Infection Fatality Fate of COVID-19 Inferred from Seroprevalence Data

What is the IFR of COVID-19? I see this asked multiple times per day. I also see social media posts daily stating wild COVID IFR values.

My present recommended go-to-source for COVID IFR values is a peer-reviewed paper from John P.A. Ioannidis,  C.F. Rehnborg Chair in Disease Prevention, Professor of Medicine, of Epidemiology and Population Health, and (by courtesy) of Biomedical Data Science, and of Statistics; co-Director, Meta-Research Innovation Center at Stanford University (METRICS).

The paper formed part of the WHO Bulletin last month and was placed here

However for reasons unknown, it disappears (404 server error) and then reappears, repeatedly for the past few days. To preserve it as sent by the WHO, I’ve reuploaded in its native PDF format, Infection fatality rate of COVID-19 inferred from seroprevalence data, and converted the first 19 (core) pages below to HTML.


March 26th, 2021

Peer-reviewed paper ‘Reconciling estimates of global spread and infection fatality rates of COVID-19: an overview of systematic evaluations – John P.A. Ioannidis’.

Concludes “… the available evidence suggests average global IFR of ~0.15% and ~1.5-2.0 billion infections by February 2021 with substantial differences in IFR and in infection spread across continents, countries, and locations.”

Infection fatality rate of COVID-19 inferred from seroprevalence data

This online first version has been peer-reviewed, accepted and edited,

but not formatted and finalized with corrections from authors and proofreaders

John P A Ioannidisa
a Meta-Research Innovation Center at Stanford (METRICS), Stanford University, 1265 Welch Road, Stanford, California 94305, United States of America.

Correspondence to John P A Ioannidis (email: [email protected]).

(Submitted: 13 May 2020 – Revised version received: 13 September 2020 – Accepted: 15 September 2020 – Published online: 14 October 2020)


Objective To estimate the infection fatality rate of coronavirus disease 2019 (COVID-19) from seroprevalence data.

Methods I searched PubMed and preprint servers for COVID-19 seroprevalence studies with a sample size  ≥500 as of 9 September, 2020. I also retrieved additional results of national studies from preliminary press releases and reports. I assessed the studies for design features and seroprevalence estimates. I estimated the infection fatality rate for each study by dividing the number of COVID-19 deaths by the number of people estimated to be infected in each region. I corrected for the number of antibody types tested (immunoglobin, IgG, IgM, IgA).

Results I included 61 studies (74 estimates) and eight preliminary national estimates. Seroprevalence estimates ranged from 0.02% to 53.40%. Infection fatality rates ranged from 0.00% to 1.63%, corrected values from 0.00% to 1.54%. Across 51 locations, the median COVID-19 infection fatality rate was 0.27% (corrected 0.23%): the rate was 0.09% in locations with COVID-19 population mortality rates less than the global average (< 118 deaths/million), 0.20% in locations with 118–500 COVID-19 deaths/million people and 0.57% in locations with > 500 COVID-19 deaths/million people. In people < 70 years, infection fatality rates ranged from 0.00% to 0.31% with crude and corrected medians of 0.05%.

Conclusion The infection fatality rate of COVID-19 can vary substantially across different locations and this may reflect differences in population age structure and case- mix of infected and deceased patients and other factors. The inferred infection fatality rates tended to be much lower than estimates made earlier in the pandemic.



The infection fatality rate, the probability of dying for a person who is infected, is one of the most important features of the coronavirus disease 2019 (COVID-19) pandemic. The expected total mortality burden of COVID-19 is directly related to the infection fatality rate. Moreover, justification for various non-pharmacological public health interventions depends on the infection fatality rate. Some stringent interventions that potentially also result in more noticeable collateral harms1 may be considered appropriate, if the infection fatality rate is high. Conversely, the same measures may fall short of acceptable risk–benefit thresholds, if the infection fatality rate is low.

Early data from China suggested a 3.4% case fatality rate2 and that asymptomatic infections were uncommon,3 thus the case fatality rate and infection fatality rate would be about the same. Mathematical models have suggested that 40–81% of the world population could be infected,4,5 and have lowered the infection fatality rate to 1.0% or 0.9%.5,6 Since March 2020, many studies have estimated the spread of the virus causing COVID-19 – severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) – in various locations by evaluating seroprevalence. I used the prevalence data from these studies to infer estimates of the COVID-19 infection fatality rate.



Seroprevalence studies

The input data for calculations of infection fatality rate were studies on the seroprevalence of COVID-19 done in the general population, or in samples that might approximately represent the general population (e.g. with proper reweighting), that had been published in peer-reviewed journals or as preprints (irrespective of language) as of 9 September 2020. I considered only studies with at least 500 assessed samples because smaller data sets would result in large uncertainty for any calculations based on these data. I included studies that made seroprevalence assessments at different time intervals if at least one time interval assessment had a sample size of at least 500 participants. If there were different eligible time intervals, I selected the one with the highest seroprevalence, since seroprevalence may decrease over time as antibody titres decrease. I excluded studies with data collected for more than a month that could not be broken into at least one eligible time interval less than one month duration because it would not be possible to estimate a point seroprevalence reliably. Studies were eligible regardless of the exact age range of participants included, but I excluded studies with only children.

I also examined results from national studies from preliminary press releases and reports whenever a country had no other data presented in published papers of preprints. This inclusion allowed these countries to be represented, but information was less complete than information in published papers or preprints and thus requires caution.

I included studies on blood donors, although they may underestimate seroprevalence and overestimate infection fatality rate because of the healthy volunteer effect. I excluded studies on health-care workers, since this group is at a potentially high exposure risk, which may result in seroprevalence estimates much higher than the general population and thus an improbably low infection fatality rate. Similarly, I also excluded studies on communities (e.g. shelters or religious or other shared-living communities). Studies were eligible regardless of whether they aimed to evaluate seroprevalence in large or small regions, provided that the population of reference in the region was at least 5000 people.

