Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
John P A Ioannidis
Infection fatality rate of COVID-19
This online first version has been peer-reviewed, accepted and edited,
but not formatted and finalized with corrections from authors and proofreaders
Infection fatality rate of COVID-19 inferred from
seroprevalence data
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: jioannid@stanford.edu).
(Submitted: 13 May 2020 - Revised version received: 13 September 2020 - Accepted: 15 September 2020
- Published online: 14 October 2020)
Abstract
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.
Introduction
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,
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Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
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.
Methods
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.
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Article ID: BLT.20.265892
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,
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Article ID: BLT.20.265892
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
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Article ID: BLT.20.265892
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.
Results
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:
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,
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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
######).
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
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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.
Discussion
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
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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.
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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
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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.
Funding:
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.
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Table 1. Eligible seroprevalence studies on COVID-19 published or deposited as preprints as of 9 September 2020:
dates, sampling and recruitment
Author
Country (location)
Dates
Sampling and recruitment
Figar et al.47
Argentina (Barrio
10-26 June
Probabilistic sampling of a slum neighbourhood, sampling from people 14 years or
Padre Mugica)
older across households
Herzog et al.38
Belgium
30 March-5 April and
Residual sera from 10 private diagnostic laboratories in Belgium, with fixed numbers
20-26 April
per age group, region and periodical sampling, and stratified by sex
Hallal et al.25
Brazil
15-22 May
Sampling from 133 cities (the main city in each region), selecting 25 census tracts
with probability proportionate to size in each sentinel city, and 10 households at
random in each tract. Aiming for 250 participants per city
Gomes et al.34
Brazil (Espirito Santo)
13-15 May
Cross-section of major municipalities with houses as the sampling units
Da Silva et al.68
Brazil (Maranhao)
27 July-8 August
Three-stage cluster sampling stratified by four state regions in the state of
Maranhao; the estimates took clustering, stratification and non-response into
account
Amorim Filho et
Brazil (Rio de Janeiro)
14-27 April (eligible:
Blood donors without flulike symptoms within 30 days of donation; had close contact
al.41
24-27 April)
with suspected or confirmed COVID-19 cases in the 30 days before donation; or
had travelled abroad in the past 30 days
Silveira et al.17
Brazil (Rio Grande do
9-11 May (third round,
Multistage probability sampling in each of nine cities to select 500 households, from
Sul)
after 11-13 April, and
which one member was randomly chosen for testing
25-27 April)
Tess et al.42
Brazil (Sao Paulo)
4-12 May
Randomly selected adults and their cohabitants sampled from six districts of Sao
Paulo City with high numbers of cases
Skowronski et
Canada (British
15-27 May (after
Specimens from patients attending one of about 80 diagnostic service centres of the
al.50
Columbia)
baseline in 5-13
only outpatient laboratory network in the Lower Mainland
March)
Torres et al.43
Chile (Vitacura)
4-19 May
Classroom stratified sample of children and all staff in a community placed on
quarantine after school outbreak
Chang et al.55
China
January-April weekly:
38 144 healthy blood donors in Wuhan, Shenzhen and Shijiazhuang who met the
3-23 February
criteria for blood donation during the COVID-19 pandemic in China
(Wuhan); 24
February-15 March
(Shenzhen); 10
February-1 March
(Shijiazhuang)
Wu et al.14
China (Wuhan)
3-15 April
People applying for a permission to resume work (n = 1 021) and hospitalized
patients (n = 381)
Ling et al.32
China (Wuhan)
26 March-28 April
Age 16-64 years, going back to work, with no fever, headache or other symptoms
of COVID-19
Xu et al.60
China (Guangzhou)
23 March-2 April
Healthy blood donors in Guangzhou
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Xu et al.40
China (several regions)
30 March-10 April
Voluntary participation by public call for haemodialysis patients (n = 979 in
Zingzhou, Ubei and n = 563 in Guangzhou/Foshun, Guangdong) and outpatients in
Chingqing (n = 993), and community residents in Chengdu, Sichuan (n = 9 442), and
required testing for factory workers in Guangzhou, Guandong (n = 442)
Jerkovic et al.26
Croatia
23-28 April
DIV Group factory workers in Split and Sibenik-Knin invited for voluntary testing
Erikstrup et al.12
Denmark
6 April-3 May
All Danish blood donors aged 17-69 years giving blood. Blood donors are healthy
and must comply with strict eligibility criteria; they must self-defer for two weeks if
they develop fever with upper respiratory symptoms
Petersen et al.52
Denmark (Faroe
27 April-1 May
1500 randomly selected residents invited to participate, samples collected from
Islands)
1 075
Fontanet et al.39
France (Crepy-en-
28-30 April
Pupils, their parents and relatives, and staff of primary schools exposed to SARS-
Valois)
CoV-2 in February and March 2020 in a city north of Paris
Fontanet et al.13
France (Oise)
30 March-4 April
Pupils, their parents and siblings, as well as teachers and non-teaching staff of a
high-school
Streeck et al.16
Germany (Gangelt)
30 March-6 April
600 adults with different surnames in Gangelt were randomly selected; all
household members were asked to participate in the study
Kraehling et al.21
Germany (Frankfurt)
6-14 April
Employees of Infraserv Höchst, a large industrial site operator in Frankfurt am Main.
No exclusion criteria
Bogogiannidou et
Greece
March and April (April
Leftover blood samples collected from a nationwide laboratory network, including
al.62
data used)
both private and public hospital laboratories (27 laboratories in total)
Merkely et al.57
Hungary
1-16 May
Representative sample (n = 17 787) of the Hungarian population 14 years living in
private households ( 8 283 810)
Gudbjatsson et
Iceland
Several cohorts
30 576 people in Iceland, including those documented to be infected, those
al.58
between April and
quarantined and people not known to have been exposed.
