibodies against
SARS-CoV-2 infection start appearing by the end of
the rst week after symptom onset and most cases
are IgG positive by the end of second week15. We
therefore, considered the number of reported RT-
qPCR-conrmed COVID-19 cases by May 3 and 11,
2020 (respectively, 15 days and one week before the
initiation of serosurvey on May 18 in at least half of the
clusters) to estimate the plausible range of infections.
The infection to case ratio (ICR) was dened as the
number of individuals with SARS-CoV-2 infection
(as per the IgG detection) divided by the number of
RT-qPCR cases of COVID-19 reported by the date of
sample collection from the ICMR laboratory database.
Assuming a three-week lag time from infection to
death16, we considered the reported number of deaths
in the districts included in the serosurvey by May
24 and June 1, 2020 to estimate the plausible range
of the infection fatality ratio (IFR)17. The number of
infections was estimated only in the surveyed districts
for each stratum for calculating stratum-specic IFR.
A total of 30,283 households were visited from
700 clusters in 70 districts across the four strata
(Table I). About one-fourth (n=181, 25.9%) of the
surveyed clusters were from urban areas. A total
of 28,000 individuals consented to participate. The
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response rate in dierent strata ranged from 86.9 to
95.9 per cent. Nearly half (n=13,552, 48.5%) of the
survey participants were aged between 18 and 45 yr
and 51.5 per cent (n=14,390) were female. In all, 18.7
per cent of the participants had an occupation with a
high risk of exposure to potentially infected persons
(Table II).
Four hundred and eighty six individuals (1.7%)
reported a history of respiratory symptoms in the
preceding one month, of whom, 44.7 per cent (n=217)
sought medical care and 30.9 per cent (n=67) of those
who sought care were hospitalized. One hundred
and fty one (0.5%) individuals reported a history of
contact with a COVID-19 case and 70 (0.3%) reported
that they were tested for COVID-19 any time before
the survey. One person had been diagnosed positive
(Table II).
Of the 28,000 individuals initially tested by
COVID Kavach ELISA, 256 were classied as
positive and 69 as indeterminate. On repeat testing of
Table II. Characteristics of study participants in dierent strata of districts
Characteristics Stratum*Overall
Zero (n=6,014) Low (n=8,822) Medium (n=6,380) High (n=6,784)
Age (yr)
18-45 3,234 (53.8) 4,302 (48.9) 2,611 (41.1) 3,405 (50.3) 13,552 (48.5)
45-60 1,844 (30.7) 3,031 (34.4) 2,310 (36.4) 2,340 (34.6) 9,525 (34.1)
>60 930 (15.5) 1,468 (16.7) 1,431 (22.5) 1,019 (15.1) 4,848 (17.4)
Missing data 6 21 28 20 75
Mean age±SD 43.4±15.4 45.1±15.0 48.3±15.2 44.6±14.8 45.3±15.2
Male 2,964 (49.3) 4,300 (48.9) 3,209 (50.5) 3,041 (44.9) 13,514 (48.4)
Female 3,037 (50.6) 4,493 (51.0) 3,140 (49.4) 3,720 (55.0) 14,390 (51.5)
Others 7 (0.1) 9 (0.1) 5 (0.1) 6 (0.1) 27 (0.1)
Missing data 6 20 26 17 69
Occupation with high exposure 1,186 (19.7) 1,501 (17.1) 1,142 (18.0) 1,397 (20.7) 5,226 (18.7)
History of respiratory symptoms in
last 30 days
131 (2.2) 156 (1.8) 109 (1.7) 89 (1.3) 486 (1.7)
Sought medical care for respiratory
70 (53.0) 68 (43.6) 51 (46.8) 28 (31.5) 217 (44.7)
History of hospitalization 24 (34.3) 24 (35.3) 15 (29.4) 4 (14.3) 67 (30.9)
History of contact with COVID-19 case 88 (1.5) 46 (0.5) 6 (0.1) 11 (0.2) 151 (0.5)
Ever tested for COVID-19 by RT-qPCR 6 (0.1) 11 (0.1) 16 (0.3) 37 (0.5) 70 (0.3)
Values given as n (%) except otherwise stated. *Based on incidence of reported COVID-19 cases as per the ICMR laboratory database.
RT-qPCR, real-time reverse transcription polymerase chain reaction
Table I. Districts and number of individuals surveyed
Stratum Total
number of
Number of
of clusters
Number of
clusters in
urban area (%)
Number of
Number of
enrolled (%)
Zero cases*233 15 150 17 (11.3) 6301 6014 (95.4)
Low*229 22 220 45 (20.5) 9202 8822 (95.9)
Medium*84 16 160 49 (30.6) 7340 6380 (86.9)
High*190 17 170 70 (41.2) 7440 6784 (91.2)
Total 736 70 700 181 (25.9) 30,283 28,000 (92.5)
*Based on the incidence of reported COVID-19 cases in the ICMR laboratory database as on April 25, 2020. Low=0.1-<5, medium=5-10,
high=>10 per million
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the indeterminate serum samples by COVID Kavach
ELISA, 34 turned positive. Finally, 157 of these 290
were detected positive using the Euroimmun ELISA.
The overall unweighted seroprevalence was 0.56
per cent (95% CI: 0.48-0.66%). The unweighted
prevalence of IgG antibodies against SARS-CoV-2 was
0.47 per cent (95% CI: 0.31-0.67%) in the stratum with
zero reported COVID-19 cases, 0.48 per cent (95%
CI: 0.34-0.64%) in the stratum with low incidence,
0.74 per cent (95% CI: 0.54-0.98%) in the stratum
with medium incidence and 0.59 per cent (95% CI:
0.42-0.80%) in the stratum with high incidence. The
weighted prevalence of infection after adjusting for
the serial sensitivity and specicity of the two ELISA
tests in the respective strata was 0.68 per cent (95% CI:
0.42-1.11%), 0.62 per cent (95% CI: 0.43-0.89%), 1.03
per cent (95% CI: 0.44-2.37%) and 0.72 per cent (95%
CI: 0.44-1.17%). The pooled adjusted prevalence of
SARS-CoV-2 infection was 0.73 per cent (0.34-1.13%)
at the national level (Table III). The post facto design
eect was 1.9.
Factors associated with IgG positivity: As compared
to the seronegative individuals, the individuals positive
for IgG antibodies were more likely to be male (OR:
1.47; 95% CI: 1.07-2.02), have an occupation with a
higher risk of exposure to potentially infected persons
(adjusted OR: 1.39; 95% CI: 0.96-2.02) and reside in
urban slums (OR: 1.90; 95% CI: 1.23-2.94) (Table IV).
Burden of SARS-CoV-2 infection: Applying the
stratum-specic adjusted prevalence of IgG antibodies
to the total population of adults in 2020, we estimated a
cumulative 6.46 million (3.82-11.1 million) infections
in India by May 3, 2020 (Table V). The infection to
case ratio was 81.6 (95% CI: 48.3-141.4) up to May
11 and 130.1 (95% CI: 77.0-225.2) up to May 3, 2020
considering a total of 79,230 and 49,720 COVID-19
cases reported in India by the respective dates. The IFR
per 10,000 infections on May 24 ranged between 0.18
(95% CI: 0.11-0.29) in zero stratum and 11.72 (95%
CI: 7.21-19.19) in the high stratum districts. IFR per
10,000 infections as on June 1 ranged between 0.27
(95% CI: 0.17-0.44) in zero stratum and 15.04 (95%
CI: 9.26-24.62) in the high stratum districts (Table V).
