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SARS-CoV-2 Infections in the World: An Estimation of the Infected Population and a Measure of How Higher Detection Rates Save Lives

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This paper provides an estimation of the accumulated detection rates and the accumulated number of infected individuals by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Worldwide, on July 20, it has been estimated above 160 million individuals infected by SARS-CoV-2. Moreover, it is found that only about 1 out of 11 infected individuals are detected. In an information context in which population-based seroepidemiological studies are not frequently available, this study shows a parsimonious alternative to provide estimates of the number of SARS-CoV-2 infected individuals. By comparing our estimates with those provided by the population-based seroepidemiological ENE-COVID study in Spain, we confirm the utility of our approach. Then, using a cross-country regression, we investigated if differences in detection rates are associated with differences in the cumulative number of deaths. The hypothesis investigated in this study is that higher levels of detection of SARS-CoV-2 infections can reduce the risk exposure of the susceptible population with a relatively higher risk of death. Our results show that, on average, detecting 5 instead of 35 percent of the infections is associated with multiplying the number of deaths by a factor of about 6. Using this result, we estimated that 120 days after the pandemic outbreak, if the US would have tested with the same intensity as South Korea, about 85,000 out of their 126,000 reported deaths could have been avoided.
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ORIGINAL RESEARCH
published: 25 September 2020
doi: 10.3389/fpubh.2020.00489
Frontiers in Public Health | www.frontiersin.org 1September 2020 | Volume 8 | Article 489
Edited by:
Zisis Kozlakidis,
International Agency For Research On
Cancer (IARC), France
Reviewed by:
Haiyan Fu,
Dali University, China
Paulin Sonon,
Aggeu Magalhães Institute
(IAM), Brazil
*Correspondence:
Carlos Villalobos
cvillal@uni-goettingen.de;
cvillalobos@utalca.cl
Specialty section:
This article was submitted to
Infectious Diseases - Surveillance,
Prevention and Treatment,
a section of the journal
Frontiers in Public Health
Received: 09 May 2020
Accepted: 31 July 2020
Published: 25 September 2020
Citation:
Villalobos C (2020) SARS-CoV-2
Infections in the World: An Estimation
of the Infected Population and a
Measure of How Higher Detection
Rates Save Lives.
Front. Public Health 8:489.
doi: 10.3389/fpubh.2020.00489
SARS-CoV-2 Infections in the World:
An Estimation of the Infected
Population and a Measure of How
Higher Detection Rates Save Lives
Carlos Villalobos*
Escuela de Ingeniería Comercial, Centro de Investigación en Economía Aplicada, Facultad de Economía y Negocios,
Universidad de Talca, Talca, Chile
This paper provides an estimation of the accumulated detection rates and the
accumulated number of infected individuals by the novel severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2). Worldwide, on July 20, it has been estimated
above 160 million individuals infected by SARS-CoV-2. Moreover, it is found that only
about 1 out of 11 infected individuals are detected. In an information context in which
population-based seroepidemiological studies are not frequently available, this study
shows a parsimonious alternative to provide estimates of the number of SARS-CoV-2
infected individuals. By comparing our estimates with those provided by the population-
based seroepidemiological ENE-COVID study in Spain, we confirm the utility of our
approach. Then, using a cross-country regression, we investigated if differences in
detection rates are associated with differences in the cumulative number of deaths. The
hypothesis investigated in this study is that higher levels of detection of SARS-CoV-2
infections can reduce the risk exposure of the susceptible population with a relatively
higher risk of death. Our results show that, on average, detecting 5 instead of 35 percent
of the infections is associated with multiplying the number of deaths by a factor of about
6. Using this result, we estimated that 120 days after the pandemic outbreak, if the US
would have tested with the same intensity as South Korea, about 85,000 out of their
126,000 reported deaths could have been avoided.
Keywords: infection fatality ratio, infection detection ratio, estimates of SARS-CoV-2 infections, asymptomatic
SARS-CoV-2 population, multiple linear regression
INTRODUCTION
Governments and policymakers dealing with the COVID-19 pandemic will fail in their objectives
if their actions are guided by misleading data or subsequent misinformation. The authorities
should have reliable estimations of the number of SARS-CoV-2 infected individuals. However,
there are few attempts to estimate the total amount of infections (15). Consequently, health
systems face enormous challenges since an unknown and probably a high proportion of all SARS-
CoV-2 infections remains undetected. Moreover, data suggest that infected individuals can be
highly contagious before the onset of symptoms and SARS-CoV-2 can be also highly contagious
in individuals who will never develop any symptoms (610).