Junea
Malani et al.61
India (Mumbai)
29 June-19 July
Geographically-spaced community sampling of households, one individual per
household was tested in slum and non-slum communities in three wards, one each
from the three main zones of Mumbai
Khan et al.67
India (Srinagar)
1-15 July
Adults (> 18 years) who visited selected hospitals across the Srinagar District
Shakiba et al.8
Islamic Republic of
April (until 21 April)
Population-based cluster random sampling design through telephone call invitation,
Iran (Guilan)
household-based
Fiore et al.31
Italy (Apulia)
1-31 May
Blood donors 18-65 years old free of recent symptoms possibly related to COVID-
19, no close contact with confirmed cases, symptom-free in the preceding 14 days,
no contact with suspected cases
Doi et al.11
Japan (Kobe)
31 March-7 April
Randomly selected patients who visited outpatient clinics and received blood testing
for any reason. Patients who visited the emergency department or the designated
fever consultation service were excluded
Takita et al.29
Japan (Tokyo)
21 April-20 May
Two community clinics in the main railway stations in Tokyo (Navitas Clinic Shinjuku
and Tachikawa)
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Article ID: BLT.20.265892
Nawa et al.48
Japan (Utsunomiya
14 June-5 July
Invitations enclosed with a questionnaire were sent to 2 290 people in 1 000
City)
households randomly selected from Utsunomiya City’s basic resident registry; 742
completed the study
Uyoga et al.44
Kenya
30 April-16 June
Residual blood donor serum samples from donors 16-65 years in four sites
(~90% of samples in
(Mombasa, Nairobi, Eldoret and Kisumu)
last 30 days)
Snoeck et al.20
Luxembourg
16 April-5 May
Representative sample (no details how ensured), 1 807 of 2000 contacted provided
data, were < 79 years and had serology results
Slot et al.15
Netherlands
1-15 April
Blood donors. Donors must be completely healthy, but they may have been ill in the
past, provided that they recovered at least 2 weeks before
Westerhuis et
Netherlands
Early March and early
Left-over plasma samples from patients of nine age categories in Erasmus Medical
al.64
(Rotterdam)
April
Center in Rotterdam: 879 samples in early March and 729 in early April)
Nisar et al.49
Pakistan (Karachi)
25 June-11 July (after
Cross-sectional household surveys in a low- (district Malir) and high-transmission
baseline on 15-25
(district East) area of Karachi with households selected using simple random
April)
sampling (Malir) and systematic random sampling (East)
Javed et al.66
Pakistan (urban
06-Jul
Adult, working population aged 18-65 years, recruited from dense, urban
Karachi, Lahore,
workplaces including factories, businesses, restaurants, media houses, schools,
Multan, Peshawar and
banks, hospitals (health-care providers), and from families of positive cases in cities
Quetta)
in Pakistan
Abu Raddad et
Qatar
12 May-12 July
Convenience sample of residual blood specimens collected for routine clinical
al.51
(highest
screening or clinical management from 32 970 outpatient and inpatient departments
seroprevalence on 12-
for a variety of health conditions (n = 937 in 12-31 May)
31 May)
Noh et al.59
Republic of Korea
25-29 May
Outpatients who visited two hospitals in south-west Seoul which serve six
administrative areas
Pollan et al.36
Spain
27 April-11 May
35 883 households selected from municipal rolls using two-stage random sampling
stratified by province and municipality size, with all residents invited to participate
(75.1% of all contacted individuals participated)
Crovetto et al.30
Spain (Barcelona)
14 April-5 May
Consecutive pregnant women for first trimester screening or delivery in two
hospitals
Stringhini et al.10
Switzerland (Geneva)
6 April-9 May (5
Randomly selected previous participants of the Bus Santé study with an email (or
consecutive weeks)
telephone contact, if email unavailable); participants were invited to bring all
members of their household aged 5 years and older
Emmenegger et
Switzerland (Zurich)
Prepandemic until
Patients at the University Hospital of Zurich and blood donors in Zurich and Lucerne
al.28
June (patients) and
May (blood donors)
Ward et al.65
United Kingdom
20 June-13 July
Random population sample of 100 000 adults over 18 years
(England)
Thompson et al.18
United Kingdom
21-23 March
Blood donors. Donors should not have felt unwell in the past 14 days; some other
(Scotland)
deferrals also applied regarding travel and COVID-19 symptoms
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Article ID: BLT.20.265892
Havers et al.35
USA (10 states)
23 March-1 April
Convenience samples using residual sera obtained for routine clinical testing
(Washington, Puget
(screening or management) by two commercial laboratory companies
Sound and New York,
New York City), 1-8
April (Louisiana), 5-10
April (Florida, south),
13-25 April
(Pennsylvania,
Philadelphia,
metropolitan area),
20-26 April (Missouri),
23-27 April (California,
San Francisco Bay
Area), 20 April-3 May
(Utah), 26 April-3 May
(Connecticut), 30
April-12 May
(Minnesota,
Minneapolis)
Ng et al.24
USA (California, Bay
March
1000 blood donors in diverse Bay Area locations (excluding those with self-reported
Area)
symptoms or abnormal vital signs)
Sood22
USA (California, Los
10-14 April
Proprietary database representative of the county. A random sample of these
Angeles)
residents was invited, with quotas for enrolment for subgroups based on age, sex,
race and ethnicity distribution
Chamie et al.