I searched PubMed® (LitCOVID), and medRxiv, bioRxiv and Research Square using the terms “seroprevalence” OR “antibodies” with continuous updates. I made the first search in early May and did monthly updates, with the last update on 9 September, 2020. I contacted field experts to retrieve any important studies that may have been missed.

From each study, I extracted information on location, recruitment and sampling strategy, dates of sample collection, sample size, types of antibody measured (immunoglobulin G (IgG), IgM and IgA), the estimated crude seroprevalence (positive samples divided by all samples tested), adjusted seroprevalence and the factors that the authors considered for adjustment.


Inferred infection fatality rate

If a study did not cover an entire country, I collected information on the population of the relevant location from the paper or recent census data so as to approximate as much as possible the relevant catchment area (e.g. region(s) or county(ies)). Some studies targeted specific age groups (e.g. excluding elderly people and/or excluding children) and some estimated numbers of people infected in the population based on specific age groups. For consistency, I used the entire population (all ages) and, separately, the population 0–70 years to estimate numbers of infected people. I assumed that the seroprevalence would be similar in different age groups, but I also recorded any significant differences in seroprevalence across age strata so as to examine the validity of this assumption.

I calculated the number of infected people by multiplying the relevant population size and the adjusted estimate of seroprevalence. If a study did not give an adjusted seroprevalence estimate, I used the unadjusted seroprevalence instead. When seroprevalence estimates with different adjustments were available, I selected the analysis with largest adjustment. The factors adjusted for included COVID-19 test performance, sampling design, and other factors such as age, sex, clustering effects or socioeconomic factors. I did not adjust for specificity in test performance when positive antibody results were already validated by a different method.

For the number of COVID-19 deaths, I chose the number of deaths accumulated until the date 1 week after the midpoint of the study period (or the date closest to this that had available data) – unless the authors of the study had strong arguments to choose some other time point or approach. The 1-week lag accounts for different delays in developing antibodies versus dying from infection. The number of deaths is an approximation because it is not known when exactly each patient who died was infected. The 1-week cut-off after the study midpoint may underestimate deaths in places where patients are in hospital for a long time before death, and may overestimate deaths in places where patients die soon because of poor or even inappropriate care. Whether or not the health system became overloaded may also affect the number of deaths. Moreover, because of imperfect diagnostic documentation, COVID-19 deaths may have been both overcounted and undercounted in different locations and at different time points.

I calculated the inferred infection fatality rate by dividing the number of deaths by the number of infected people for the entire population, and separately for people < 70 years. I took the proportion of COVID-19 deaths that occurred in people < 70 years old from situational reports for the respective locations that I retrieved at the time I identified the seroprevalence studies. I also calculated a corrected infection fatality rate to try and account for the fact that only one or two types of antibodies (among IgG, IgM, IgA) might have been used. I corrected seroprevalence upwards (and inferred infection fatality rate downwards) by one tenth of its value if a study did not measure IgM and similarly if IgA was not measured. This correction is reasonable based on some early evidence,7 although there is uncertainty about the exact correction factor.

Data synthesis

The estimates of the infection fatality rate across all locations showed great heterogeneity with I2 exceeding 99.9%; thus, a meta-analysis would be inappropriate to report across all locations. Quantitative synthesis with meta-analysis across all locations would also be misleading since locations with high COVID-19 seroprevalence would tend to carry more weight than locations with low seroprevalence. Furthermore, locations with more studies (typically those that have attracted more attention because of high death tolls and thus high infection fatality rates) would be represented multiple times in the calculations. In addition, poorly conducted studies with fewer adjustments would get more weight because of spuriously narrower confidence intervals than more rigorous studies with more careful adjustments which allow for more uncertainty. Finally, with a highly skewed distribution of the infection fatality rate and with large between-study heterogeneity, typical random effects models would produce an incorrectly high summary infection fatality rate that approximates the mean of the study-specific estimates (also strongly influenced by high-mortality locations where more studies have been done); for such a skewed distribution, the median is more appropriate.

Therefore, in a first step, I grouped estimates of the infection fatality rate from studies in the same country (or for the United States of America, the same state) together and calculated a single infection fatality rate for that location, weighting the study-specific infection fatality rates by the sample size of each study. This approach avoided inappropriately giving more weight to studies with higher seroprevalence estimates and those with seemingly narrower confidence intervals because of poor or no adjustments, while still giving more weight to larger studies. Then, I used the single summary estimate for each location to calculate the median of the distribution of location-specific infection fatality rate estimates. Finally, I explored whether the location-specific infection fatality rates were associated with the COVID-19 mortality rate in the population (COVID-19 deaths per million people) in each location as of 12 September 2020; this analysis allowed me to assess whether estimates of the infection fatality rate tended to be higher in locations with a higher burden of death from COVID-19.



Seroprevalence studies

I retrieved 61 studies with 74 eligible estimates published either in the peer-reviewed literature or as preprints as of 9 September 2020.8–68 Furthermore, I also considered another eight preliminary national estimates.69–76 This search yielded a total of 82 eligible estimates (Fig. 1).

The studies varied substantially in sampling and recruitment designs (Table 1; available at: Of the 61 studies, 24 studies8,10,16,17,20,22,25,33,34,36,37,42,46–49,52–54,61,63,65,68 explicitly aimed for random sampling from the general population. In principle, random sampling is a stronger design. However, even then, people who cannot be reached (e.g. by email or telephone or even by visiting them at a house location) will not be recruited, and these vulnerable populations are likely to be missed. Moreover, several such studies8,10,16,37,42 focused on geographical locations with high numbers of deaths, higher than other locations in the same city or country, and this emphasis would tend to select eventually for a higher infection fatality rate on average.