The ndings of the rst national population-based
serosurvey indicated that 0.73 per cent of adults in India
were exposed to SARS-CoV-2 infection, amounting to
6.4 million infections in total by the early May 2020.
The seroprevalence ranged between 0.62 and 1.03
per cent across the four strata of districts.
Population-based estimates of seroprevalence
provide information about the state of the epidemic
in the country. A dashboard of seroepidemiological
data available from 22 countries estimated the
pooled seroprevalence to be 4.76 per cent, ranging
from 0.65 Zero cent in Scotland to 26.6 per cent
in Iran18. These surveys used dierent types of
serologic tests including lateral ow immunoassay
using capillary blood (rapid test), ELISA,
Luciferase immunoprecipitation system assay,
immunochromatography and chemiluminescence18,19.
The ndings of our survey indicated that the overall
seroprevalence in India was low, with less than one per
cent of the adult population exposed to SARS-CoV-2
by mid May 2020. The low prevalence observed in
most districts indicates that India is in early phase of
the epidemic and the majority of the Indian population
is still susceptible to SARS-CoV-2 infection. It is,
therefore, necessary to continue to implement the
context-specic containment measures including the
testing of all symptomatics, isolating positive cases
and tracing high risk contacts to slow transmission and
to prevent the overburdening of the health system20.
Table III. Seroprevalence of IgG antibodies against SARS-CoV-2 infection in dierent strata of districts
Incidence of
reported COVID-19
cases (stratum)
Number of
Per cent (95% CI)
Zero 6,014 28 0.47 (0.31-0.67) 0.60 (0.37-0.97) 0.68 (0.42-1.11)
Low 8,822 42 0.48 (0.34-0.64) 0.55 (0.38-0.78) 0.62 (0.43-0.89)
Medium 6,380 47 0.74 (0.54-0.98) 0.90 (0.39-2.06) 1.03 (0.44-2.37)
High 6,784 40 0.59 (0.42-0.80) 0.63 (0.39-1.02) 0.72 (0.44-1.17)
Overall 28,000 157 0.56 (0.48-0.66) 0.64 (0.30-0.99) 0.73 (0.34-1.13)
After applying *sampling weights and clustering; **adjusting for test performance. CI, condence interval
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As per the present survey ndings, the prevalence
of infection in the general population was not dierent
across dierent strata of districts categorized on the
basis of the level of PCR-based case reporting. The
level of seropositivity to SARS-CoV-2 detected in the
stratum of districts with zero cases could be on account
of two reasons. First, the stratication of districts was
done based on the reported number of COVID-19 cases
as on April 25, 2020. The serosurvey in the 15 districts
of these strata was conducted during May 11 to June 4,
2020 after a median interval of 23 days (range: 16-40).
During this period, as per the ICMR laboratory
database, three districts had reported COVID-19 cases
at least two weeks before the initiation of survey and
thus were no longer reporting zero cases. Second,
there could be under-detection of COVID-19 cases
in the zero stratum districts on account of low testing
as well as poor access to the testing laboratories. In
four of the 15 districts in this stratum, COVID-19
testing laboratory was not available at the district
headquarters and the samples were transported to the
State headquarter hospitals for diagnosis. The present
ndings of seropositivity in the strata of districts with
zero to low incidence of COVID-19 cases underscores
the need to strengthen surveillance and augment the
testing of suspected cases in these areas.
The estimated seroprevalence is a function of the
sensitivity and specicity of serological tests. Adequate
thresholds for sensitivity and specicity are inuenced
by the prevalence of infection. As was done in our
study, the use of two tests in a sequential manner under
the condition of positive result on both the tests would
Table IV. Socio-demographic risk factors associated with IgG positivity
Socio-demographic characteristics IgG
IgG negative Crude odds
ratio (95% CI)
Adjusted odds
ratio (95% CI)
Age (yr) (n=157) (n=27,768)
18-45 68 (43.3) 13,484 (48.6) 1.00
46-60 62 (39.5) 9,463 (34.1) 1.30 (0.92-1.84)
>60 27 (17.2) 4,821 (17.3) 1.11 (0.71-1.74)
Sex (n=157) (n=27,774)
Male 91 (58.0) 13,423 (48.3) 1.47 (1.07-2.02)
Female 66 (42.0) 14,324 (51.6) 1.00
Others - 27 (0.1) -
Area of residence (n=157) (n=27,843)
Urban slum 25 (15.9) 2,496 (9.0) 1.90 (1.23-2.94)
Urban non-slum 23 (14.6) 4,694 (16.9) 0.93 (0.59-1.46)
Rural (village) 109 (69.4) 20,653 (74.1) 1.00
Occupation with higher risk of exposure
to potentially infected persons
(n=155) (n=27,668)
Yes 41 (26.5) 5,185 (18.7) 1.56 (1.09-2.23) 1.39 (0.96-2.02)*
No 114 (73.5) 22,483 (81.3) 1.00
Values shown as n (%). *Adjusted for age, sex, area of residence
Table V. Estimated number of infections and infection fatality ratio (IFR) by strata of districts
Estimated number of infections
in all districts (95% CI)
infections in
surveyed districts
(May 24,
(June 1,
IFR (per 10,000 infections) 95% CI
May 24, 2020 June 1, 2020
Zero 856,062 (528,744-1,397,395) 109,872 2 3 0.18 (0.11-0.29) 0.27 (0.17-0.44)
Low 1,817,118 (1,260,259-2,608,443) 212,885 15 22 0.70 (0.49-1.02) 1.03 (0.72-1.49)
Medium 1,518,367 (648,623-3,493,718) 391,941 54 97 1.38 (0.60-3.23) 2.47 (1.08-5.79)
High 2,276,841 (1,391,403-3,699,866) 289,143 339 435 11.72 (7.21-19.19) 15.04 (9.26-24.62)
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lead to an overall increase in the specicity at the cost
of lowering of sensitivity11,13. The sequential use of
COVID Kavach and Euroimmun ELISA allowed us
to potentially reduce the false positive to as low as
0.01 per cent by obtaining a serial specicity of 99.99
per cent (if the independence between the tests is high).
However, the serial sensitivity was reduced to 86.67
per cent that resulted in a slight increase in the false
negatives, resulting in a potential underestimation
of seroprevalence. Testing with greater specicity is
preferred in a low prevalence setting such as ours to
minimize the large number of false positives21.
Serosurveys provide important estimates of the
total number of infections in the country. Based on the
overall adjusted seroprevalence of 0.73 per cent and
reported number of COVID-19 cases, it was estimated
that for every RT-qPCR conrmed case of COVID-19,
there were 82-130 infections in India. The high
infection to case ratio in India could be on account of
the prioritization of testing among symptomatics or the
variability in testing rates across the States22. The IFR
reects the societal cost of achieving SARS-CoV-2
herd immunity through infection. Calculation of IFR
is dependent on an accurate reporting of deaths and the
number of estimated infections. Considering that the
death reporting in India is incomplete, and dierences
in access to testing facilities across districts necessary
for declaring the COVID-19 conrmed deaths, the
present IFR is likely an underestimate. While the
overall IFR based on the serosurvey ndings was much
lower than that reported from Santa Clara County,
USA (0.12-0.2%)16, Iran (0.08-0.12%)23, Brazil and
Spain (1%)24, the IFR from the high-stratum districts,
where reporting is assumed to be more complete, was
similar to those reported above. In addition to the
completeness of death reporting, the heterogeneity
in IFR can also be explained by the dierences in
age structure of the population, access to healthcare
facilities, quality of care and variation in the prevalence
of comorbidities24,25.