Villalobos How SARS-CoV-2 Detection Saves Lives
Undetected infections are dangerous because infectious
individuals spread the coronavirus in unpredictable ways.
Undetected infections consist of non-PCR-tested individuals
with symptoms and asymptomatic individuals (non-COVID-19
patients) that are likely to remain undetected over all phases
of the infection. However, non-PCR-tested individuals with
symptoms would tend to auto-select themselves, depending on
the severity of their symptoms (from mild to severe), toward
treatment and late detection. For this reason, it is important
to know the proportion of the infected population which is
asymptomatic or has such mild symptoms that self-select them
into the group of non-PCR-tested individuals (1115). Here,
regarding the estimation of the number of infections, and for
purposes of public health, I advocate the view by Amartya Sen
and Martha Nussbaum that is preferable to be vaguely right than
precisely wrong.
The public health problem is that undetected asymptomatic
individuals, as well as late-detected SARS-CoV-2 infected
individuals, increase the risk for vulnerable groups1. Since there
is a transmission channel between the level of detection and
the number of deaths, the early detection of asymptomatic
infections, pre-symptomatic, and mild COVID-19 cases is a
public health concern.
Moreover, undetected cases also are responsible for the
collapse of the health system by numerous aggravated and
sometimes unexpected COVID-19 patients requiring treatment
in a short period. Overwhelmed health care systems reduce
the recovery prospects of patients by the lack of treatment,
undertreatment, increased risk of mistreatment of all patients,
including those with COVID-19, and also put at unnecessarily
risk the health workforce (21,22).
The problem is that many governments formulate their
strategies and responses to the pandemic based on figures that
they can control. This problem of reverse causality produces
contra-productive incentives for governments since public
opinion tends to negatively react to the report of the cumulative
and the marginal numbers of detected (reported) cases. The
contradiction is that something good, such as the increase in the
testing efforts by governments can be perceived by the public
opinion as something bad (due to the increase in detections).
Worldwide, the media communicates confirmed cases and deaths
as the relevant parameters to take into consideration when
assessing the evolution of the pandemic. This is a mistake
since this emphasis discourages governments from decidedly
pushing for mass testing with the obvious consequence of an
increased number of detected cases (although, as shown in this
paper, there is a theoretical mechanism relating more testing
with saving lives). More sophisticated observers would use the
crude and adjusted case fatality ratios to assess the pandemic
evolution. However, international comparisons show that crude
and adjusted case fatality ratios are highly heterogeneous and
their use can be misleading (23,24). For instance, the simple
division of the cumulative number of deaths by the cumulative
1Some evidence has been found claiming that elderly and male individuals are in
higher relative risk since the consequences of COVID-19 are more severe amongst
them (1620).
number of confirmed cases underestimated the true case fatality
ratio in past epidemics (24,25). Although nowadays many case
fatality ratios have been estimated in this pandemic correcting
many of the observed past biases (2628), they are still depending
on testing efforts made by countries.
The problem with heterogeneous case fatality ratios
(different proportions of all cases that will end in death
due to methodological differences on the denominator) is
that they are not anchored at any exogenous information
that allows researchers to perform international or territorial
comparisons based on credible, and transparent assumptions.
Consequently, to rely on the number of confirmed cases makes
international comparations impossible since governments
have shown to implement highly heterogeneous SARS-
CoV-2 testing strategies ending up in different levels of
location-based under-ascertainment.
In an attempt to solve the mentioned problem, we anchor our
analysis in the cumulative number of deaths, which is a statistic
much more difficult to alter, in free societies, than the number of
SARS-CoV-2 tests2.
We use this information together with the newest and
sound estimates of the age-stratified infection fatality ratios
(IFRs) provided in the recent SARS-CoV-2 related literature. In
particular, we base our analysis on the IFR of 0.657% reported in
Verity et al. (26). This IFR is very close to the 0.75% reported
in a meta-analysis of 13 IFR estimates from a wide range of
countries, and that were published between February and April
of 2020 (30). We also assume orthogonal attack rates of the
infection which is also supported by recent literature (16). By
weighting the age-stratified IFRs by the country population age-
groups shares in each country, it is possible to obtain country-
specific IFRs.