Eleven studies assessed blood donors,12,15,18,24,28,31,41,44,45,55,60 which might underestimate COVID-19 seroprevalence in the general population. For example, 200 blood donors in Oise, France showed 3.00% seroprevalence, while the seroprevalence was 25.87% (171/661) in pupils, siblings, parents, teachers and staff at a high school with a cluster of cases in the same area; the true population seroprevalence may be between these two values.13

For other studies, healthy volunteer bias19 may underestimate seroprevalence, attracting people with symptoms26 may overestimate seroprevalence, and studies of employees,14,21,25,32,66 grocery store clients23 or patient cohorts11,14,27–30,36,38,40,50,51,56,59,62,64,67 risk sampling bias in an unpredictable direction.

All the studies tested for IgG antibodies but only about half also assessed IgM and few assessed IgA. Only seven studies assessed all three types of antibodies and/or used pan-Ig antibodies. The ratio of people sampled versus the total population of the region was more than 1:1000 in 20 studies (Table 2; available at:


Seroprevalence estimates

Seroprevalence for the infection ranged from 0.02% to 53.40% (58.40% in the slum sub- population in Mumbai; Table 3). Studies varied considerably depending on whether or not they tried to adjust their estimates for test performance, sampling (to get closer to a more representative sample), clustering (e.g. when including household members) and other factors. The adjusted seroprevalence occasionally differed substantially from the unadjusted value. In studies that used samples from multiple locations, between-location heterogeneity was seen (e.g. 0.00–25.00% across 133 Brazilian cities).25

Inferred infection fatality rate

Inferred infection fatality rate estimates varied from 0.00% to 1.63% (Table 4). Corrected values also varied considerably (0.00–1.54%).

For 15 locations, more than one estimate of the infection fatality rate was available and thus I could compare the infection fatality rate from different studies evaluating the same location. The estimates of infection fatality rate tended to be more homogeneous within each location, while they differed markedly across locations (Fig. 2). Within the same location, infection fatality rate estimates tend to have only small differences, even though it is possible that different areas within the same location may also have real differences in infection fatality rate. France is one exception where differences are large, but both estimates come from population studies of outbreaks from schools and thus may not provide good estimates of population seroprevalence and may lead to an underestimated infection fatality rate.

I used summary estimates weighted for sample size to generate a single estimate for each location. Data were available for 51 different locations (including the inferred infection fatality rates from the eight preliminary additional national estimates in Table 5).

The median infection fatality rate across all 51 locations was 0.27% (corrected 0.23%).
Most data came from locations with high death tolls from COVID-19 and 32 of the locations had a population mortality rate (COVID-19 deaths per million population) higher than the global average (118 deaths from COVID-19 per million as of 12 September 2020;79 Fig. 3). Uncorrected estimates of the infection fatality rate of COVID-19 ranged from 0.01% to 0.67% (median 0.10%) across the 19 locations with a population mortality rate for COVID-19 lower than the global average, from 0.07% to 0.73% (median 0.20%) across 17 locations with population mortality rate higher than the global average but lower than 500 COVID-19 deaths per million, and from 0.20% to 1.63% (median 0.71%) across 15 locations with more than 500 COVID-19 deaths per million. The corrected estimates of the median infection fatality rate were 0.09%, 0.20% and 0.57%, respectively, for the three location groups.

For people < 70 years old, the infection fatality rate of COVId-19 across 40 locations with available data ranged from 0.00% to 0.31% (median 0.05%); the corrected values were similar.



The infection fatality rate is not a fixed physical constant and it can vary substantially across locations, depending on the population structure, the case-mix of infected and deceased individuals and other, local factors. The studies analysed here represent 82 different estimates of the infection fatality rate of COVID-19, but they are not fully representative of all countries and locations around the world. Most of the studies are from locations with overall COVID-19 mortality rates that are higher than the global average. The inferred median infection fatality rate in locations with a COVID-19 mortality rate lower than the global average is low (0.09%). If one could sample equally from all locations globally, the median infection fatality rate might be even substantially lower than the 0.23% observed in my analysis.

COVID-19 has a very steep age gradient for risk of death.80 Moreover, many, and in some cases most, deaths in European countries that have had large numbers of cases and deaths81 and in the USA82 occurred in nursing homes. Locations with many nursing home deaths may have high estimates of the infection fatality rate, but the infection fatality rate would still be low among non- elderly, non-debilitated people.

Within China, the much higher infection fatality rate estimates in Wuhan compared with other areas of the country may reflect widespread nosocomial infections,83 as well as unfamiliarity with how to manage the infection as the first location that had to deal with COVID-19. The very many deaths in nursing homes, nosocomial infections and overwhelmed hospitals may also explain the high number of fatalities in specific locations in Italy84 and New York and neighbouring states.23,27,35,56 Poor decisions (e.g. sending COVID-19 patients to nursing homes), poor management (e.g. unnecessary mechanical ventilation) and hydroxychloroquine may also have contributed to worse outcomes. High levels of congestion (e.g. in busy public transport systems) may also have exposed many people to high infectious loads and, thus, perhaps more severe disease. A more aggressive viral clade has also been speculated.85 The infection fatality rate may be very high among disadvantaged populations and settings with a combination of factors predisposing to higher fatalities.37

Very low infection fatality rates seem common in Asian countries.8,11,29,48,49,51,59,61,67 A younger population in these countries (excluding Japan), previous immunity from exposure to other coronaviruses, genetic differences, hygiene etiquette, lower infectious load and other unknown factors may explain these low rates. The infection fatality rate is low also in low-income countries in both Asia and Africa,44,49,66,67 perhaps reflecting the young age-structure. However, comorbidities, poverty, frailty (e.g. malnutrition) and congested urban living circumstances may have an adverse effect on risk and thus increase infection fatality rate.