33
USA (California, San
25-28 April
United States census tract 022 901 population-dense area (58% Latin American) in
Francisco)
San Francisco Mission district, expanded to neighbouring blocks on 28 April
Bendavid et al.19
USA (California, Santa
2-3 April
Facebook advertisement with additional targeting by zip code
Clara)
Biggs et al.53
USA (Georgia, DeKalb
28 April-3 May
Two-stage cluster sampling design used to randomly select 30 census blocks in
and Fulton)
DeKalb county and 30 census blocks in Fulton county, with a target of seven
participating households per census block
McLaughlin et
USA (Idaho, Blaine
4-19 May
Volunteers who registered via a secure web link, using prestratification weighting to
al.46
county)
the population distribution by age and sex within each zip code
Bryan et al.9
USA (Idaho, Boise)
Late April
People from the Boise, Idaho metropolitan area, part of the Crush the Curve
initiative
Menachemi et
USA (Indiana)
25-29 April
Stratified random sampling among all persons aged 12 years using Indiana’s 10
al.54
public health preparedness districts as sampling strata
Feehan et al.63
USA (Louisiana, Baton
15-31 July
Representative sample in a method developed by Public Democracy
Rouge)
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Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
Feehan et al.37
USA (Louisiana,
9-15 May
Pool of potential participants reflecting the demographics of the parishes was based
Orleans and Jefferson
on 50 characteristics, then a randomized subset of 150 000 people was selected,
Parish)
and 25 000 were approached with digital apps, and 2 640 were recruited
Rosenberg et
USA (New York)
19-28 April
Convenience sample of people 18 years living in New York State, recruited
al.23
consecutively on entering 99 grocery stores and through an in-store flyer
Meyers et al.56
USA (New York)
2-30 March (Columbia
Discarded clinical samples in Columbia Medical Center, New York City (n = 814 in
University Medical
24 February-30 March, 742 of those in the period 2-30 March) and samples from
Center, New York
CareMount central laboratory (960 samples on 13 and 14 March, 505 samples on
City); 13-28 March
20/21 March, and 376 samples on 27/28 March) from its network of clinics in five
(CareMount central
counties north of New York City
laboratory)
Reifer et al.27
USA (New York,
Early May
Patients seen in an urgent care facility in Brooklyn
Brooklyn)
Nesbitt et al.45
USA (Rhode Island)
27 April-11 May
Consecutive blood donors
COVID-19: coronavirus disease-19; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.
a
Sample collection time for some sub-cohorts may have exceeded 1 month,
but more than half of the cases were already documented by polymerase
chain reaction testing before any antibody testing and the last death occurred on 20 April.
Note:
Some studies included additional data sets that did not fulfil the
eligibility criteria (e.g. had sample size < 500 or were health-care
workers) and they
are not presented here.
Page 24 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
Table 2. Sample size, types of antibodies assessed and population size in the
studies included to assess COVID-19 infection fatality rate, 2020
Country (location)
Sample sizea, no.
Antibody
Population,b no.
% of population < 70
yearsc
Argentina (Barrio Padre
873
IgG
49 983
99
Mugica)47
Belgium38
3 391 (20-26 April)
IgG
11 589 623
86
Brazil (133 cities)25
24 995
IgG and IgM
74 656 499
94 (Brazil)
Brazil (Espirito Santo)34
4 608
IgG and IgM
4 018 650
94 (Brazil)
Brazil (Maranhao)68
3 156
IgG and IgM
7 114 598
92
Brazil (Rio de Janeiro), blood
669 (24-27 April)
IgG and IgM
17 264 943
94 (Brazil)
donors41
Brazil (Rio Grande do Sul)17
4 500
IgG
11 377 239
91
Brazil (Sao Paulo)42
517
IgG and IgM
298 240 (6 districts)
94 (Brazil)
Canada (British Columbia)50
885
IgG, IgM and IgA
5 071 000
94
Chile (Vitacura)43
1 244
IgG and IgM
85 000
92 (Chile)
China, blood donors55
Wuhan
930 (3-23 February)
IgG and IgM
11 210 000
93 (China)
Shenzhen
3 507 (24 February-
IgG and IgM
13 030 000
93 (China)
15 March)
Shijiazhuang
6 455 (10 February-1
IgG and IgM
11 030 000
93 (China)
March)
China (Wuhan)14
1 401
IgG and IgM
11 080 000
93 (China)
China (Wuhan)32
1 196 (4-8 April)
IgG and IgM
11 080 000
93 (China)
China (Guangzhou), blood
2 199
IgG, IgM and IgA
115 210 000 (Guangdong)
93 (China)
donors60
China (several regions)40
Hubei (not Wuhan)
979
IgG and IgM
48 058 000
93 (China)
Chongqing
993
IgG and IgM
31 243 200
93 (China)
Sichuan
9 442
IgG and IgM
83 750 000
93 (China)
Guangdong
1 005
IgG and IgM
115 210 000
93 (China)
Croatia26
1 494
IgG and IgM
4 076 000
86
Denmark blood donors12
20 640
IgG and IgM
5 771 876
86
Denmark (Faroe Islands)52
1 075
IgG and IgM
52 428
88
France (Crepy-en-Valois)39
1 340
IgG
5 978 000 (Hauts-de-France)
89
France (Oise)13
661
IgG
5 978 000 (Hauts-de-France)
89
Germany (Gangelt)16
919
IgG and IgA