The relevance of this study is 3-fold: Firstly, the estimation
of the true number of infections includes not only confirmed
cases but COVID-19 undetected cases, as well as SARS-CoV-2-
infected individuals without the disease, or in a pre-symptomatic
stage. Therefore, to provide an estimation of the true number
of SARS-CoV-2 infections is of more utility than to be only
informed about the number of confirmed infections. This is
because confirmed cases depend on the testing efforts that can
be altered or even manipulated by governments. Moreover,
one can compare the true estimate of infections with the
number of COVID-19 patients that require hospitalization.
Such ratios can contribute to predicting, with exogenous-
to-government information, shortages of the health systems.
2Death-related statistics are nor free of problems. It is recognized that not all deaths
due to COVID-19 in all countries are reported following WHO international
norms and standards for medical certificates of COVID-19 cause of death and
International Classification of Diseases (ICD) mortality coding (29). Moreover, in
many countries, there is controversy over whether the COVID-19 death figures
are reliable or not (for instance in Spain, Chile, and the UK), especially when these
figures are compared against those from the number of excess deaths during the
pandemic. More generally, it is a matter of concern that the official accumulated
death figures show significant breaks responding probably to counting issues
rather than to real deaths’ dynamics. In our data, these breaks can be found in
Spain, Chile, China, Ecuador, the Philippines, and the United Kingdom.
Frontiers in Public Health | www.frontiersin.org 2September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
Secondly, the estimation of the true number of SARS-CoV-
2 infections allows us to estimate the detection rate of the
infection, which is a measure of the performance of health
systems and governments while facing the pandemic. One can
expect that higher levels of detection of SARS-CoV-2 infections,
which includes asymptomatic population, and those in their
early stages of the infection (which are more infectious) can
reduce the risk exposure of the susceptible population with
relatively a high risk of death, that is, the elderly and those
individuals with preexisting conditions (17). Accordingly, a
highly neglected statistic, such as the detection rate should
be considered highly relevant from the public health point
of view. Thirdly, in this paper, we test the hypothesis that
higher detection rates can save lives while providing a measure
of this impact (having in mind that is preferable to be
vaguely right than precisely wrong). Thus, this study aims to
quantify the importance of testing while providing empirical
support to the utility of implementing massive SARS-CoV-
2 tests.
Overall, this study argues that it is crucial to compute
the evolution of the cumulative number of estimated SARS-
CoV-2 infected individuals, and subsequently, the cumulative
detection rates. This information would provide public health
managers and governments the incentives to improve detection
rates, rather than to the opposite. Moreover, the identification
strategy can be used at lower levels of aggregation, such as
regions, provinces, and municipalities to improve responses to
the pandemic, including the planning of selective lockdowns or
spatial-selective enhancements of the installed critical care units.
In summary, this study proposes a baseline estimation of the
number of SARS-CoV-2 infections and detection rates based on
current information and transparent assumptions. However, the
assumptions discussed later in this paper can be later modified
to match the current scientific available evidence and country-
specific developments and contexts.
DATA AND METHODS
Data
For this research, we use the cumulative number of deaths and
confirmed cases in the world and by country, published by
OurWorldInData.org, a project of the Global Change Data Lab
with the collaboration of the Oxford Martin Programme on
Global Development at the University of Oxford3. Age-stratified
demographic proportions of the population were obtained from
the UN population data4. The age-stratified IFRs are those
reported in Verity et al. (26)5. Our method also requires to
know the distribution of the number of days between infection
3Available online at: https://ourworldindata.org/coronavirus-source-data
4United Nations, Department of Economic and Social Affairs, Population
Division. World Population Prospects 2019, Online Edition. Rev. 1. United Nations
(2020). Available online at: https://population.un.org/wpp/Download/Standard/
Population/ (accessed April 19, 2020).
5Following Verity et al. (26), the estimated IFRs correct for many types of bias.
The infection fatality ratios were obtained after combining adjusted case fatality
ratios with data on infection prevalence amongst individuals returning home from
Wuhan in repatriation flights.
and death. Since this number is unknown, we approach to
this number using the sum of the median incubation period
as reported in Lauer et al. (31), and the mean number of
days between the onset of symptoms and death as reported in
Verity et al. (26). For our empirical exercise, we rely on World
Development data by the World Bank (GDP per capita and
health expenditure as a share of the GDP)6and in World Health
Organization data for BCG vaccination7.