Antibody titres may decline with time10,28,32,86,87 and this would give falsely low prevalence estimates. I considered the maximum seroprevalence estimate when multiple repeated measurements at different time points were available, but even then some of this decline cannot be fully accounted for. With four exceptions,10,28,32,51 the maximum seroprevalence value was at the latest time point.

Positive controls for the antibody assays used were typically symptomatic patients with positive polymerase chain reaction tests. Symptomatic patients may be more likely to develop antibodies.87–91 Since seroprevalence studies specifically try to reveal undiagnosed asymptomatic and mildly symptomatic infections, a lower sensitivity for these mild infections could lead to substantial underestimates of the number of infected people and overestimate of the inferred infection fatality rate.

A main issue with seroprevalence studies is whether they offer a representative picture of the population in the assessed region. A generic problem is that vulnerable people at high risk of infection and/or death may be more difficult to recruit in survey-type studies. COVID-19 infection is particularly widespread and/or lethal in nursing homes, in homeless people, in prisons and in disadvantaged minorities.92 Most of these populations are very difficult, or even impossible, to reach and sample and they are probably under-represented to various degrees (or even entirely missed) in surveys. This sampling obstacle would result in underestimating the seroprevalence and overestimating infection fatality rate.

In principle, adjusted seroprevalence values may be closer to the true estimate, but the adjustments show that each study alone may have unavoidable uncertainty and fluctuation, depending on the type of analysis chosen. Furthermore, my corrected infection fatality rate estimates try to account for undercounting of infected people when not all three antibodies (IgG, IgM and IgA) were assessed. However, the magnitude of the correction is uncertain and may vary in different circumstances. An unknown proportion of people may have responded to the virus using immune mechanisms (mucosal, innate, cellular) without generating any serum antibodies.93–97

A limitation of this analysis is that several studies included have not yet been fully peer- reviewed and some are still ongoing. Moreover, despite efforts made by seroprevalence studies to generate estimates applicable to the general population, representativeness is difficult to ensure, even for the most rigorous studies and despite adjustments made. Estimating a single infection fatality rate value for a whole country or state can be misleading, when there is often huge variation in the population mixing patterns and pockets of high or low mortality. Furthermore, many studies have evaluated people within restricted age ranges, and the age groups that are not included may differ in seroprevalence. Statistically significant, modest differences in seroprevalence across some age groups have been observed in several studies.10,13,15,23,27,36,38

Lower values have been seen in young children and higher values in adolescents and young adults, but these patterns are inconsistent and not strong enough to suggest major differences extrapolating across age groups.

Acknowledging these limitations, based on the currently available data, one may project that over half a billion people have been infected as of 12 September, 2020, far more than the approximately 29 million documented laboratory-confirmed cases. Most locations probably have an infection fatality rate less than 0.20% and with appropriate, precise non-pharmacological measures that selectively try to protect high-risk vulnerable populations and settings, the infection fatality rate may be brought even lower.


METRICS has been supported by a grant from the Laura and John Arnold Foundation.

Competing interests:

I am a co-author (not principal investigator) of one of the seroprevalence studies.