12 597
86
Germany (Frankfurt)21
1 000
IgG
2 681 000d
84 (Germany)
Greece62
6 586 (4 511 in April)
IgG
10 412 967
84
Hungary57
10 504
IgG (also had PCR)
9 657 451
88
Iceland58
30 576
Pan-Ig
366 854
90
India (Mumbai)61
6 904 (4 202 in
IgG
1 414 917 (705 523 in slums,
98
slums, 2 702 not in
709 394 in non-slums) in the 3
slums)
ward areas
India (Srinagar)67
2 906
IgG
1 500 000
97
Islamic Republic of Iran
551
IgG and IgM
2 354 848
95
(Guilan)8
Italy (Apulia), blood donors31
909
IgG and /IgM
4 029 000
84
Japan (Kobe)11
1 000
IgG
1 518 870
79 (Japan)
Japan (Tokyo)29
1 071
IgG
13 902 077
79 (Japan)
Japan (Utsunomiya City)48
742
IgG
518 610
79 (Japan)
Kenya, blood donors44
3 098
IgG
47 564 296
99
Luxembourg20
1 807
IgG and IgAe
615 729
90
Netherlands blood donors15
7 361
IgG, IgM and IgA
17 097 123
86
Netherlands (Rotterdam)64
729 (early April)
IgG
17 097 123 (Netherlands)
86
Pakistan (Karachi)49
1 004
IgG and IgM
16 700 000
98 (Pakistan)
Pakistan (urban)66
24 210
IgG and IgM
79 000 000 (urban)
98
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Article ID: BLT.20.265892
Qatar51
937
IgG
2 800 000
99
Republic of Korea59
1 500
IgG
2 667 341
90 (southern
Republic of Korea)
Spain36
61 075
IgG
46 940 000
85
Spain (Barcelona)30
874
IgG, IgM and IgA
7 566 000 (Catalonia)
86
Switzerland (Geneva)10
577 (20-27 April)
IgG
500 000
88
Switzerland (Zurich)28
1 644 patients (1-15
IgG
1 520 968 (Zurich canton)
88
April)
Switzerland (Zurich)28
1 640 blood donors
IgG
1 930 525 (Zurich and Lucerne)
88
(May)
United Kingdom (England)65
109 076
IgG
56 287 000
86
United Kingdom (Scotland),
500
IgG
5 400 000
88
blood donors18
USA (10 states)35
Washington, Puget Sound
3 264
Pan-Ig
4 273 548
90 (Washington)
Utah
1 132
Pan-Ig
3 282 120
92
New York, New York City
2 482
Pan-Ig
9 260 870
89
Missouri
1 882
Pan-Ig
6 110 800
88
Florida, south
1 742
Pan-Ig
6 345 345
86 (Florida)
Connecticut
1 431
Pan-Ig
3 562 989
88
Louisiana
1 184
Pan-Ig
4 644 049
92 =
California, San Francisco Bay
1 224
Pan-Ig
2 173 082
90
Pennsylvania, Philadelphia
824
Pan-Ig
4 910 139
90
Minnesota, Minneapolis
860
Pan-Ig
3 857 479
90
USA (California, Bay Area)24
1 000
IgG
7 753 000
90
USA (California, Los
863
IgG and IgM
7 892 000
92
Angeles)22
USA (California, San
3 953
IgG (also PCR
5174 (in census tract 022 901)
95
Francisco)33
testing)
USA (California, Santa
3 300
IgG and IgM
1 928 000
90
Clara)19
USA (Idaho, Boise)9
4 856
IgG
481 587 (Ada county)
92
USA (Georgia, DeKalb and
696
Total Ig
1 806 672
88 (Georgia)
Fulton counties)53
USA (Idaho, Blaine county)46
917
IgG
23 089
92
USA (Indiana)54
3 629
IgG (also RT-PCR
6 730 000
89
done)
USA (Louisiana, Baton
138
IgG
699 200 (East Baton Rouge,
92 (Louisiana)
Rouge)63
West Baton Rouge, Ascension,
Livingston)
USA (Louisiana, Orleans and
2 640
IgG
825 057
92 (Louisiana)
Jefferson Parish)37
USA (New York)23
15 101
IgG
19 450 000
90
USA, New York56
Columbia University Medical
742 (2-30 March)
IgG and IgM
9 260 870
89
Center, New York City
CareMount central laboratory,
1 841
IgG and IgM
10 189 130 (New York state
89
five New York state counties
excluding New York City)
USA (New York, Brooklyn)27
11 092
IgG
2 559 903
91
USA (Rhode Island), blood
1 996
IgG and IgM
1 059 000
88
donors45
COVID-19: coronavirus disease-19; Ig: immunoglobin; RT-PCR: real-time polymerase chain reaction.
a Dates in brackets are the specific dates used when seroprevalence was evaluated at multiple consecutive time points
or setting.
b Some studies focused on age-restricted populations of the specific location under study, for example: people 17-70
years in the Denmark blood donor study (n = 3 800 000); people 18-79-years in the Luxembourg study (n = 483 000);
Page 26 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
people < 70 years in the Netherlands blood donor study (n = 13 745 768); people 18 years in the New York state study
(n = 15 280 000); people > 19 years in the Utah population of the 10-state United States study (n = 2 173 082); people
18 years in Blaine county, Idaho (n = 17 611); people 15-64 years in the Kenya blood donor study (n = 27 150 165);
people > 14 years living in private premises in Hungary; people > 18 years (n = 551 185) in Baton Rouge, Louisiana;
people 18-65 years working in urban locations in Pakistan (n = 22 100 000); and people > 18 years in Srinagar District,
India (n = 1 020 000). In this table and subsequent analyses, the entire population in the location is considered for
consistency across studies.
c Information in parenthesis specify the population.
d Participants were recruited from a large number of districts, but most districts had very few participants; here I included
the population of the nine districts with > 1:10 000 sampling ratio (846/1000 participants came from these nine districts).
e Considered positive if both IgG and IgA were positive; in the other studies, detection of any antibody was considered
positive.