In this study, our regression analysis relies on data for 91
countries covering above 86% of the world population. The
remaining countries were excluded because they either do not
have significant mortality figures (for instance Uruguay, Monaco,
Bermuda, etc.), or full data.
Methods
Estimation Strategy
In this study, we rely on a very simple rationale. At a given point
in time, the cumulative number of deaths should be a proportion
of the cumulative number of infections somewhat in the past. But
how many days in the past? The answer lies in the sum of the
number of days of incubation and the number of days between
the onset of symptoms and death. This rationale follows a report
focusing on the 40 most-affected countries by the pandemic in
the world (32). However, in this paper, we deviated from the
mentioned report by using the key parameters in a different way,
which translated into a different estimation of the number of
infected individuals.
On average, deaths occur 18 days (17.8 days with 95%
credible interval [CrI] 16.9–19.2) after the onset of COVID-19
symptoms (26), while the incubation period of COVID-19 has
been estimated in about 5 days (5.1 days with 95% CI, 4.5–5.8) as
reported in Lauer et al. (31). Thus, by comparing the cumulative
number of deaths at time tin country i(cdeaths(i,t)) with the
country-specific infection fatality ratio (ifri), which is assumed
constant over time, it is possible to obtain a rough approximation
of the cumulative number of SARS-CoV-2 infections 23 days (18
days +5 days) in the past (cinfected(i,t23))8.
cinfected(i,t23)=cdeaths(i,t)
ifri
(1)
6https://data.worldbank.org/indicator/ (accessed April 24, 2020).
7https://apps.who.int/gho/data/node.main.A830?lang=en (accessed April 24,
2020).
8Differently to Bommer and Vollmer (32), we include the incubation period while
avoiding the subtraction of the number of days between the onset of symptoms
and detection to the relevant lag period. These differences explain the discrepancies
between both set of estimates. Moreover, by combining the cumulative distribution
function of the SARS-CoV-2 incubation period as reported in Lauer et al. (31) and
an approximation of the Gamma distribution with correction for epidemic growth
of the days between the onset of symptoms to death as reported in Verity et al.
(26), one can calculate a vector of probabilities to weight the cumulative number
of deaths required in equation 1. The weighting vector goes from t2(representing
the proportion of deaths of those who experienced 1 day between infection and
the onset of symptoms, plus one day from the onset of symptoms to death) to t72
(representing the proportion of deaths of those who experienced 12 days between
infection and the onset of symptoms and 60 days between the onset of symptoms
to death). The smoothed approach produces almost an identical estimation of
the cumulative number of infected individuals. Given that and for the sake of
simplicity, we prefer to use the non-smoothing approach.
Frontiers in Public Health | www.frontiersin.org 3September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
FIGURE 1 | Infection fatality ratios (selected countries, in percentage). Source: Own elaboration.
Additionally, we use the ratio between the cumulative
number confirmed (detected) cases at time t23 in country
i(cconfirmed(i,t23)) and the cumulative number of infected
individuals (cinfected(i,t23)) at time t23 in country ias a rough
measure of the cumulative rate of detection of SARS-CoV-2
infections at time t23.
detection rate(i,t23)=cconfirmed(i,t23)
cinfected(i,t23)
(2)
Infection Fatality Ratio
In order to estimate the country-specific infection fatality
ratio for country iused in equation 1, we weight the age-
stratified infection fatality ratios reported in Verity et al.
(26), by the age-group population shares of country i. The
calculation of the age-stratified infection fatality ratios relies on
two assumptions that can be modified when producing point
estimates of the number of individuals affected by a SARS-
CoV-2 infection. Firstly, it assumes that there are no cross-
country differences in the average overall health status of the
population, comorbidity, or in the soundness of the different
health systems. In absence of standardized country-specific
information of these variables, this assumption is convenient
although, at first sight, it can be considered a restrictive one.
However, it is quite the opposite since, in richer countries
with higher proportions of elderly populations, the estimated
infection mortality ratios are likely to be overestimated. If so,
our estimates of the infected population represent a lower limit
of the true number of infections. The second assumption is
that the attack rate of the coronavirus is unrelated to the age
and sex of susceptible individuals. This is in concordance with
the evidence in respiratory infections in previous pandemic
processes (26,33). Then, the distribution of IFRs across
countries reflects the “fixed” lethality of the virus associated
to a varying demographic structure of the population across
the world.
Figure 1 presents the calculated infection fatality ratios for the
world, and for 50 countries in which the lethality of the pandemic
has been more significant.