  1. Melnick ER, Ioannidis JPA. Should governments continue lockdown to slow the spread of covid-19? BMJ. 2020 Jun 3;369:m1924. PMID:32493767
  2. WHO Director-General’s opening remarks at the media briefing on COVID-19 – 3 March 2020. Geneva: World Health Organization; 2020. Available from: 3-march-2020 [cited 2020 May 10].
  3. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) 16–24 February 2020. Geneva: World Health Organization; 2020. Available from: [cited 2020 May 10].
  4. McGinty JC. How many people might one person with coronavirus infect? Wall Street Journal. February 14, 2020. Available from: [cited 2020 Feb 27].
  5. Ferguson NM, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, et al. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. London: Imperial College; 2020. Available from: [cited 2020 May 10].
  6. Meyerowitz-Katz G, Merone L. A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates. [online ahead of print]. Int J Infect Dis. 2020 Sep 29;S1201(20):32180–9. PMID:33007452
  7. Garcia-Basteiro AL, Moncunill G, Tortajada M, Vidal M, Guinovart C, Jiménez A, et Seroprevalence of antibodies against SARS-CoV-2 among health care workers in a large Spanish reference hospital. Nat Commun. 2020 Jul 8;11(1):3500. PMID:32641730
  8. Shakiba M, Nazari S, Mehrabian F, Rezvani SM, Ghasempour Z, Heidarzadeh A. Seroprevalence of COVID-19 virus infection in Guilan province, Iran [preprint]. Cold Spring Harbor: medRxiv; 2020.
  9. Bryan A, Pepper G, Wener MH, Fink SL, Morishima C, Chaudhary A, et al. Performance characteristics of the Abbott Architect SARS-CoV-2 IgG assay and seroprevalence in Boise, Idaho. J Clin Microbiol. 2020 Jul 23;58(8):e00941-20. PMID:32381641
  10. Stringhini S, Wisniak A, Piumatti G, Azman AS, Lauer SA, Baysson H, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. Lancet. 2020 Aug 1;396(10247):313–9. PMID:32534626
  11. Doi A, Iwata K, Kuroda H, Hasuike T, Nasu S, Kanda A, et al. Estimation of seroprevalence of novel coronavirus disease (COVID-19) using preserved serum at an outpatient setting in Kobe, Japan: a cross-sectional study [preprint]. Cold Spring Harbor: medRxiv; 2020.
  12. Erikstrup C, Hother CE, Pedersen OBV, Mølbak K, Skov RL, Holm DK, et al. Estimation of SARS-CoV-2 infection fatality rate by real-time antibody screening of blood donors. Clin Infect Dis. 2020 Jun 25; ciaa849. PMID:32584966
  13. Fontanet A, Tondeur L, Madec Y, Grant R, Besombes C, Jolly N, et al. Cluster of COVID-19 in northern France: a retrospective closed cohort study [preprint]. Cold Spring Harbor: medRxiv; 2020.
  14. Wu X, Fu B, Chen L, Feng Y. Serological tests facilitate identification of asymptomatic SARS-CoV-2 infection in Wuhan, China. J Med Virol. 2020 Apr 20;92(10):1795–6. PMID:32311142
  15. Slot E, Hogema BM, Reusken CBEM, Reimerink JH, Molier M, Karregat HM, et Herd immunity is not a realistic exit strategy during a COVID-19 outbreak [preprint]. Durham: Research Square; 2020. 25862/v1
  16. Streeck H, Schulte B, Kümmerer BM, Richter E, Höller T, Fuhrmann C, et al. Infection fatality rate of SARS-CoV-2 infection in a German community with a super- spreading event [preprint]. Cold Spring Harbor: medRxiv; 2020.
  17. Silveira MF, Barros AJD, Horta BL, Pellanda LC, Victora GD, Dellagostin OA, et al. Population-based surveys of antibodies against SARS-CoV-2 in Southern Nat Med. 2020 Aug;26(8):1196–9. PMID:32641783
  18. Thompson C, Grayson N, Paton RS, Lourenco J, Penman BS, Lee L. Neutralising antibodies to SARS coronavirus 2 in Scottish blood donors – a pilot study of the value of serology to determine population exposure [preprint]. Cold Spring Harbor: medRxiv; 2020.
  19. Bendavid E, Mulaney B, Sood N, Shah S, Ling E, Bromley-Dulfano R, et al. COVID- 19 Antibody Seroprevalence in Santa Clara County, California [preprint]. Cold Spring Harbor: medRxiv; 2020.
  20. Snoeck CJ, Vaillant M, Abdelrahman T, Satagopam VP, Turner JD, Beaumont K, et al. Prevalence of SARS-CoV-2 infection in the Luxembourgish population: the CON-VINCE study [preprint]. Cold Spring Harbor: medRxiv; 2020.
  21. Kraehling V, Kern M, Halwe S, Mueller H, Rohde C, Savini M, et al. Epidemiological study to detect active SARS-CoV-2 infections and seropositive persons in a selected cohort of employees in the Frankfurt am Main metropolitan area [preprint]. Cold Spring Harbor: medRxiv; 2020.
  22. Sood N, Simon P, Ebner P, Eichner D, Reynolds J, Bendavid E, et al. Seroprevalence of SARS-CoV-2-specific antibodies among adults in Los Angeles County, California, on April 10–11, 2020. JAMA. 2020 06 16;323(23):2425–7. PMID:32421144
  23. Rosenberg ES, Tesoriero JM, Rosenthal EM, Chung R, Barranco MA, Styer LM, et al. Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York. Ann Epidemiol. 2020 Aug;48:23–29.e4. PMID:32648546
  24. Ng D, Goldgof G, Shy B, Levine A, Balcerek J, Bapat SP, et al. SARS-CoV-2 seroprevalence and neutralizing activity in donor and patient blood from the San Francisco Bay Area [preprint]. Cold Spring Harbor: medRxiv; 2020.