Page 27 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
Table 3. Prevalence of COVID-19 and estimated number of people infected, 2020
Country (location)
Seroprevalence (%)
Estimated no. of
Crude
Adjusted (adjustments)
people infected
Argentina (Barrio Padre Mugica)47
ND
53.4 (age, sex, household, non-response)
26 691
Belgium38
5.7
6.0 (sampling, age, sex, province)
695 377
Brazil (133 cities)25
1.39
1.62 overall, varying from 0 to 25.0 across 133
1 209 435a
cities (test, design)
Brazil (Espirito Santo)34
2.1
ND
84 391
Brazil (Maranhao)68
37
40.4 (clustering, stratification, non-response)
2 877 454
Brazil (Rio de Janeiro), blood
6
4.7 (age, sex, test)
811 452
donors41
Brazil (Rio Grande do Sul)17
0.222
0.222 (sampling)b
25 283
Brazil (Sao Paulo)42
5.2
4.7 (sampling design)
14 017
Canada (British Columbia)50
0.45
0.55 (age)
27 890
Chile (Vitacura)43
11.2
ND
9 500
China, blood donors55
Wuhan
3.87
ND
433 827
Shenzhen
0.06
ND
7 818
Shijiazhuang
0.02
ND
2 206
China (Wuhan)14
10
ND
1 108 000
China (Wuhan)32
8.36 (3.53 for
ND (2.80 (age, sex, test) for entire period)
926 288
entire period)
China (Guangzhou), blood
0.09
ND
104 783
donors60
China (several regions)40
Hubei (not Wuhan)
3.6
ND
1 718 110
Chongqing
3.8
ND
11 956 109
Sichuan
0.6
ND
487 847
Guangdong
2.2
ND
2 522 010
Croatia26
1.27c
ND
51 765
Denmark, blood donors12
2
1.9 (test)
109 665
Denmark (Faroe Islands)52
0.6
0.7 (test)
365
France (Crepy-en-Valois)39
10.4
ND
620 105
France (Oise)13
25.9
ND
1 548 000
Germany (Gangelt)16
15
20.0 (test, cluster, symptoms)
2 519
Germany (Frankfurt)21
0.6
ND
16 086
Greece62
0.42 (April)
0.49 (age, sex, region)d
51 023
Hungary57
0.67
0.68 (design, age, sex, district)
65 671
Iceland58
2.3
0.9 (including those positive by PCR)
3 177
(quarantined),
0.3 (unknown
exposure)
India (Mumbai)61
54.1 in slum
58.4 in slum areas, 17.3 in non-slum areas (test,
534 750
areas, 16.1 in
age, sex)
non-slum areas
India (Srinagar)67
3.8
3.6 (age, sex)
54 000
Islamic Republic of Iran (Guilan)
8
22
33.0 (test, sampling)
770 000
Italy (Apulia), blood donors31
0.99
ND
39 887
Japan (Kobe)11
3.3
2.7 (age, sex)
40 999
Japan (Tokyo)29
3.83
ND
532 450
Japan (Utsunomiya City)48
0.4
1.23 (age, sex, distance to clinic, district,
6 378
cohabitants)
Kenya, blood donors44
5.6
5.2 (age, sex, region, test)
2 783 453
Luxembourg20
1.9
2.1 (age, sex, district)
12 684
Netherlands, blood donors15
2.7
ND
461 622
Netherlands (Rotterdam)64
3
ND
512 910
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Article ID: BLT.20.265892
Pakistan (Karachi)49
16.3 (20.0 in
11.9 (age, sex; 15.1 in East, 8.7 in Malir)
1 987 300
East, 12.7 in
Malir)
Pakistan (urban)66
17.5
ND
13 825 000
Qatar51
30.4 (24.0 for
ND
851 200
entire period)
Republic of Korea59
0.07
ND
1 867
Spain36
ND
5.0e (sampling, age, sex, income)
2 347 000
Spain (Barcelona)30
14.3
ND
1 081 938
Switzerland (Geneva)10
10.6
10.9 (test, age, sex)
54 500
Switzerland (Zurich)28
Unclear
1.3 in patients during 1-15 April and 1.6 in blood
19 773 (Zurich);
donors in May (multivariate Gaussian
30 888 (Zurich
conditioning)
and Lucerne)
United Kingdom (England)65
5.6
6.0 (test, sampling)
3 360 000
United Kingdom (Scotland) blood
1.2
ND
64 800
donors18
USA (six states)35
(age, sex, test)
Washington, Puget Sound
1.3
1.1
48 291
Utah
2.4
2.2
71 550
New York, New York City
5.7
6.9
641 778
Missouri
2.9
2.7
161 936
Florida, south
2.2
1.9
117 389
Connecticut
4.9
4.9
176 012
Louisiana
ND
5.8
267 033
California, San Francisco Bay
ND
1
64 626
Pennsylvania, Philadelphia
ND
3.2
156 633
Minnesota, Minneapolis
ND
2.4
90 651
USA (California, Bay Area)24
0.4 (blood
0.1 (test and confirmation)
7 753
donors)
USA (California, Los Angeles)22
4.06
4.65 (test, sex, race and ethnicity, income)
367 000
USA (California, San Francisco)33
4.3 in the
6.1 (age, sex, race and ethnicity, test)
316
census track
USA (California, Santa Clara)19
1.5
2.6 (test, sampling, cluster)
51 000
USA (Idaho, Boise)9
1.79
ND
8620
USA (Georgia, DeKalb and Fulton
2.7
2.5 (age, sex, race and ethnicity)
45 167
counties)53
USA (Idaho, Blaine county)46
22.4
23.4 (test, age, sex, household)
5 403
USA (Indiana)54
2.3 (IgG or PCR)
2.8 (age, race, Hispanic ethnicity)
187 802
USA (Louisiana, Baton Rouge)63
6
6.6 (census, race, parish) including PCR
46 147
positives
USA (Louisiana, Orleans and
6.9 (IgG or PCR)
6.9 for IgG (census weighting, demographics)
56 578
Jefferson Parish)37
USA (New York)23
12.5
14.0 (test, sex, age race and ethnicity, region)
2 723 000
USA, New York56
Columbia University Medical Center,
5
ND
463 044
New York City
CareMount central laboratory, five
1.8
ND
183 404
New York state counties
USA (New York, Brooklyn)27
47
ND
1 203 154
USA (Rhode Island), blood
3.9
ND
41 384
donors45
COVID-19: coronavirus disease 2019; ND: no data available; PCR: polymerase chain reaction; test: test performance.
a The authors calculated 760 000 to be infected in the 90 cities that had 200-250 samples tested, but many of the other 43
cities with < 200 samples may be equally or ever better represented since they tended to be smaller than the 90 cities (mean
population 356 213 versus 659 326).