Recently, a cross-sectional epidemiological study with a
super-spreading event in the county of Heinsberg in Germany
offered the opportunity to estimate the infection fatality
ratio in the community (34). The estimated infection fatality
ratio was 0.36%. Although this number is surprisingly low
when compared with other estimations, for instance, the
used in this study for Germany (1.3%), it is not evident
that the true infection fatality ratio is closer to 0.36% rather
than 1.3%. This is because there can be local factors that
explain the discrepancy as pointed out in the Heinsberg
study. Amongst these factors, it might be mentioned
Frontiers in Public Health | www.frontiersin.org 4September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
FIGURE 2 | Estimates of the number of SARS-CoV-2 infections and the estimated detection rate in the World. (A) Estimated and confirmed infected population. (B)
Estimated Global detection rate. Note that (B) depicts the detection rates until t23. Both panels display 95% confidence intervals. Source: Own elaboration.
comorbidity gaps, ethnic differences, the quality and coverage
of the health systems, climatic differences, immunization
levels, etc.9.
Consequently, it might be necessary to assess the
consequences of using an overestimated infection fatality ratio
(that is, an IFR closer to the one reported in the Heinsberg study,
or others inferred from seroprevalence data (36). The answer
is that the number of infections would be underestimated, and
that detection rates would be overestimated (since the infection
fatality ratio is on the denominator). An overestimation of the
detection rates reduces the validity of international rankings
based on this figure. However, from the public health point
of view, this would be irrelevant since, as discussed later, all
countries should increase their detection rates of SARS-CoV-2
infections as much as possible.
Regression Analysis
To investigate whether improving the detection rates of SARS-
CoV-2 infections is potentially associated to save lives, we
use a parsimonious synchronic cross-country multiple linear
regression10. That is, we use the information reported 15, 60,
9For instance, the reported IFRs for a group of 9,496 Danish blood donors with no
comorbidity aged 17–69 reached 0.082% (35).
10In the context of the pandemic, a synchronic estimation refers to the use of
information of countries in the same pandemic phase, that is, after the same
and 105 days after the confirmation of the first 100 SARS-CoV-2
infections, which corresponds to the pandemic outbreak (PO). At
a given pandemic phase, we regress the natural logarithm of the
cumulative number of deaths in country i, ln deathsi, on their
estimated detection rates (DRi)and its squared to assess whether
there is a non-linear relationship of this conditional correlation11.
The four parsimonious regressions have a demographic
control that corresponds to the estimated country-specific
infection fatality ratio (ifri). This is a non-endogenous control
since it only captures the impact of demography (population
shares by age-groups) on the number of deaths and not the
reverse. The regressions control for the population size of the
country iin its natural logarithmic form ln(popi). This control
is necessary because the share of the susceptible population
remains persistently at relatively higher levels in more populated
countries when compared with the less populated ones. We
also include the natural logarithm of the number of confirmed
SARS-CoV-2 infections in each country ln(confirmedi). This is
a measure of the persistence of the mortality process while
controlling for cross-country differences in their absolute testing
number of days since the pandemic outbreak. On the contrary, a non-synchronic
estimation neglects the pandemic phases but considers as reference period the
calendar day.
11Output tables without the square of the detection rates are available in the
Supplementary Material.
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Villalobos How SARS-CoV-2 Detection Saves Lives
FIGURE 3 | World distribution of deaths as of 20 July 2020. Source: Own Elaboration.
performances. The regressions also control for the economic
performance of a country by means of the natural logarithm
of the per capita gross domestic product ln(gdppci)12. We also
include the current health expenditure as share of GDP in 2017
(healthsharei). This control is needed to account for relative
resource-dependent differences in the coverage/quality of the
health systems around the globe. Finally, we use available data
to explore a possible association between BCG vaccination and
aggravated cases of COVID-19, and deaths [a relationship which
is being investigated in some clinical trials (37)]13. The evidence
is still inconclusive because the argued existence of uncontrolled
confounders (3842). However, if these confounders exist, they
can bias the relationship between SARS-CoV-2 detections rates
and the cumulative number of deaths. Based on this argument,
we include a raw of dummies capturing the degree of BCG
vaccination coverage as follows: BGC group 1: no mandatory
vaccination (up to 49.9% coverage), BGC group 2: 50 to 79.9%
coverage, BGC group 3: 80 to 89.9%, BGC group 4: 90 to 98.9%,
and BGC group 5: 99 to 100%. The reference category is BCG
12In constant 2017 international dollars with the same purchase power.