  25. Hallal PC, Hartwig FP, Horta BL, Victora GD, Silveira MF, Struchiner CJ, et al. Remarkable variability in SARS-CoV-2 antibodies across Brazilian regions: nationwide serological household survey in 27 states [preprint]. Cold Spring Harbor: medRxiv; 2020.
  26. Jerkovic I, Ljubic T, Basic Z, Kruzic I, Kunac N, Bezic J, et al. SARS-CoV-2 antibody seroprevalence in industry workers in Split-Dalmatia and Sibenik-Knin County, Croatia [preprint]. Cold Spring Harbor: medRxiv; 2020.
  27. Reifer J, Hayum N, Heszkel B, Klagsbald I, Streva VA. SARS-CoV-2 IgG antibody responses in New York City [preprint]. Cold Spring Harbor: medRxiv; 2020.
  28. Emmenegger M, De Cecco E, Lamparter D, Jacquat RPB, Ebner D, et al. Early plateau of SARS-CoV-2 seroprevalence identified by tripartite immunoassay in a large population [preprint]. Cold Spring Harbor: medRxiv; 2020.
  29. Takita M, Matsumura T, Yamamoto K, Yamashita E, Hosoda K, Hamaki T, et al. Geographical profiles of COVID-19 outbreak in Tokyo: an analysis of the primary care clinic-based point-of-care antibody testing. J Prim Care Community Health. 2020 Jan-Dec;11:2150132720942695. PMID:32674696
  30. Crovetto F, Crispi F, Llurba E, Figueras F, Gomez-Roig MD, Gratacos E. Seroprevalence and clinical spectrum of SARS-CoV-2 infection in the first versus third trimester of pregnancy [preprint]. Cold Spring Harbor: medRxiv; 2020.
  31. Fiore JR, Centra M, De Carlo A, Granato T, Rosa A, Sarno M, et al. Results from a survey in healthy blood donors in South Eastern Italy indicate that we are far away from herd immunity to SARS-CoV-2. J Med Virol. 2020 Aug 13;jmv.26425. PMID:32790086
  32. Ling R, Yu Y, He J, Zhang J, Xu S, Sun R, et al. Seroprevalence and epidemiological characteristics of immunoglobulin M and G antibodies against SARS-CoV-2 in asymptomatic people in Wuhan, China [preprint]. Cold Spring Harbor: medRxiv; 2020.
  33. Chamie G, Marquez C, Crawford E, Peng J, Petersen M, Schwab D, et al. SARS- CoV-2 community transmission during shelter-in-place in San Francisco [preprint]. Cold Spring Harbor: medRxiv; 2020.
  34. Gomes CC, Cerutti C, Zandonade E, Maciel EKN, Carvalho de Alencar FE, Almada GL, et al. A population-based study of the prevalence of COVID-19 infection in Espírito Santo, Brazil: methodology and results of the first stage [preprint]. Cold Spring Harbor: medRxiv; 2020.
  35. Havers FP, Reed C, Lim T, Montgomery JM, Klena JD, Hall AJ, et al. Seroprevalence of Antibodies to SARS-CoV-2 in 10 Sites in the United States, March 23-May 12, 2020. JAMA Intern Med. 2020 Jul 21. PMID:32692365
  36. Pollán M, Pérez-Gómez B, Pastor-Barriuso R, Oteo J, Hernán MA, Pérez-Olmeda M, et al.; ENE-COVID Study Group. Prevalence of SARS-CoV-2 in Spain (ENE- COVID): a nationwide, population-based seroepidemiological study. Lancet. 2020 08 22;396(10250):535–44. PMID:32645347
  37. Feehan AK, Fort D, Garcia-Diaz J, Price-Haywood E, Velasco C, Sapp E, et al. Seroprevalence of SARS-CoV-2 and infection fatality ratio, Orleans and Jefferson Parishes, Louisiana, USA, May 2020. Emerg Infect Dis. 2020 Jul 30;26(11). PMID:32731911
  38. Herzog S, De Bie J, Abrams S, et al. Seroprevalence of IgG antibodies against SARS coronavirus 2 in Belgium – a prospective cross sectional study of residual samples [preprint]. Cold Spring Harbor: medRxiv; 2020.
  39. Fontanet A, Grant R, Tondeur L, et al. SARS-CoV-2 infection in primary schools in northern France: A retrospective cohort study in an area of high transmission [preprint]. Cold Spring Harbor: medRxiv; 2020.
  40. Xu X, Sun J, Nie S, Li H, Kong Y, Liang M, et al. Seroprevalence of immunoglobulin M and G antibodies against SARS-CoV-2 in China. Nat Med. 2020 08;26(8):1193– 5. PMID:32504052
  41. Amorim Filho L, Szwarcwald CL, Mateos SOG, Leon ACMP, Medronho RA, Veloso VG, et al.; Grupo Hemorio de Pesquisa em Covid-19. Seroprevalence of anti- SARS-CoV-2 among blood donors in Rio de Janeiro, Brazil. Rev Saude 2020;54:69. PMID:32638883
  42. Tess BH, Granato CFH, Alves MC, Pintao MC, Rizzatti E, Nunes MC, et al. SARS- CoV-2 seroprevalence in the municipality of São Paulo, Brazil, ten weeks after the first reported case [preprint]. Cold Spring Harbor: medRxiv; 2020.
  43. Torres JP, Piñera C, De La Maza V, Lagomarcino AJ, Simian D, Torres B, et al. SARS-CoV-2 antibody prevalence in blood in a large school community subject to a Covid-19 outbreak: a cross-sectional study. Clin Infect Dis. 2020 Jul 10;ciaa955. PMID:32649743
  44. Uyoga S, Adetifa IMO, Karanja HK, Nyagwange J, Tuju J, Wankiku P, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Kenyan blood donors [preprint]. Cold Spring Harbor: medRxiv; 2020.
  45. Nesbitt DJ, Jin D, Hogan JW, Chan PA, Simon MJ, Vargas M, et al. Low seroprevalence of SARS-CoV-2 in Rhode Island blood donors determined using multiple serological assay formats [preprint]. Cold Spring Harbor: medRxiv;
  46. McLaughlin CC, Doll MK, Morrison KT, McLaughlin WL, O’Connor T, Sholukh AM, et al. High community SARS-CoV-2 antibody seroprevalence in a ski resort community, Blaine County, Idaho, US. preliminary results [preprint]. Cold Spring Harbor: medRxiv; 2020.
  47. Figar S, Pagotto V, Luna L, Salto J, Manslau MW, Mistchenko AS, et al. Community- level SARS-CoV-2 seroprevalence survey in urban slum dwellers of Buenos Aires City, Argentina: a participatory research [preprint]. Cold Spring Harbor: medRxiv; 2020.