The present serosurvey had certain limitations.
First, the seroprevalence estimates had wide
condence intervals across all the strata of districts.
The sample size was calculated assuming a minimum
seroprevalence of one per cent across all strata. Our
sample size was underpowered to precisely estimate
the lower prevalence observed in the strata of districts
with low incidence of reported COVID-19 cases.
However, our sample size was adequate to estimate
the seroprevalence in other strata. The estimate of
infection to case ratio also had low precision as a
result. These baseline results will help improve sample
size estimations in future rounds of serosurveys.
Second, the study participants were interviewed to
collect information about history of the symptoms for
the preceding month. However, as the presence of IgG
antibodies reects exposure to SARS-CoV-2 since the
beginning of the epidemic, we were not able to estimate
how many seropositive individuals ever had probably
COVID-19 symptoms. Due to only a few observations,
it was not possible to associate prior RT-qPCR testing,
hospitalization or contact status with the seropositivity.
Third, errors in serological testing, especially due to
the test specicity, can aect prevalence estimates,
particularly when the prevalence is low. We sought to
improve the test specicity by conrming positives
detected in the general population using a separate test
with a dierent antigen. However, both ELISAs use
the same mechanism, serology, and the Euroimmun
ELISA antigen, which is solely a recombinant domain
of the primary immunogenic component of the virus, is
a subset of the whole virion, which is used by Kavach
ELISA. Thus, positive test results will be conditionally
dependent between the two. The degree of dependence
is unknown, but this assumption creates an upward
bias in our prevalence estimate. The seroprevalence of
0.73 per cent was estimated assuming that the two tests
are completely independent. The seroprevalence could
be as low as 0.26 per cent (considering sensitivity of
COVID Kavach and specicity of Euroimmun ELISA)
assuming that the two tests are completely dependent.
However, as the dependence between the two ELISAs
is unlikely to be complete, serial testing would improve
the serial specicity to some degree. In the worst case
scenario of complete dependence between the two
tests, the conclusions of the study that in the beginning
of May 2020, there was limited spread of SARS-CoV-2
infection across India, remained the same. Fourth,
with emerging data about the highly clustered nature
of SARS-CoV-2 transmission, our estimates could
be biased. By selecting only a single individual per
household, we may be underestimating the prevalence
as transmission would be expected to be higher within
the household. We may also underestimate prevalence
if our selection missed clusters with higher prevalence
including those among most of the metropolitan cities.
Only Chennai and Bengaluru were included in the
serosurvey on account of the random selection process.
In conclusion, the ndings of the serosurvey
indicated a low prevalence of SARS-CoV-2 infection
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in the general population in India in early May 2020. As
most of the population remains susceptible to infection,
our public health strategy needs to plan for an inevitable
increase in transmission. Repetition of the population-
based serosurvey can better inform changes in the
extent and speed of transmission and help evaluate the
potential impact of containment strategies over time in
dierent parts of the country. Seroprevalence estimates
conducted later in the epidemic, or in the settings with
higher prevalence, will provide more robust infection
to case and infection to fatality ratios. It is further
recommended to establish the district-level facility-
based sentinel serosurveillance to systematically
monitor the trend of infection in the long term to inform
local decision-making at the lowest administrative
unit of public health response towards the COVID-19
epidemic in the country.
Acknowledgment: Authors acknowledge the eld supervision
and support provided by the WHO-India, Ministry of Health and
Family Welfare, Government of India, State and District Health
ocials, and Primary Healthcare sta in planning and conduct of
the serosurvey.
Financial support & sponsorship: Financial support
provided by the Indian Council of Medical Research, New Delhi,
is duly acknowledged.
Conicts of Interest: None.
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India COVID-19 Serosurveillance Group
Andhra Pradesh Team: Shanta A., Dheeraj Tumu, U. Venkateswarlu, Bharath J., Seetahal Varma, Vijaya Kumari, G.G.J.N. Lakshmi,
Venkata Prasad, K. Naga Bhushanam, K. Ramu, K. Sindhuja, M. Prasad Rao, R.J.K. Hemanth, S.N. Prasad, K. Appalanaidu,
V.V. Savitrammadevi, T. Govindarao, Govind, B. Avinash, A. Sandeep, A. Kiran Kumar, G. Vijaya, B. Ramesh, Banka Jagadeesh,
G. Appalanaidu, C. Narayanamma, S. Suguna, V.A. Appalacharulu, G. Tamada, Ramesh, K. Syam Sundar Rao, M. Kranthi Kumar,
Priyanka, J. Ranikosala, M. Siva Prasad, Prasad, V. Naga Lalitha, P. Siva, Rajkumar, Thanuja, Prasad, V. Karuna Sri, N. Srinivasa Rao,
P. Jeevan, Jayalaksmi, Keerthi Battu, Prasad, Ch Vijayalakshmi, Jayalaksmi, Susila, R. Vijayalakshmi, G. Leela Sri, Sudharani, Vamsi,
Manjushalini, Sha, Suvarna, S. Sazeera Banu, Vijaya Sekhar, Vasanthi, Padma Narendra, Seenaiah, S. Sazeera Banu, Vijaya Sekhar,
Suresh, Giri, J. Ramesh, D. Ravikumar, M. Rajasekhar, G. Vijay Kumar, A. Sreekanth, B. Vinay, Ch Pallivi, Sarath V., Srihari R. Kalyan,
U. Sabarinath
Assam Team: Gautam Borgohain, Mohib Chandra Dekaraja, Mridul Bharati Nath, Ankumoni Saikia, Arup Deka, Bhaskar Das, Nabanita
Sharmah, Pratul Sharmah, Ekparana Hazarika, Dhurba Baruah, Madhurjya Changkakoti, Saraswati Kaushik, Syedu Semin Islam, Nazia
Mehzabin, Nagen Sarma, Ganesh Deori, Nakul Shyam, Jirjar Phura, Clif Pator, Biren Chandra Tumung, Joydhon Timung, Bhaskarjyoti
Bharali, Dondo Gogoi, Montu Shyam, Thaneswar Teron, Jimmy Paul Kerkatta, Bhabajyoti Borah, Dhurbajyoti Pathak, Gokul Brahma,
Utpal Sarmah, Pankaj Kumar Das, Lekhraaj Gautam Chetry, Jamanjyoti Sarmah, Anjan Hazarika, Moniruth Zaman, Liladhar Brahma,
Raju Brahma, Beauty Boro, Premtush Muru, Anju Rabha Hazarika, Malika Bhuyan, Monika Hazarika, Bikash Bora, Madhuchand