13https://apps.who.int/gho/data/node.main.A830?lang=en (accessed April 24,
2020).
group 1.
ln deathsi=α+β1DRi+β2DR2
i+β3ifri+β4ln(popi)
+β5ln(confirmedi)+β6ln gdppci
+β7healthsharei+β8bcg2i+β9bcg3i+β10bcg4i
+β11bcg5i+µii=1, ..., 91 (3)
Robustness
An alternative approach is used to indirectly investigate the
conditional association between detection rates and SARS-CoV-
2 related deaths. Instead of using the detection rates and its
square, we use the natural logarithm of the estimated number
of infections ln(infectionsi) while dropping from the equation
the natural logarithm of the number of confirmed (detected)
SARS-CoV-2 infections as follows:
ln deathsi=α+β1ln(infectionsi)+β2ifri+β3ln(popi)
+β4ln(gdppci)+β5healthsharei
+β6bcg2i+β7bcg3i+β8bcg4i+β9bcg5i+µi
i=1, ..., 91 (4)
Regarding the statistical inference, significance tests rely on a
heteroscedasticity consistent covariance matrix (HCCM) type
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Villalobos How SARS-CoV-2 Detection Saves Lives
FIGURE 4 | World distribution of the estimated number of SARS-CoV-2 infections as of 20 July 2020. Source: Own Elaboration.
HC3 which is suitable when the number of observations is
small (43). Although in the presence of heteroscedasticity of
unknown form, Ordinary Least Square estimates are unbiased,
the inference can be misleading due to the fact that the usual tests
of significance are generally inappropriate (43).
Additionally, we estimate the same set of equations (the main
specification and the robustness specification 15, 60, and 105
days after the pandemic outbreak) using robust regressions. We
do this because we have the concern that parameter estimates
may be biased if, in some countries (outliers), the report of
the cumulative number of deaths has been involuntarily altered
or even manipulated. Robust regression resists the effect of
such outliers, providing better than OLS efficiency when heavy-
tailored error distributions exist as it can be likely the case (44).
RESULTS
Descriptive Analysis
On July 20, the estimated infected population reaches about 160
million individuals (Figure 2A). This number is about 19 times
larger than the reported number of confirmed cases (about 8.6
million represented by the dashed line). Note that the number of
infections is estimated based on detection rates calculated 23 days
in the past. Thus, for the period t23 to t, the number of SARS-
CoV-2 infected individuals are estimated using the estimation
rate as in t23. Therefore, the estimation of SARS-CoV-2 infected
individuals can be biased if detection rates deteriorate or improve
considerably within this time span.
The accuracy of our estimations can be assessed by
contrasting them against to those provided by population-based
seroepidemiological studies. There are some studies of this
type focusing on restricted geographical areas, for instance, in
Germany and Switzerland (34,45). However, to the best of
our knowledge, there is only one country level and large scale
population-based seroepidemiological study performed in Spain
(46). The ENE-COVID study in Spain finds that, on 11 May, 5%
of the population would test IgG positive against SARS-CoV-
2. It implies that about 2.35 million individuals were infected
by SARS-CoV-2. Similarly, in our study we estimated on 11
May an infected population of about 2.25 million individuals.
This evidence suggests that our method can be a suitable
alternative when population-based seroepidemiological studies
are not available, which is frequently the case. Here, it is
important to recognize that, from the public health point of
view, it is preferable to be vaguely right than precisely wrong.
On 11 May, Spain confirmed only 246,504 cases (about 10%
of all estimated infections). At that time, it would have been
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Villalobos How SARS-CoV-2 Detection Saves Lives
FIGURE 5 | World distribution of the estimated detection rates of SARS-CoV-2 infections as of 20 July 2020 (in percentage). Source: Own Elaboration.
convenient that public health authorities and the public opinion
would have the information that, for each confirmed case, there
were significantly much more individuals spreading the infection
in unpredictable ways.
Back to the global estimates, by comparing the cumulative
number of estimated infections with the cumulative number
of confirmed (detected) cases, we obtain, at the end of June
2020, a global detection rate of about 9% (Figure 2B). The
global detection rate curve shows an U-shape with a minimum
at the beginning of the third week of March reaching only
1.1%. The last data suggest that detection rates are steadily
increasing. Moreover, the semi-logarithmic plot in Figure 2A
suggests that the infection stopped spreading at its maximum
pace approximately during the third week of March, but
unfortunately, it increased its speed again around the last week
of June.