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Article ID: BLT.20.265892
b An estimate is also provided adjusting for test performance, but the assumed specificity of 99.0% seems inappropriately
low, since as part of the validation process the authors found that several of the test-positive individuals had household
members who were also infected, thus the estimated specificity was deemed by the authors to be at least 99.95%.
c 1.20% in workers in Split without mobility restrictions, 3.37% in workers in Knin without mobility restrictions, 1.57% for all
workers without mobility restrictions; Split and Knin tended to have somewhat higher death rates than nationwide Croatia,
but residence of workers is not given, so the entire population of the country is used in the calculations.
d An estimate is also provided adjusting for test performance resulting in adjusted seroprevalence of 0.23%, but this seems
inappropriately low, since the authors report that all positive results were further validated by ELISA.
e 5.0% with point of care test, 4.6% with immunoassay, 3.7% with both tests positive, 6.2% with at least one test positive.
Notes: Of the studies where seroprevalence was evaluated at multiple consecutive time points, the seroprevalence estimate
was the highest in the most recent time interval with few exceptions, for example: in the Switzerland (Geneva) study,10 the
highest value was seen 2 weeks before the last time interval; in the Switzerland (Zurich) study,28 the highest value was seen
in the period 1-15 April for patients at the university hospital and in May for blood donors; and in the China (Wuhan) study,32
the highest value was seen about 3 weeks before the last time interval.
Page 30 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
Table 4. Deaths from COVID-19 and inferred infection fatality rates, overall and in people
younger than 70 years, by location, 2020
Location
Deaths from COVID-
Inferred infection
% of deaths from
Infection fatality
19, no. (date)
fatality rate
COVID-19 in people
rate in people < 70
(corrected), %
< 70 yearsa
years (corrected),
%
Argentina (Barrio Padre
44 (1 July)
0.16
(0.13)
~70
0.11
(0.09)
Mugica)47
Belgium38
7594 (30 April)
1.09
(0.87)
10
0.13
(0.10)
Brazil (133 cities)25
-b
Median 0.30 (0.27)
31 (< 60 years)
0.10
(0.9)
Brazil (Espirito Santo)34
363 (21 May)
0.43
(0.39)
31 (Brazil, < 60 years)
0.14
(0.13)
Brazil (Maranhao)68
4272 (8 August)
0.15
(0.14)
23
0.04
(0.03)
Brazil (Rio de Janeiro), blood
1019 (3 May)
0.12
(0.11)
31 (Brazil, < 60 years)
0.04
(0.04)
donors41
Brazil (Rio Grande do Sul)17
124 (14 May)
0.49
(0.39)
31 (Brazil, < 60 years)
0.19
(0.15)
Brazil (Sao Paulo)c,42
Unknown (15 May)
Unknown, but likely
31 (Brazil, < 60 years)
Unknown, but likely
> 0.4
> 0.1
Canada (British Columbia)50
164 (28 May)
0.59
(0.59)
13
0.08
(0.08)
Chile (Vitacura) c,43
Unknown (18 May)
Unknown, but likely
36 (Chile)
Unknown, but likely
< 0.2
< 0.1
China, blood donors55
Wuhan
1935 (20 February)
0.45
(0.41)
50
0.24
(0.22)
Shenzhen
1 (5 March)
0.01
(0.01)
About 50 (if similar to
0.01
(0.01)
Wuhan)
Shijiazhuang
1 (27 February)
0.05
(0.04)
About 50 (if similar to
0.03
(0.02)
Wuhan)
China (Wuhan)14
3869 (2 May)
0.35
(0.31)
50
0.19
(0.15)
China (Wuhan)32
3869 (13 April)
0.42
(0.38)
50
0.23
(0.21)
China (Guangzhou), blood
8 (5 April)
0.00
(0.00)
About 50 (if similar to
0.00
(0.00)
donors60
Wuhan)
China (several regions)40
Hubei (not Wuhan)
643 (12 April)
0.04
(0.03)
About 50 (if similar to
0.02
(0.02)
Wuhan)
Chongqing
6 (12 April)
0.00
(0.00)
About 50 (if similar to
0.00
(0.00)
Wuhan)
Guangdong
8 (12 April)
0.00
(0.00)
About 50 (if similar to
0.00
(0.00)
Wuhan)
Sichuan
3 (12 April)
0.00
(0.00)
About 50 (if similar to
0.00
(0.00)
Wuhan)
Croatia26
79 (3 May)
0.15
(0.14)
13
0.02
(0.02)
Denmark, blood donors12
370 (21 April)
0.34
(0.27)
12
0.05
(0.04)
Faroe Islands52
0 (5 May)
0.00
(0.00)
0
0.00
(0.00)
France (Crepy-en-Valois)39
2325 (5 May)d
0.37
(0.30)
7 (France, < 65 years)
0.04
(0.03)
France (Oise)13
932 (7 April)d
0.06
(0.05)
7 (France, < 65 years)
0.01
(0.01)
Germany (Gangelt)16
7 (15 April)
0.28
(0.25)
0
0.00
(0.00)
Germany (Frankfurt)21
42e (17 April)
0.26
(0.21)
14 (Germany)
0.04
(0.03)
Greece62
121 (22 April)
0.24
(0.19)
30
0.09
(0.07)
Hungary57
442 (15 May)
0.67
(0.54)
No data
No data
Iceland58
10 (1 June)
0.30
(0.30)
30
0.10
(0.10)
India (Mumbai)61
495 (13-20 July)
0.09
(0.07)
50 (< 60 years, India)
0.04
(0.03)
India (Srinagar)67
35 (15 July)f
0.06
(0.05)
50 (< 60 years, India)
0.03
(0.03)
Islamic Republic of Iran
617 (23 April)
0.08
(0.07)
No data
No data
(Guilan)8
Italy (Apulia), blood donors31
530 (22 May)
1.33
(1.20)
15 (Italy)
0.24
(0.22)
Japan (Kobe)11
10 (mid-April)
0.02
(0.02)
21 (Japan)
0.01
(0.01)