Rabha, Tamanna Choudhury, Seema Das, Amal Kalita, Abanti Das, Sanjib Saikia, Jyotish Kalita, Sahjahan Ali, Kishor Kumar Sharma,
Chandi Keot, Debojit Deka, Nilima Hembrom, Nandeswar Hazarika, Abani Barman Birwajit, Kumar Bora, Sanjeev Engti Kathal, Bhaiti
Engti, Parikhit Gogoi, Lek Chetry, Jyotish kalita, Subroto Bhattarjee, Bhagya Deka, Arup Das, Kalpana Devi, Kasturi Dutta, Fredrick
Daimari, Netra Kamal, Chakrabarty, Rranaki Puma, Pranjal Bora, Bipul Mech, Pranab Kumar Sarmah, Nebedita Bharali, Sanjib Kumar
Rajguru, Rutheshvardhan Burru, Agastin Kerketa, Amir Sohail Khan, Amlan Jyoti Bora, Bibek Saikia, Bidya Pegu, Boidujya Rai Gogoi,
Chandrasmita Sarmah, Daisy Gogoi Thapa, Gamuk kutum, Gunin Mili, Jitumoni Saikia, Krishna Kadka, Mirnal Ngatey, Moon Saikia,
Munna Sarkar, Pankaj Phukan, Parmanada Upadhyay, Prahlad Das, Pranjal Das, Rimpi Konwar, Rituraj Borgohain, Ritwik Dutta, Ronjali
Doley, Santanu Kakoty, Tapan Kalita, Tikendra Gogoi, Tikendrajit Das, Jiban Saikia, Pullab Das, Mahanta Gogoi
Bihar Team: B.P. Subramanya, Gitika Shankar, Dileep Kumar, Anand Gautam, Susheel Gautam, Adarsh Varghese, Kunnal Kuvalekar,
Naveen Mandal, Kumar Gautam, Sanjeev Gupta, Ujjwal Prakash, Sahdeo Mandal, Kumar Ayush, Saurav Kumar, Santosh Kumar, Rahul
Kumar, Ranjeet Kumar, Paras Kumar, Kumar Mandal, Satish Thakur, Amit Kumar, Amit Lakra, Ashish Kumar, Binod Kumar, Amit
Ranjan, Prateek Raushan, Vikash Kumar, Mumtaz Alam, Amrendra Kumar, Baijnath Rai, Alok Kumar, Sudarshan Kumar, Sakaldeep
Kumar, Ajit Kumar, Aaditya Panday, Umesh Kumar, Dhirendra Kumar, Abhay Kumar, Sanjeet Kumar, Bhoop Dhakar, Kamlesh Kumar,
Alupt Kumar, Vikash Kumar, Kundan Kunal, Vikash Roy, Sushil Kumar, Vivek Kumar
Chhattisgarh Team: Gaurav Parihar, Manish, P. Vijay, Rochak Saxena, Varun, Prashant, Shitij Khapade, Pranit, Anshuman Chaudhury,
Archana Nagwanshi, Sunil Kumar Pankaj, Irshad Khan, Rahul Roy, Nand Kumar Sahu, Gulshan Sahu, Kunj Bihari Patel, Devadas Joshi,
Nand Kumar Modi, Pekhan Kumar Sahu, Rajesh Kumar Soni, Dev Kumar Sahu, Vivek Jaiswal, Hemant Kumar Bawanthade, Mahesh
Gopal Patel, Bhoopendra Kumar, Avadh Ram Baghel, Champuram Ratre, Vokesh Kumar Yadu
Gujarat Team: A.M. Kadri, Harsh N. Bakshi, Pranav G. Patel, R.S. Kashyap, Arthur Mcwan, Anand Santoke, Monark Vyas, Pankaj
Nimavat, S.A. Aarya, Chirag Ramesh Chandra Modi, Praveen L. Asari, Pankaj Nimawat, Shabbir Ali Dedhrotiya, Yagvalky Jani, Aniket
Rana, Jitendra Patel, Swapnil B. Shah, Hasmukh Vaghsinh Parmar, Arthur Mcwan, Hardik Nakshiwala, Vaidehi Gohil, Jagdish Patel,
virus infection in Guilan province, Iran. medRxiv 2020.
24. Mallapaty S. How deadly is the coronavirus? Scientists are
close to an answer. Nature 2020; 582 : 467-8.
25. Perez-Saez J, Lauer SA, Kaiser L, Regard S, Delaporte E,
Guessous I, et al. Serology-informed estimates of SARS-
CoV-2 infection fatality risk in Geneva, Switzerland. Lancet
Infect Dis 2020; S1473-3099(20)30584-3.
For correspondence: Dr Manoj V. Murhekar, ICMR-National Institute of Epidemiology, Ayapakkam, Chennai 600 077, Tamil Nadu, India
[Downloaded free from on Monday, September 14, 2020, IP:]
H.V. Parmar, Parulben Patel, Jigneshbhai Tadvi, Piyushbhai Parasar, Vinodbhai Valvi, Jagdishbhai Padvi, Hardik Gavit, Krishnaben
Joshi, Hasmukhbhai Variya, Chiragbhai Bhil, Dharmendra Rathva, Dhawal Patel, Divyaben Zala, Jigneshbhai Patel, Mayurbhai Vasava,
Manmitbhai Solanki, Darshnaben Patel, Chetnaben Chaudhari, Aartiben Rathva, Riyaben Mistry, Nikiben Bhau, Jyotsnaben Bariya,
Tejasbhai Patel, Krunalbhai Darji, Kartikbhai Prajapati, Rahulbhai Rohit, Kum Babita Roy, Pareshbhai M. Parmar, Virendrasinh V. Zala,
Manojbhai Balabhai Bhagora, Brijeshbhai Rameshbhai Sutariya, Pareshbhai V. Patel, Hemantbhai D. Kalasva, Jigneshbhai B. Patel,
Yashvantbhai N. Nayak, Hiteshbhai B. Patel, Pragneshkumar R. Modi, Sonaben Lakhiyabhai Bumbadiya, Rekhaben Kantibhai Parmar,
Kokilaben J. Parmar, Nitaben Dharmendrabhai Patel, Varshaben Patel, Nitaben Bharatbhai Prajapati, Shardaben Jayantibhai Vankar,
Chetnaben Bharatbhai Bhatiya, Jagrutiben Jitendrasinh Chauhan, Alkaben Maheshbhai Chamar, Divya Chhaganbhai Patel, Ravisinh
Chauhan, Nimisha Patel, Yash Lalitbhai Shah, Misha Patel, Parul Pankajbhai Parmar, Harsha Sadat, Puja Patel, Girish Maneklal Shah,
Partapsinh Chaturbhai Taviyad, Vasudev Javalabhai Paragi, Raginiben Jivangir Gosai, Krutikaben Anilbhai Rana, Imtiyazbhai Rasulbhai
Shaikh, Madhuben Khushalbhai Mahera, Bhavikaben Surendrabhai Patel, Rajendrabhai Babubhai Patel, Prakashakumar Vadilal Patel,
Sangitaben Somabhai Patel, Geetaben Motibhai Patel, Hemprabha Gumansinh Baria, Pratapbhai Bhikhabhai Pagi, Bharatbhai Punambhai
Rana, Jinalben Harshadbhai Patel, Archanaben Parsing Pandavi, Dilipbhai Jivsinh Baria, IshavarSinh Jasvantsinh Rathod, Sharmishtha
Somabhai Patel, Sunitaben Kasiram Solanki
Jammu and Kashmir Team: Tasnim Syed, Haseena Mir, Shazia Khan, Sahila Nabi, Nazia Khaki, Iqra Nisar, Tanzeela Bashir Qazi,
Shahroz Nabi, Misbah Ferooz Kawoosa, Iram Sabah, Abdul Aziz Lone, IshtiyaQ Sumji, Afnan Showkat, Mudassira, Arif, Arsalaan, Asif,
Sheema, Javaid, Suraiyya, Humaira, Jahangeer, Muhammad Akram, Sameena, Syed Bilal, Feroz Ahmad, Tufail Ahmad, Mushtaq Ahmad,
Abdul Rashid, Farooq Ahmad
Jharkhand Team: Rajeev Ranjan Pathak, Amarendra Kumar, Anoob Razak, Valema Deogam, Mrityunjay, Shekhawat Hussain, Pramod
Kumar, Sunil Kumar Singh, Tarun Joshlakra, Ashok Kumar, Sobhna Toppo, Sharan, Buka Oroan, Kamlesh Oroan, Suraj Mahto, Ajay
Kerketta, Anushil Anand, Viresh Kumar Mishra, Abdul Kalam, Azad Raushan Raj, Aman Gupta, Puja Kachhap, Jyoti Anant, Alok Kumar,
Soni Khatun, Mukesh Kumar Agrawal, Pratima Kumari, Vikash Kumar Sinha, Mamta Kachhap, Prakash K., Jayram Mehta, Swagata
Lakshmi Tarafdar, Sudhanshu Munda, Nilesh Kumar
Karnataka Team: Kiran K., Sarika Jain, Kumar M.V., H.P. Arundathi Das, Ranganath R., Vivekanand Reddy, Nischit K.R., Hamsaveni
G., Swathi S. Aithal, Hemanth Kumar N.K., S. Shantharaju, N. Vijayalakshmi, S. Somashekharayya, Hiremath, Charanraj Rao, Ravi
Kumar M.T., Neelkanthayyaswamy I. Hiremath, Srikantha Y.G., Hariprashanth R., Prasanna M.N., Lal Kumar R., Bhuvaneshwari R.,
Ragapriya R., Dineshkumar B., Praveen B. Pujar, Ullera Ashoka, Sunil A.N., Umar Farooq M. Dalwai, Narasimharaju N., Bheema Zakeer
Hussain, Lakshmikanth Sankara, Laxmikant Shrimant Dhanshetti, Santhosh M.S.