  48. Nawa N, Kuramochi J, Sonoda S, Yamaoka Y, Nukui Y, Miyazaki Y, et al. Seroprevalence of SARS-CoV-2 IgG antibodies in Utsunomiya City, Greater Tokyo, after first pandemic in 2020 (U-CORONA): a household- and population- based study [preprint]. Cold Spring Harbor: medRxiv; 2020.
  49. Nisar I, Ansari N, Amin M, Khalid F, Hotwani A, Rehman N, et al. Serial population based serosurvey of antibodies to SARS-CoV-2 in a low and high transmission area of Karachi, Pakistan [preprint]. Cold Spring Harbor: medRxiv; 2020.
  50. Skowronski DM, Sekirov I, Sabaiduc S, Zou M, Morshed M, Lawrence D, et al. Low SARS-CoV-2 sero-prevalence based on anonymized residual sero-survey before and after first wave measures in British Columbia, Canada, March–May 2020 [preprint]. Cold Spring Harbor: medRxiv; 2020.
  51. Abu Raddad LJ, Chemaitelly H, Ayoub HH, Al Kanaani Z, Al Khal A, Al Kuwari E, et al. Characterizing the Qatar advanced-phase SARS-CoV-2 epidemic [preprint]. Cold Spring Harbor: medRxiv; 2020.
  52. Petersen MS, Strøm M, Christiansen DH, Fjallsbak JP, Eliasen EH, Johansen M, et al. Seroprevalence of SARS-CoV-2-Specific Antibodies, Faroe Islands. Emerg Infect Dis. 2020 Jul 29;26(11). PMID:32726200
  53. Biggs HM, Harris JB, Breakwell L, Dahlgren FS, Abedi GR, Szablewski CM, et al.; CDC Field Surveyor Team. Estimated community seroprevalence of SARS-CoV-2 antibodies – two Georgia counties, April 28–May 3, 2020. MMWR Morb Mortal Wkly Rep. 2020 Jul 24;69(29):965–70. PMID:32701941
  54. Menachemi N, Yiannoutsos CT, Dixon BE, Duszynski TJ, Fadel WF, Wools- Kaloustian KK, et al. Population point prevalence of SARS-CoV-2 infection based on a statewide random sample – Indiana, April 25–29, 2020. MMWR Morb Mortal Wkly Rep. 2020 Jul 24;69(29):960–4. PMID:32701938
  55. Chang L, Hou W, Zhao L, Zhang Y, Wang Y, Wu L, et al. The prevalence of antibodies to SARS-CoV-2 among blood donors in China [preprint]. Cold Spring Harbor: medRxiv; 2020.
  56. Meyers K, Liu L, Lin W-H, Luo Y, Yin M, Wu Y, et al. Antibody testing documents the silent spread of SARS-CoV-2 in New York prior to the first reported case [preprint]. Durham: Research Square; 2020.
  57. Merkely B, Szabó AJ, Kosztin A, Berényi E, Sebestyén A, Lengyel C, et al.; HUNgarian COronaVirus-19 Epidemiological Research (H-UNCOVER) investigators. Novel coronavirus epidemic in the Hungarian population, a cross- sectional nationwide survey to support the exit policy in Hungary. Geroscience. 2020 Aug;42(4):1063–74. PMID:32677025
  58. Gudbjartsson DF, Norddahl GL, Melsted P, Gunnarsdottir K, Holm H, Eythorsson E, et al. Humoral immune response to SARS-CoV-2 in Iceland. N Engl J Med. 2020 Sep 1.NEJMoa2026116. [online ahead of print]. PMID:32871063
  59. Noh JY, Seo YB, Yoon JG, Seong H, Hyun H, Lee J, et al. Seroprevalence of anti- SARS-CoV-2 antibodies among outpatients in southwestern Seoul, Korea. J Korean Med Sci. 2020 Aug 24;35(33):e311. PMID:32830472
  60. Xu R, Huang J, Duan C, Liao Q, Shan Z, Wang M, et al. Low prevalence of antibodies against SARS-CoV-2 among voluntary blood donors in Guangzhou, China. J Med Virol. 2020 Aug 19.jmv.26445. [online ahead of print]. PMID:32813273
  61. Malani A, Shah D, Kang G, Lobo GN, Shastri J, Mohanan M, et al. Seroprevalence of SARS-CoV-2 in slums and non-slums of Mumbai, India, during June 29–July 19, 2020 [preprint]. Cold Spring Harbor: medRxiv; 2020.
  62. Bogogiannidou Z, Vontas A, Dadouli K, Kyritsi MA, Soteriades S, Nikoulis DJ, et al. Repeated leftover serosurvey of SARS-CoV-2 IgG antibodies, Greece, March and April 2020. Euro Surveill. 2020 Aug;25(31):2001369. 7917.ES.2020.25.31.2001369 PMID:32762796
  63. Feehan AK, Velasco C, Fort D, Burton JH, Price-Haywood E, Katzmarzyk PT, et Racial and workplace disparities in seroprevalence of SARS-CoV-2 in Baton Rouge, Louisiana, 4 July 15–31, 2020 [preprint]. Cold Spring Harbor: medRxiv; 2020.
  64. Westerhuis BM, de Bruin E, Chandler FD, Ramakers CRB, Okba NMA, Li W, et al. Homologous and he 1 terologous antibodies to coronavirus 229E, NL63, OC43, HKU1, SARS, MERS and SARS-CoV-2 antigens in an age stratified cross- sectional serosurvey in a large tertiary hospital in The Netherlands [preprint]. Cold Spring Harbor: medRxiv; 2020.
  65. Ward H, Atchinson C, Whitaker M, Ainslie KED, Elliott J, Okell L, et al. Antibody prevalence for SARS-CoV-2 following the peak of the pandemic in England: REACT2 study in 100,000 adults [preprint]. Cold Spring Harbor: medRxiv;
  66. Javed W, Baqar J, Abidi SHB, Farooq Sero-prevalence findings from metropoles in Pakistan: implications for assessing COVID-19 prevalence and case-fatality within a dense, urban working population [preprint]. Cold Spring Harbor: medRxiv; 2020.
  67. Khan MS, Qurieshi MA, Haq I, Majid S, Akbar A. Seroprevalence of SARS-CoV-2 specific IgG antibodies in District Srinagar, northern India – a cross-sectional study [preprint].Cold Spring Harbor: bioRxiv; 2020.