Japan (Tokyo)29
189 (11 May)
0.04
(0.03)
21 (Japan)
0.01
(0.01)
Japan (Utsunomiya City)48
0 (14 June)
0.00
(0.00)
0
0.00
(0.00)
Page 31 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
Kenya, blood donors44
64 (31 May)
0.00
(0.00)
58 (< 60 years)
0.00
(0.00)
Luxembourg20
92 (2 May)
0.73
(0.58)
9
0.07
(0.06)
Netherlands, blood donors15
3134 (15 April)
0.68
(0.68)
11
0.09
(0.09)
Netherlands (Rotterdam)64
3134 (15 April)
0.65
(0.52)
11
0.08
(0.06)
Pakistan (Karachi)49
~1500 (9 July)g
0.08
(0.07)
~70
0.06
(0.05)
Pakistan (urban)66
5266 (13 July)h
0.04
(0.04)
~70
0.03
(0.03)
Qatar51
93 (19 June)
0.01
(0.01)
74
0.01
(0.01)
Republic of Korea59
2 (3 June)i
0.10
(0.09)
0
0.00
(0.00)
Spain36
26 920 (11 May)
1.15
(0.92)
13
0.18
(0.14)
Spain (Barcelona)30
5137 (2 May)
0.48
(0.48)
13 (Spain)
0.07
(0.07)
Switzerland (Geneva)10
243 (30 April)
0.45
(0.36)
8
0.04
(0.03)
Switzerland (Zurich)28
107 (15 April, Zurich),
0.51
(0.41)
8 (Switzerland)
0.05
(0.04)
147 (22 May, Zurich
and Lucerne)
England65
38 854 (9 July)
1.16
(0.93)
20
0.27
(0.22)
Scotland, blood donors18
47 (1 April)
0.07
(0.06)
9 (< 65 years)
0.01
(0.01)
USA (10 states)35
Washington, Puget Sound
207 (4 April)
0.43
(0.43)
10 (state, < 60 years)
0.05
(0.05)
Utah
58 (4 May)
0.08
(0.08)
28 (< 65 years)
0.03
(0.03)
New York
4146 (4 April)
0.65
(0.65)
34 (state)
0.25
(0.25)
Missouri
329 (30 April)
0.20
(0.20)
23
0.05
(0.05)
Florida, south
295 (15 April)
0.25
(0.25)
28 (state)
0.08
(0.08)
Connecticut
2718 (6 May)
1.54
(1.54)
18
0.31
(0.31)
Louisiana
806 (11 April)
0.30
(0.30)
32
0.10
(0.10)
California, San Francisco Bay
321 (1 May)
0.50
(0.50)
25
0.14
(0.14)
Pennsylvania, Philadelphia
697 (26 April)
0.45
(0.45)
21 (state)
0.10
(0.10)
Minnesota, Minneapolis
436 (13 May)
0.48
(0.48)
20 (state)
0.10
(0.10)
USA (California, Bay Area)24
12 (22 March)
0.15
(0.12)
25
0.04
(0.03)
USA (California, Los
724 (19 April)
0.20
(0.18)
24 (< 65 years)
0.06
(0.05)
Angeles)22
USA (California, San
0 (4 May)
0.00
(0.00)
0
0.00
(0.00)
Francisco)33
USA (California; Santa Clara)19
94 (22 April)
0.18
(0.17)
35
0.07
(0.06)
USA (Idaho, Boise)9
14 (24 April)
0.16
(0.13)
14 (Idaho)
0.02
(0.02)
USA (Georgia)53
198 (7 May)
0.44
(0.44)
30
0.15
(0.15)
USA (Idaho, Blaine county)46
5 (19 May)
0.10
(0.08)
14 (Idaho)
0.02
(0.01)
USA (Indiana)54
1099 (30 April)
0.58
(0.46)
24
0.16
(0.13)
USA (Louisiana, Baton
420 (30 July)
0.91
(0.73)
32 (Louisiana)
0.32
(0.25)
Rouge)63
USA (Louisiana, Orleans and
925 (16 May)
1.63
(1.31)
32
0.57
(0.46)
Jefferson Parish)37
USA (New York)23
18 610 (30 April)j
0.68 (0.54)j
34
0.26 (0.23)d
USA (New York Columbia
965 (28 March, New
0.15
(0.14)
34
0.06
(0.05)
University Medical Center,
York state)
New York City and CareMount
central laboratory, five New
York state counties)56
USA (New York, Brooklyn)27
4894 (19 May)j
0.41 (0.33)j
34 (New York state)
0.15 (0.14)d
USA (Rhode Island), blood
430 (11 May)
1.04
(0.83)
17
0.20
(0.16)
donors45
COVID-19: coronavirus disease 2019.
a Whenever the number or proportion of COVID-19 deaths at age < 70 years was not provided in the paper, I retrieved the
proportion
of these deaths from situation reports of the relevant location. If I
could not find this information for the specific
location, I used a larger geographic area. For Brazil, the closest information that I found was from a news report.77 For
Croatia, I retrieved data on age for 45/103 deaths through Wikipedia.78
Page 32 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
b Data are provided by the authors for deaths per 100 000 population in each city along with inferred infection fatality rate in
each city, with wide differences across cities; the infection fatality rate shown here is the median across the 36 cities with
200-250 samples and at least one positive sample (the interquartile range for the uncorrected infection fatality rate is 0.20-
0.60%
and across all cities is 0-2.4%, but with very wide uncertainty in each
city). A higher infection fatality rate is alluded to
in the preprint, but the preprint also shows a scatter diagram for survey-based seroprevalence versus reported deaths per
population with a regression slope that agrees with an infection fatality rate of 0.3%.
c Information on deaths was not available for the specific locations. In the Sao Paulo study, the authors selected six districts
of Sao Paulo most affected by COVID-19, they do not name the districts and the number of deaths as of mid-May is not
available,
but using data for death rates across all Sao Paulo would give an
infection fatality rate of > 0.4% overall. In the
Vitacura
study, similarly one can infer from the wider Santiago metropolitan
area that the infection fatality rate in the Vitacura
area would probably be < 0.2% overall.