The world distribution of the number of deaths, the estimated
number of SARS-CoV-2 infections, and the detection rates
of SARS-CoV-2 infections across the world are displayed in
Figures 35, respectively.
Since the global estimates are no more than an aggregation
of the trajectories made by the different countries in the world,
we investigate how heterogeneous the detection rates across
countries are. Table 1 presents this information in a synchronic
way. The rankings compare countries in the same phase of their
respective pandemic processes, that is after 15, 30, 45, 60, 75,
and 90 days after the confirmation of the first 100 SARS-CoV-
2 infections (pandemic outbreak). This approach allows us to
perform such an international comparison.
At a first sight, it is noteworthy the fact that each of the first
24 countries ranked on the top by the initial detection rate (15
days after the beginning of the pandemic outbreak) does not
accumulate more than 500 deaths 45 days after initiating their
pandemic processes. Thus, it seems to exist a strong correlation
between detection rates and the cumulative number of deaths for
a given stage of the pandemic process. Countries with high counts
of deaths ranked very badly in their initial detection rates. For
example, the US, Spain, Italy, UK, France, and Belgium ranked
in place 90, 82, 81, 89, 87, and 85, out of 91 countries listed in
the ranking.
A second conclusion is that the relative improvement of
detection rates over time, that is, 30, 45, 60, 75, and 90 days after
the beginning of the pandemic processes, does not alter the fact
that those countries are still ranked the worst in terms of deaths.
That is, improving detection over time has declining returns to
scale when comes to save lives.
The depicted relationship between detection rates and the
cumulative number of deaths remains almost unchanged when
using non-synchronic data as of 20 May in Table 2. This table
mixes information of countries at different stages from their
pandemic processes. So, it must be interpreted with caution.
Although efforts to increase detection have been significative in
Frontiers in Public Health | www.frontiersin.org 8September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
TABLE 1A | Synchronic descriptive statistics (15, 30, and 45 days after the pandemic outbreak).
Country/Days
since the first
100 cases
were
confirmed
Detection
rankings
Confirmed Cases
(in thousands)
Estimated Cases
(in thousands)
Estimated
detection rate
(Percentage)
Number of deaths
(Count)
15 30 45 15 30 45 15 30 45 15 30 45 15 30 45
South Korea 1 2 3 6.3 8.8 10.2 15.8 22.5 25.3 39.8 39.1 40.4 42 103 183
Australia 2 1 1 1.8 6.0 6.7 6.7 9.7 10.4 27.3 61.1 64.1 7 45 74
Luxembourg 3 6 7 2.2 3.4 3.8 9.3 11.2 12.3 23.3 30.0 30.9 23 69 90
Thailand 4 3 2 1.4 2.6 2.9 6.0 6.9 7.0 23.0 37.8 41.7 7 41 54
Lithuania 5 5 12 0.8 1.3 1.4 3.4 4.1 5.4 23.0 32.4 26.3 9 36 46