Kerala Team: Rakesh P.S., Srinath Ramamurthy, Vinod Kumar V.G., Suja Aloysius, Anitha A.K., Sharath G. Rao, Nikilesh Menon
Ravikumar, Arun Raj, Akhila Pradeep, Abhirami M.R., Shilna A., Nikhilamol T., Anumol Raju S., Asitha A.S., Manoj M., Sarath T.S.,
Sindhya R., Jaicy G., Neethu Sugathan, Sumi K., Peneena Varghese, Banupriya K., Anupranam M.P., Prakash Jaison V., Vishnu Raj B.S.,
Venoth V.S.
Madhya Pradesh Team: Praveen K. Bharti, Pushpendra Singh, Suyash Shrivastav, Anil Kumar Verma, R.K. Saxena, Shivendra Mishra, Mahavir
Khandelwal, Sunita Parmar, Seema Jaiswal, Praveen Jadhiya, Bal Krishan Tiwari, Jitendra Kumar, Priyanka Singore, Santosh Kumar Patkar,
Monu Sen, Rekha Prajapati, Lipi Jain, Hemant Singh Thakur, Priyanka Birha, Pratipal Singh, Vikram Bathri, Sanjay, Prahalad Kumar Soni,
Ashish Patel, Ashok Solanki, Jyothi Ahirwar, Ashok Kumar Gupta, Geeta Devi, Manjeeta, Shashikant Tiwari, Hemant Pancheshwar, Mahendra
Kumar Jain, Ganesh Damor, Ramswaroop Uikey, Akansha Kushram, Vivek Patel, Bhagwansingh Patil, Shashibhushan Dubey, Yogendra Morya,
Shashank Kesharwani, Himmat Singh Kewat, Pushpendra Singh Rajput, Surendra Kumar Jhariya, Sandip Sharma, Hari Burman, Amirullah
Khan, Shorabh Bhadoriya, Ramesh Prajapati, Sheetal Sariyam
Maharashtra Team: Padmaja Jogewar, Archana Patil, Anupkumar Yadav, Rushikesh Rajkumar Andhalkar, Salil Patil, Shahanara Valwalkar,
Swati Salunkhe, Seema Nair, Deepthy Benoy, Asmita Kadhe, Rushikesh Mane, Sandip Bharaswadkar, Sandip Shinde, Rahul Dwiwedi,
Pradip N. Murambikar, Sandip Sangale, Sunil D. Pote, Chetan Khade, Dhakane, Nagoji S. Chavan, Dilip Potule, Prakash Nandapurkar,
Rahul Rekhawar, Radhakishan Pawar, Ashok Thorat, Sanjay Suryavanshi, Balaji Shinde, Nilkqnth Bhosikar, Vipin Itankar, Amol Gaikwad,
Shivshakti Pawar, Dhupal Girigosavi, Sanjay Salunkhe, Satish Ghatage, Abhijit Choudhary, Abhijit Raut, Sujata Joshi, Madhav Thakur,
Deepak Mungalikar, Balasaheb Nagargoje, Shankarao Deshmukh, Mujib Sayyad, Hema Ramesh Vishwakarma, Nyutan Rajkumar Wankar,
Rahul Bapurao Arke, Pritam Balasaheb Marodkar, Inayatulla Mahamad Husen, Archana Ganpat Gaikwad, Pramod Ananat Jamale, Namrata
Hajari, Aditya Ashok Bengle, Jagdeep Pralahd Bansode, Sumeta Nilkanth Dhete, Akshay Ramesh Phulari, Sunil Balkrishna Shirke,
Aakansha Chaudhari, Vivek Uttamrao Yengade, Padmakar Gurunath Kendre, Suraj Shivaji Rakhunde, Bhagwan Munjabhav Harkal, Avinash
Madhukar Shinde, Amit Pralhadrao Patil, Prathamesh Shivaji Chavan, Ajit Balu Buchude, Anil Vyankat Rathod, Dhiraj Prakash Panpatil,
Tejas Hetendrakumar Phule, R.N. Ahire, Sahane A., S. Dange, D.S. Motkar, B.N. Sanap, Vilas Latpate, A.B. Labade, A.N. Pathan, Ranjit
Bhor, Dnyandev Sangale, D.B. Aher, Ganesh Gunjal, Pankaj Deshmukh, Chaudhatri J., Shahane, B.S. Darade, J.K. Phate, Pallavi More,
Kalpesh Patil, Satish Mahajan, Wasim Haidar Shaikh, Harish Patil, Gajanan Gadri, Pooja Mahajan, Sunil Patil, Nana Borse, Satish Mahajan,
Jayant Nehate, Bhagyashri Kambale, Gautam Gayakwad, Samir Somkul, Dipak Pohekar, Sunil Mahajan, Jivandas Meshram, Rajendra
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Deshmukh, Baban Zagade, Vitthal Sanap, Amol Patki, Sachin Jadhav, S. Deshpande, R.S. Jadhav, Shivaji Rathod, Santosh Hajare, Pawal
Y.J., Ashish Waghamare, Mohan M. Galande, Pund, Sanap, Jadhav Gundprasad, Jyotiba Kale, Nagargoje, Kranti Gholve, L.D. Kulkarni,
Priyanka Chavhan, Choudhari, K.B. Kachve, Kalidas Walivadikar, Varsha Kale, Omprakash Navghare, Shamshul Hudda, Jyoti Kale, Atul
Kulkarni, Pornima Bhise, S.N. Sayyad, S.D. Bansode, Ritesh Chavan, Amol Patil, Nitin Desai, Patel, Kalyani Wadkar, Manjusa Dhage, Rani
Ballal, Jyoshna Patil, V.V. Kulkarni, Sachine Chavan, Ashwini Patil, Mahesh Shinge, Laxman Gejage, Asharani Anuse, Akshay Mali, Ankita
Javalekar, Kumare, Kadtan, Bharat Bagal, Shrimati Ramdhane, Sudhir Kandharkar, Deshmane, S.B. Gaikwad, Asohk Waddewar, Sudarshan
Admankar, V.R. Methekar
Odisha Team: Nutan Dwibedi, Spandan Bhanjadeo, Sushree Sukanya Samantray, Sagarkanta Pradhan, Sadruddin Khan, Kahnu Charan
Sahoo, Satyabrata Rout, Dinabandhu Padhan, Subrat Kumar Nayak, Janaki Biswal, Manas Bhoi, Jeevan Kumar Mohanta, Rojalin Das,
Nirupama Sahoo, Ashish Kumar Mohapatra
Punjab/Haryana Team: Alok Kumar, Priyanka Agarwal, Srinivasan Selvamani, Ashrafjit Chahal, Kamal Paul Rakesh Sarpal, Ramesh
Kumar, Sudesh Sahota, Harpreet Bains, Suchitra, Gaurav Kumar, Pankaj Sharma, Wilson Masih, Gurmeet Singh, Gurpreet Singh, Karanvir
Ghosal, Davinderdeep Kaur, Jyoti, Harwinder Singh, Manpreet Kaur, Pardeep Kaur, Kalpna
Rajasthan Team: Suman Sundar Mohanty, Suresh Yadav, Ramesh Kumar Sangwan, Vikas Dhikav, Ramesh Kumar Huda, Elantamilan
D., Mahendra Thakor, Rakesh Vishwakarma, Azmat Khan, Mohd Arif Baig, Anirudh Tiwari, Rajneesh Kumar, Trilok Kumar, Balwant
Manda, G.S. Deval, Pooranlal Meena, J.P. Bunkar, Narrottam Sharma, R.S. Bharti, Satish Kumar Mishra, Bheron Singh Jatav, Sharwan