d For France, government situation reports provide the number of deaths per region only for in-hospital deaths; therefore, I
multiplied the number of in-hospital deaths by a factor equal to: total number of deaths/in-hospital deaths for all of France.
e Estimated from no. of deaths in Hesse province on 17 April × proportion of deaths in the nine districts with key enrolment
(enrolment ratio > 1:10 000) in the study among all deaths in Hesse province.
f I calculated the approximate number of deaths assuming the same case fatality ratio in the Srinagar district as in the
Jammu and Kashmir state where it is located.
g For Karachi, it is assumed that about 30% of COVID-19 deaths in Pakistan are in Karachi (since about 30% of the cases
are there).
h The number of deaths across all Pakistan; I assumed that this number is a good approximation of deaths in urban areas
(most deaths occur in urban areas and there is some potential underreporting).
i I calculated the approximate number of deaths from the number of cases in the study areas in south-western Seoul,
assuming a similar case fatality as in Seoul overall.
j Confirmed COVID-19 deaths; inclusion of probable COVID-19 deaths would increase the infection fatality rate estimates by
about a quarter.
Page 33 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
Table 5. Infection fatality rates for coronavirus disease-19 inferred from preliminary
nationwide seroprevalence data, 2020
Country
Sample size
Date
Reported
Population,
Deaths, no.
Inferred
(antibody)
seroprevalence
no.
(date)
infection
(%)
fatality rate
(corrected), %
Afghanistan75
9 500 (IgG?)
August?
31.5
39 021 453
1300 (8 May)
0.01
(0.01)
Czechia71
26 549 (IgG)
23 April-1 May
0.4
10 710 000
252 (4 May)
0.59
(0.47)
Finland69
674 (IgG)
20-26 Aprila
2.52
5 541 000
211 (30 April)
0.15
(0.12)
Georgia76
1 068 (IgG?)
18-27 May
1
3 988 264
12 (30 May)
0.03 (0.03)b
Israel72
1 709 (IgG?)
May
2-3
9 198 000
299 (10 June)
0.13 (0.10)c
Russian
650 000 (IgG?)
June?
14
145 941 776
5859 (7 June)
0.03
(0.03)
Federation74
Slovenia73
1368 (IgG?)
April
3.1
2 079 000
92 (1 May)
0.14
(0.11)
Sweden70
1 200 (IgG)
18-24 May
6.3
10 101 000
4501 (28 May)
0.71
(0.57)
COVID-19: coronavirus disease 2019; Ig: immunoglobin.
a The seroprevalence was slightly lower in subsequent weeks.
b The survey was done in Tbilisi, the capital city with a population 1.1 million. I could not retrieve the count of deaths
in Tbilisi, but if more deaths happened in Tbilisi, then the infection fatality rate may be higher, but still < 0.1%.
c Assuming a seroprevalence of 2.5%.
Notes: These are countries for which no eligible studies were retrieved in the literature search. The results of these
studies have been announced to the press and/or in preliminary reports, but are not yet peer reviewed and
published. The question marks indicate that the antibody type or date were not clear.
Page 34 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
Fig. 1. Flowchart for selection of seroprevalence studies on severe acute respiratory
syndrome coronavirus 2, 2020
Items identified through literature searches:
LITCOVID (seroprevalence OR antibodies) 1391 items
medRxiv (seroprevalence OR antibodies) 2302 items
bioRxiv ((seroprevalence OR antibodies) AND (SARS-CoV-2 OR COVID-19))
1147 items
Research Square (seroprevalence OR antibodies) 380 items
5108 items excluded
during first screening of
titles and abstracts
112 items evaluated in depth
1 item added from
communication
with experts
52 items excluded during
in-depth full-article
61 eligible articles for the analysis with a total of 74
eligible seroprevalence estimates
8 added from
identifying
unpublished national
82 eligible seroprevalence estimates from 51 different
locations
COVID-19: coronavirus disease 2019; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.
Page 35 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
Fig. 2. Estimates of infection fatality rates for COVID-19 in locations that had two or more
estimates, 2020
COVID-19: coronavirus disease 2019.
Notes: Locations are defined at the level of countries, except for the USA where they are defined at the level of
states and China is separated into Wuhan and non-Wuhan areas. Corrected infection fatality rate estimates are
shown (correcting for what types of antibodies were assayed).
Page 36 of 37
Publication: Bulletin of the World Health Organization; Type: Research
Article ID: BLT.20.265892
Fig. 3. Corrected estimates of COVID-19 infection fatality rate in each location plotted
against COVID-19 mortality rate as of September 12, 2020 in that location
COVID-19: coronavirus disease 2019
Notes: Locations are defined at the level of countries, except for the United Kingdom of Great Britain and Northern
Ireland where they are defined by jurisdiction, USA are defined at the level of states and China is separated into
Wuhan and non-Wuhan areas. Included locations are: Afghanistan; Argentina, Belgium Brazil; Canada; Chile; China
(non-Wuhan and Wuhan); Croatia; Czechia; Denmark; Faroe Islands; Finland; France; Georgia; Germany; Greece;
Hungary; Iceland; India; Islamic Republic of Iran (Islamic Republic of); Israel; Italy; Japan; Kenya; Luxembourg;
Netherlands; Pakistan; Qatar; Russian Federation; Slovenia; Republic of Korea; Spain; Sweden; Switzerland; United
Kingdom (England, Scotland); and USA (California, Connecticut, Florida, Georgia, Idaho, Indiana, Louisiana,
Minnesota, Missouri, New York, Pennsylvania, Rhode Island, Utah, Washington). When several infection fatality rate
estimates were available from multiple studies for a location, the sample size-weighted mean is used. One outlier
location with very high deaths per million population (1702 for New York) is not shown.
Page 37 of 37