  68. Da Silva A, Lima-Neto L, de Azevedo C, da Costa L, Braganca M, Filho A, et al. Population-based seroprevalence of SARS-CoV-2 is more than halfway through the herd immunity threshold in the State of Maranhão, Brazil [preprint]. Cold Spring Harbor: medRxiv; 2020.
  69. [Weekly report of the population serology survey of the corona epidemic.] Helsinki: Finland Department of Health and Welfare; 2020. [Finnish]. Available from: [cited 2020 Jul 12].
  70. [First results on antibodies after review of covid-19 in blood donors.] Solna: Swedish Public Health Agency; 2020. [Swedish]. Available from: covid-19-hos-blodgivare/ [cited 2020 Jul 12].
  71. [Studie SARS-CoV-2-CZ-Preval.] Prague: Institute of Health Information and Statistics of the Czech Republic; 2020. [Czech]. Available from: https://covid- [cited 2020 Jul 12].
  72. Jaffe-Hoffman M. Coronavirus herd immunity? Not in Israel, according to a serological study. Jerusalem Post. 2020 Jun 2. Available from: [cited 2020 Jul 12].
  73. First study carried out on herd immunity of the population in the whole territory of Slovenia. Ljubljana: Republic of Slovenia; 2020. Available from: [cited 2020 Jul 12].
  74. [Popova declared immunity to coronavirus in 14% of those tested]. Interfax. 2020 Jun
    [Russian]. Available from: [cited 2020 Aug 12].
  75. Gul A. Ten million Afghans likely infected and recovered: COVID-19 survey. VOA. 2020 Aug 5. Available from: [cited 2020 Aug 12].
  76. Preliminary results of seroprevalence study in Tbilisi: 10 out of 1068 residents have coronavirus antibodies. 2020 Jun 29. Available from: [cited 2020 Aug 12].
  77. Genot L. In Brazil, COVID-19 hitting young people harder. The Jakarta Post. 2020 May 22. Available from: [cited 2020 Jul 12].
  78. COVID-19 pandemic in Croatia [internet]. Wikipedia; 2020. Available from [cited 2020 Jul 12].
  79. COVID-19 coronavirus pandemic [internet]. Dover: Worldometer; 2020. Available from: [cited 2020 Sep 12].
  80. Ioannidis JPA, Axfors C, Contopoulos-Ioannidis DG. Population-level COVID-19 mortality risk for non-elderly individuals overall and for non-elderly individuals without underlying diseases in pandemic epicenters. Environ Res. 2020 Sep;188:109890. PMID:32846654
  81. Booth R. Half of coronavirus deaths happen in care homes, data from EU The Guardian. 2020 Apr 13. Available from: [cited 2020 Apr 27].
  82. American Geriatrics Society. American Geriatrics Society Policy Brief: COVID-19 and nursing homes. J Am Geriatr Soc. 2020 May;68(5):908–11. PMID:32267538
  83. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. 2020 Mar 17;323(11):1061–9. PMID:32031570
  84. Boccia S, Ricciardi W, Ioannidis JPA. What other countries can learn from Italy during the COVID-19 pandemic. JAMA Intern Med. 2020 Jul 1;180(7):927–8. PMID:32259190
  85. Brufsky A. Distinct viral clades of SARS-CoV-2: Implications for modeling of viral spread. J Med Virol. 2020 Apr 20;92(9):1386–90. PMID:32311094
  86. Rosado J, Cockram C, Merkling SH, Demeret C, Meola A, Kerneis S, et al. Serological signatures of SARS-CoV-2 infection: implications for antibody-based diagnostics [preprint]. Cold Spring Harbor: medRxiv; 2020.
  87. Long QX, Tang XJ, Shi QL, Li Q, Deng HJ, Yuan J, et al. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections. Nat Med. 2020 Aug;26(8):1200–4. PMID:32555424
  88. Wu F, Wang A, Liu M, Wang Q, Chen J, Xia S, et al. Neutralizing antibody responses to SARS-CoV-2 in a COVID-19 recovered patient cohort and their implications [preprint]. Cold Spring Harbor: medRxiv; 2020.
  89. Seow J, Graham C, Merrick B, Acors S, Steel KJA, Hemmings O, et al. Longitudinal evaluation and decline of antibody responses in SARS-CoV-2 infection [preprint]. Cold Spring Harbor: medRxiv; 2020.
  90. Edouard S, Colson P, Melenotte C, De Pinto F, Thomas L, La Scola B, et al. Evaluating the serological status of COVID-19 patients using an indirect immunofluorescent assay, France [preprint]. Cold Spring Harbor: medRxiv;
  91. Solbach W, Schiffner J, Backhaus I, Burger D, Staiger R, Tiemer B, et al. Antibody profiling of COVID-19 patients in an urban low-incidence region in northern Germany [preprint]. Cold Spring Harbor: medRxiv; 2020.
  92. Ioannidis JPA. Global perspective of COVID-19 epidemiology for a full-cycle pandemic. Eur J Clin Invest. 2020 Oct 7;e13421. PMID:33026101
  93. Kikkert M. Innate immune evasion by human respiratory RNA viruses. J Innate Immun. 2020;12(1):4–20. PMID:31610541
  94. Krammer F. The human antibody response to influenza A virus infection and vaccination. Nat Rev Immunol. 2019 Jun;19(6):383–97. PMID:30837674
  95. Cervia C, Nilsson J, Zurbuchen Y, Valaperti A, Schreiner J, Wolfensberger A, et Systemic and mucosal antibody secretion specific to SARS-CoV-2 during mild versus severe COVID-19 [preprint]. Cold Spring Harbor: bioRxiv. 2020.
  96. Gallais F, Velay A, Wendling M-J, Nazon C, Partisani M, Sibilia J, et al. Intrafamilial exposure to SARS-CoV-2 induces cellular immune response without seroconversion [preprint]. Cold Spring Harbor: medRxiv; 2020.
  97. Sekine T, Perez-Potti A, Rivera-Ballesteros O, Strålin K, Gorin J-B, Olsson A, et ; Karolinska COVID-19 Study Group. Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19. Cell. 2020 10 1;183(1):158– 168.e14. PMID:32979941