Kumar,Sadav Khan,Mohan Meena, Praveen Baghel, Krishan Kumar,Javed Khan, Krishan Kumar, Rana Ram, Raghunath, Manohar Singh,
Pardeep Singh Jodha
Tamil Nadu Team: Nivethitha K., Ezhilarasan, Shreejaa Varrier, Aby Robinson, Joe Daniel, Bharani Anbalagan, Banuchandar
K., Arvindh, Kameswaran D., Kirankumar, Gowtham Raj M., Vigneshwar, Aravindan, Sudha, Sowmiya, Umeshkrishna, Elango,
Dheepalakshmi, Prakash, Arunkumar, Manikanda Prabu, Suresh, Naveen, Saravanan, Raghavan, John Arokyadoss Y., M. Magesh Kumar,
M. Karthikesan, P. Kumaravel, Vasudevan, Anbarasan, Ramesh Kumar, A. Gomathy, R. Vijaya Prabha, I. Kalaimani, P. Lortu Stella Mary,
D. Ashok Kumar, S.A. Ravindhra, Rakeshkumar Yadav, T. Ravichandran, R. Hari Krishnan, R. Gopinath, C. Prabhakaran, S. Gomathy,
N. Santhanakumar, Udhayakumar, Ranjithkumar, Murugesan, Navaneethapandiyan, Rajmohan, Aravindh Babu, Selvendiran, Tamil Mani
Devi, Nandhakumar, G. Preethi, Chandrabalu, Akshitha, Satham Hussain, Hari Vignesh, Sentrayan, Suresh, Senbagavalli, Chandrakumar,
Ponmalaiselvan, Sundaramoorthy, Thirumalai, Manikumar, Venkatesh
Telangana Team: Pucha Uday Kumar, B. Dinesh Kumar, J.J. Babu, N. Arlappa, I.I. Meshram, G.M. Subba Rao, J.P. Devraj, M.V. Surekha,
R. Ananthan, Mammidi Raja Sriswan, M. Mahesh Kumar, B. Santosh Kumar, P. Raghavendra, D. Anwar Basha, Blessy Prabhu Priyanka,
D. Teena, G. Sarika, Mane Kumar, B. Raju Naik, Ronald Rose, Adepu Rajesham, Sneha Shukla, K. Jayakrishna, Md. Shahed Ali, Arroju,
Purnachandar, K. Rahul, Nagender Babu, K.S. Ravi, B. Deepak Kumar, N. Anjaiah, R. Laxman, N. Hanmanthu, G.V. Raji Reddy, M.
Pydiraju, Sai Kumar, Narasimhulu, P. Sreenu, K. Sree Ramakrishna, Chandrababu, Srinivas Reddy, G.L.A. Stephen, Tulja, Raghunatha
Babu, Sailaja, C. Sai Babu, P. Sunu, B. Satyanarayana, Bhavani, Aruna, Srinivas, Sheela, Nancharamma, Roja, Venkataramana, Jhansi,
Rani, Swaroopa, Vijayalaxmi, Anitha, Tulsi
Uttar Pradesh Central/Himachal Pradesh/Uttarakhand Team: Haribhan Singh, Ravinder Kumar, Rajesh Guleri, Sushil Chander,
Satyavrat Vaidya, Raman Sharma, Ashwini Yadav, Vikas Sabharwal, Pankaj Singh, Manu Jain, Manoj Bahukhandi, Ramesh Kunwar,
Ashish Gusain, Arjit Kumar, Ravindra Nath, Ashwini Yadav, Dhruv Gopal, R.C. Pandey, Prashant Upadhyay, Shishir Puri, Archana
Srivastava, Gautam Ranjan, Vineet Kumar Shukla, R.K. Gautam, Kishan Kumar, Nandan Kumar Mishra, Simran Kaur Bhojwani, Dechen
Yangdol, Upendra Singh, Amit Kumar Yadav, Mohit Tiwari, Shivani Yadav, Rahul Kumar, Harshit Kumar, Basudev Singh, Deepak
Babu, Sushil Kumar Pal, Mohit Kumar Sharma, Gopal Prasad, P. Vaidivel, Maneesh Kumar, Rahul Gond, Bitesh Kumar, Prabhat Kumar,
Hariom Kushwaha, Mohammad Gani Afridi, Nishtha Verma, Rakesh Kumar Sharma, Uday Singh Kushwaha, Veer Vishal, Saurav Yadav,
Satya Prakash, Navneet Rajput, Raju Kashyap, Mahaveer Chaudary, Iftikhar Uddin, Sunny Sharma, Santosh Kumar, Kushwah, Akhalesh,
Himanshu Parashar, Sapna Yadav
Uttar Pradesh East Team: Rajeev Singh, Kamran Zaman, Ashok Kumar Pandey, Madhu Gairola, Vinay Dange, Ghanshyam Singh, Atul
Kumar Singhal, Prakash Agrawal, Satish Chandra Singh, Amit Mohan Prasad, Ramesh Chandra Pandey, Birendra Panchal, Vishal Yadav,
Mukesh Kumar Mishra, Sonal Rajput, Jaibardhan Siddharth, Rohit Baghel, Rashmi Yadav, Ayushi Yadav, Punit Kumar, Abhishek Kumar
Mishra, Akash Kushwaha, Deepak Kumar, Vinod Kumar, Ranjeet Singh, Vipul Kumar, Vijay Kumar Prasad
Uttar Pradesh West Team: Akhileshwar Sharda, Vijaya, Bharat, Anand, Sunil Dohre, Hari Dutt, Samrat, Dilip Singh, Vijay, Naresh,
Vinay, Akash, Deshdeepak Gautam, Brijesh, Swati Singh, Shaurabh Kumar, Narendra Kumar, Sonu Yadav, Rahul Yadav, Manisha,
Sheena, Himalaya, Raju, Mevaram, Jagrat, Shah Alam, Rezwan, Preeti, Arvind, Aseem, Sonu, Krishnalata, Pravesh, Himachal, Lalit
Kumar, Rifakat Hussain, Ravi Shankar, Renu Choudhury, Natwarlal, Praveen Kumar, Himanshu Rawal, Kailash Chandra, Rajendra Pal,
Sattu Sain, Susheel Kumar, Md. Mumtazir, Vinit Chouhan, Surajban, Anil, Sandeep, Sanjay, Pradeep
West Bengal Team: Malay Kumar Saha, Debottam Pal, Falguni Denath, Subrata Biswas, Suman Kanungo, Bipra Bishnu, Amitava Sarkar,
Pritam Roy, Arup Chakrabarty, Abhijit Dey, Pallav Bhattacharya, Amlan Datta, Shubhadeep Bhuniya, Aniket Chowdhury, Subhendu Roy,
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Sanjiv Jha, Shyamal Saren, Jagannath Sarkar, Puran Sharma, Subarna Goswami, Prakash Mridha, Nitai Mondal, Dilip Biswas, Samudra
Sengupta, Somnath Mukherjee, Aatreye Chakraborty, Debashis Roy, Rabiul Islam Gayen, Santanu Nandy
National Centre for Disease Control: Himanshu Chauhan, Tanzin Dikid, Sanket Kulkarni, Aakash Shrivastava
ICMR-NIE Team: T. Karunakaran, Annamma Jose, R. Sivakumar, K. Vasanthi, K. Kalaiyarasi, S. Dhanapriya Vadhani, T. Magesh,
E.B. Arun Prasath, R. Pradeepa, Sauvik Dasgupta, Josephine Pradhan, Arya Vinod, Elizabeth Varghese, M.P. Sarath Kumar, Ponnaiah
Manickam, Amanda Rozario G.A., Beula Margrate, D. Augustine, D. Sudha Rani, Jasmine Farzana, Keerthana G. Kiruthika, Michaelraj
E., Priyanka S., Roopavathi Ongesh, V. Vettrichelvan, D. Chokkalingam, H. Dinesh Kumar. ICMR-NIV Team: Anita Aich, Rajlaxmi
Jain. ICMR-NIRT Team: Anuradha Rajamanickam, N. Pavan Kumar, Himanshu Singh Chandel, Sravanan Munisankar, Gokul Raj Katha
Muthu, Harishankar Murugesan, Suganthi Chittibabu, Anbarasu Deenadayalan, D. Madhavan, Y. John Arockiadoss, M. Mahesh Kumar,
G. Gnanamoorthy, Muthukumaravel S., Sakthivel A., Vaishnavi S., Esther Nirmala Mary J., Shakila V., Arul Nancy P., Karthikesan,
Kumaravel, Kalaichelvi, Silambu Chelvi K., Angayarkanni B., Anbalagan S., Sathyamurthi P., Madheswaran A., Mangaiyarkarasi, Syed
Nisar R.K., Inbanathan A., Sangeetha A., Karthika C., Purushothaman K., Tamilarasan V., S. Suresh, A. Yuvaraj, A. Harish. ICMR-
VCRC Team: Sankari Thirumal, S. Muthukumaravel, S. Vaishnavi, Esther Nirmala Mary, Sakthivel, V. Sakila. ICMR: Nivedita Gupta,
Priya Katriyal
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Supplementary Table. Districts for the serosurvey by strata
based on incidence of reported COVID-19 cases
Stratum District selected in the stratum
Zero Vizianagaram, Pakur, Beed, Ganjam,
Bijapur, Balrampur, Kabeerdham, Gonda,
Karbi Anglong, Udalguri, Kullu, Latehar,
Chitradurga, Rayagada, Alipurduar
Low Alipurduar, Parbhani, Nanded, Madhubani,
Simdega, Koraput, Purnia, Rajsamand,
Bareilly, Jangoan, Begusarai, Jalor Garhwal,
Kurukshetra, Kamareddy, Unnao, Mau,
Kamrup Metropolitan, Muzaarpur, Sabar
Kantha, Gurdaspur, Bankura, Jhargram
Medium 24 Paraganas South, Pulwama, Tiruvannamalai,
Sangli, Ahmad Nagar, Arwal, Thrisur, Gwalior,
Auraiya, Jalgaon, Ernakulam, Nalgonda,
Ludhiana, Surguja, Palakkad, Medinipur East
High Coimbatore, Chennai, Buxar, Ujjain, Dausa,
Gautam Buddha Nagar, Patiala, Krishna, Sri
Potti Sriramulu Nellore, Jalandhar, Saharanpur,
Jyotiba Phule Nagar, Narmada, Mahisagar,
Bangalore, Gulbarga, Dewas
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Serologic studies are crucial for clarifying dynamics of the coronavirus disease pandemic. Past work on serologic studies (e.g., during influenza pandemics) has made relevant contributions, but specific conditions of the current situation require adaptation. Although detection of antibodies to measure exposure, immunity, or both seems straightforward conceptually, numerous challenges exist in terms of sample collection, what the presence of antibodies actually means, and appropriate analysis and interpretation to account for test accuracy and sampling biases. Successful deployment of serologic studies depends on type and performance of serologic tests, population studied, use of adequate study designs, and appropriate analysis and interpretation of data. We highlight key questions that serologic studies can help answer at different times, review strengths and limitations of different assay types and study designs, and discuss methods for rapid sharing and analysis of serologic data to determine global transmission of severe acute respiratory syndrome coronavirus 2.
Full-text available
Background & objectives: Since the beginning of the year 2020, the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) impacted humankind adversely in almost all spheres of life. The virus belongs to the genus Betacoronavirus of the family Coronaviridae. SARS-CoV-2 causes the disease known as coronavirus disease 2019 (COVID-19) with mild-to-severe respiratory illness. The currently available diagnostic tools for the diagnosis of COVID-19 are mainly based on molecular assays. Real-time reverse transcription-polymerase chain reaction is the only diagnostic method currently recommended by the World Health Organization for COVID-19. With the rapid spread of SARS-CoV-2, it is necessary to utilize other tests, which would determine the burden of the disease as well as the spread of the outbreak. Considering the need for the development of such a screening test, an attempt was made to develop and evaluate an IgG-based ELISA for COVID-19. Methods: A total of 513 blood samples (131 positive, 382 negative for SARS-CoV-2) were collected and tested by microneutralization test (MNT). Antigen stock of SARS-CoV-2 was prepared by propagating the virus in Vero CCL-81 cells. An IgG capture ELISA was developed for serological detection of anti-SARS-CoV-2 IgG in serum samples. The end point cut-off values were determined by using receiver operating characteristic (ROC) curve. Inter-assay variability was determined. Results: The developed ELISA was found to be 92.37 per cent sensitive, 97.9 per cent specific, robust and reproducible. The positive and negative predictive values were 94.44 and 98.14 per cent, respectively. Interpretation & conclusions: This indigenously developed IgG ELISA was found to be sensitive and specific for the detection of anti-SARS-CoV-2 IgG in human serum samples. This assay may be used for determining seroprevalence of SARS-CoV-2 in a population exposed to the virus.
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Conducting population-based serosurveillance for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) will estimate and monitor the trend of infection in the adult general population, determine the socio-demographic risk factors and delineate the geographical spread of the infection. For this purpose, a serial cross-sectional survey would be conducted with a sample size of 24,000 distributed equally across four strata of districts categorized on the basis of the incidence of reported cases of COVID-19. Sixty districts will be included in the survey. Simultaneously, the survey will be done in 10 high-burden hotspot cities. ELISA-based antibody tests would be used. Data collection will be done using a mobile-based application. Prevalence from the group of districts in each of the four strata will be pooled to estimate the population prevalence of COVID-19 infection, and similarly for the hotspot cities, after adjusting for demographic characteristics and antibody test performance. The total number of reported cases in the districts and hotspot cities will be adjusted using this seroprevalence to estimate the expected number of infected individuals in the area. Such serosurveys repeated at regular intervals can also guide containment measures in respective areas. State-specific context of disease burden, priorities and resources should guide the use of multifarious surveillance options for the current COVID-19 epidemic.
Full-text available
Serological testing for SARS-CoV-2 has enormous potential to contribute to COVID-19 pandemic response efforts. However, the required performance characteristics of antibody tests will critically depend on the use case (individual-level vs. population-level).
Full-text available
Background. As the world grapples with the COVID-19 pandemic, there is increasing global interest in the role of serological testing for population monitoring and to inform public policy. However, limitations in serological study designs and test standards raise concerns about the validity of seroprevalence estimates and their utility in decision-making. There is now a critical window of opportunity to learn from early SARS-CoV-2 serology studies. We aimed to synthesize the results of SARS-CoV-2 serosurveillance projects from around the world and provide recommendations to improve the coordination, strategy, and methodology of future serosurveillance efforts. Methods. This was a rapid systematic review of cross-sectional and cohort studies reporting seroprevalence outcomes for SARS-CoV 2. We included completed, ongoing, and proposed serosurveys. The search included electronic databases (PubMed, MedRXIV, BioRXIV, and WHO ICTPR); five medical journals (NEJM, BMJ, JAMA, The Lancet, Annals of Internal Medicine); reports by governments, NGOs, and health systems; and media reports (Google News) from December 1, 2019 to May 1, 2020. We extracted data on study characteristics and critically appraised prevalence estimates using Joanna Briggs Institute criteria. Results. Seventy records met inclusion criteria, describing 73 studies. Of these, 23 reported prevalence estimates: eight preprints, 14 news articles, and one government report. These studies had a total sample size of 35,784 and reported 42 prevalence estimates. Seroprevalence estimates ranged from 0.4% to 59.3%. No estimates were found to have a low risk of bias (43% high risk, 21% moderate risk, 36% unclear). Fifty records reported characteristics of ongoing or proposed serosurveys. Overall, twenty countries have completed, ongoing, or proposed serosurveys. Discussion. Study design, quality, and prevalence estimates of early SARS-CoV2 serosurveys are heterogeneous, suggesting that the urgency to examine seroprevalence may have compromised methodological rigour. Based on the limitations of included studies, future serosurvey investigators and stakeholders should ensure that: i) serological tests used undergo high-quality independent evaluations that include cross-reactivity; ii) all reports of serosurvey results, including media, describe the test used, sample size, and sampling method; and iii) initiatives are coordinated to prevent test fatigue, minimize redundant efforts, and encourage better study methodology. Other. PROSPERO: CRD42020183634. No third-party funding.
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We report acute antibody responses to SARS-CoV-2 in 285 patients with COVID-19. Within 19 days after symptom onset, 100% of patients tested positive for antiviral immunoglobulin-G (IgG). Seroconversion for IgG and IgM occurred simultaneously or sequentially. Both IgG and IgM titers plateaued within 6 days after seroconversion. Serological testing may be helpful for the diagnosis of suspected patients with negative RT–PCR results and for the identification of asymptomatic infections. A cross-sectional study of hospitalized patients with COVID-19 and a longitudinal follow-up study of patients with COVID-19 suggest that SARS-CoV2-specific IgG or IgM seroconversion occurs within 20 days post symptom onset.
Background Spain is one of the European countries most affected by the COVID-19 pandemic. Serological surveys are a valuable tool to assess the extent of the epidemic, given the existence of asymptomatic cases and little access to diagnostic tests. This nationwide population-based study aims to estimate the seroprevalence of SARS-CoV-2 infection in Spain at national and regional level. Methods 35 883 households were selected from municipal rolls using two-stage random sampling stratified by province and municipality size, with all residents invited to participate. From April 27 to May 11, 2020, 61 075 participants (75·1% of all contacted individuals within selected households) answered a questionnaire on history of symptoms compatible with COVID-19 and risk factors, received a point-of-care antibody test, and, if agreed, donated a blood sample for additional testing with a chemiluminescent microparticle immunoassay. Prevalences of IgG antibodies were adjusted using sampling weights and post-stratification to allow for differences in non-response rates based on age group, sex, and census-tract income. Using results for both tests, we calculated a seroprevalence range maximising either specificity (positive for both tests) or sensitivity (positive for either test). Findings Seroprevalence was 5·0% (95% CI 4·7–5·4) by the point-of-care test and 4·6% (4·3–5·0) by immunoassay, with a specificity–sensitivity range of 3·7% (3·3–4·0; both tests positive) to 6·2% (5·8–6·6; either test positive), with no differences by sex and lower seroprevalence in children younger than 10 years (<3·1% by the point-of-care test). There was substantial geographical variability, with higher prevalence around Madrid (>10%) and lower in coastal areas (<3%). Seroprevalence among 195 participants with positive PCR more than 14 days before the study visit ranged from 87·6% (81·1–92·1; both tests positive) to 91·8% (86·3–95·3; either test positive). In 7273 individuals with anosmia or at least three symptoms, seroprevalence ranged from 15·3% (13·8–16·8) to 19·3% (17·7–21·0). Around a third of seropositive participants were asymptomatic, ranging from 21·9% (19·1–24·9) to 35·8% (33·1–38·5). Only 19·5% (16·3–23·2) of symptomatic participants who were seropositive by both the point-of-care test and immunoassay reported a previous PCR test. Interpretation The majority of the Spanish population is seronegative to SARS-CoV-2 infection, even in hotspot areas. Most PCR-confirmed cases have detectable antibodies, but a substantial proportion of people with symptoms compatible with COVID-19 did not have a PCR test and at least a third of infections determined by serology were asymptomatic. These results emphasise the need for maintaining public health measures to avoid a new epidemic wave. Funding Spanish Ministry of Health, Institute of Health Carlos III, and Spanish National Health System.
Disease surveillance forms the basis for response to epidemics. COVID-19 provides a modern example of why the classic mantra of “person, place, and time” remains crucial: epidemic control requires knowing trends in disease frequency in different subgroups and locations. We review key epidemiological concepts and discuss some of the preventable methodologic errors that have arisen in reporting on the COVID-19 crisis. (Am J Public Health. Published online ahead of print April 23, 2020: e1–e3. doi:10.2105/AJPH.2020.305708)