Croatia 6 12 13 1.0 1.8 2.1 4.5 7.4 8.3 22.6 24.4 25.3 7 36 77
Estonia 7 8 8 0.6 1.3 1.6 3.4 4.7 5.5 18.7 27.9 30.1 1 25 50
Norway 8 7 6 1.7 5.5 7.1 10.2 19.2 22.7 17.0 28.7 31.2 7 50 154
Finland 9 24 23 1.0 2.8 4.5 7.2 18.4 24.0 13.3 15.0 18.6 4 48 186
Israel 10 4 5 3.0 10.7 15.4 24.2 33.1 39.0 12.5 32.5 39.5 10 101 199
Czech R. 11 10 11 2.1 5.7 7.4 16.6 22.7 27.2 12.4 25.3 27.1 9 119 218
Japan 12 21 30 0.4 1.0 3.7 3.4 6.8 24.2 12.1 15.4 15.1 6 36 73
Greece 13 15 18 0.9 2.0 2.5 7.8 10.8 12.3 11.4 18.7 20.3 26 90 130
Chile 14 13 24 2.4 7.9 14.9 21.7 38.7 85.5 11.3 20.5 17.4 8 92 216
Austria 15 11 10 3.6 12.3 14.8 33.4 50.4 54.4 10.9 24.4 27.3 16 220 463
Bosnia & H. 16 31 33 0.7 1.3 1.9 6.1 11.9 15.1 10.8 11.0 12.9 24 48 79
Albania 17 18 17 0.4 0.6 0.8 3.6 3.6 3.9 10.4 16.8 21.6 22 26 31
Slovenia 18 25 28 0.6 1.2 1.4 6.3 8.2 8.8 10.1 14.5 16.0 9 50 82
Bulgaria 19 30 31 0.5 0.8 1.6 4.7 7.7 11.0 9.8 11.0 14.5 10 41 72
Puerto Rico 20 26 22 0.8 1.4 2.3 8.1 10.3 11.6 9.8 13.3 19.4 42 84 113
Cuba 21 19 19 0.6 1.4 1.8 7.3 8.4 8.8 8.5 16.3 20.2 16 54 77
Malaysia 22 16 16 1.5 4.0 5.5 18.3 22.5 24.7 8.3 17.6 22.4 14 63 93
Tunisia 23 29 37 0.6 0.9 1.0 7.2 8.1 8.7 8.2 11.1 11.8 22 38 44
Serbia 24 9 4 1.2 5.7 9.4 14.7 20.9 23.2 8.0 27.3 40.3 31 110 189
Portugal 25 14 14 4.3 16.0 23.9 54.1 80.5 94.5 7.9 19.8 25.3 76 470 903
Suriname 26 – – 0.3 – – 3.9 – – 7.8 – – 8 – –
Switzerland 27 17 20 4.8 20.2 27.7 75.8 119.4 137.7 6.4 16.9 20.1 43 540 1,134
Moldova 28 35 39 1.0 2.6 4.5 15.4 26.9 39.9 6.3 9.7 11.2 19 73 143
South Africa 29 51 58 1.4 2.6 6.0 22.3 52.4 121.0 6.2 5.0 4.9 5 48 116
Ukraine 30 23 21 1.7 7.6 14.2 28.4 50.6 72.0 5.9 15.1 19.7 52 193 361
Nicaragua 31 39 – 1.1 2.0 – 19.8 24.4 5.6 8.3 – 46 64
Macedonia 32 33 42 0.5 1.2 1.5 8.7 11.6 14.9 5.6 10.6 10.2 17 54 86
Denmark 33 28 26 1.5 5.1 7.9 27.2 39.7 47.2 5.4 12.8 16.8 24 203 384
El Salvador 34 41 34 0.2 0.7 1.7 4.7 8.7 14.0 5.0 8.0 12.3 8 15 33
Libya 35 53 0.4 0.7 – 8.3 16.5 4.7 4.2 – 5 18
Panama 36 36 36 1.3 4.0 6.7 28.5 43.9 56.6 4.6 9.2 11.9 32 109 192
Poland 37 34 32 1.6 6.7 11.9 35.7 67.3 90.4 4.6 9.9 13.2 18 232 562
Argentina 38 47 51 1.1 2.7 4.7 27.6 44.7 69.6 4.1 5.9 6.7 34 122 237
Bangladesh 39 43 45 2.9 10.9 26.7 73.0 151.7 297.2 4.0 7.2 9.0 101 183 386
Russia 40 27 9 2.3 24.5 106.5 60.6 188.2 370.0 3.9 13.0 28.8 17 198 1,073
Guatemala 41 66 79 0.4 0.9 3.1 10.2 33.4 123.4 3.8 2.7 2.5 11 24 55
China 42 20 25 14.4 70.6 80.3 383.7 455.1 473.8 3.8 15.5 16.9 304 1,771 2,946
Romania 43 38 40 1.5 6.3 11.3 41.4 75.9 104.7 3.5 8.3 10.8 29 306 631
Turkey 44 32 29 15.7 74.2 122.4 471.0 677.4 786.3 3.3 11.0 15.6 277 1,643 3,258
Saudi Arabia 45 44 27 1.2 4.9 20.1 38.6 74.0 124.3 3.2 6.7 16.2 8 65 152
Germany 46 22 15 3.8 57.3 125.1 123.2 374.0 545.7 3.1 15.3 22.9 8 455 2,969
Haiti 47 37 38 0.5 2.5 4.7 17.5 29.0 40.8 3.0 8.6 11.5 21 48 82
Ireland 48 46 41 2.4 9.7 19.6 81.9 159.7 187.4 2.9 6.0 10.5 36 334 1,102
(Continued)
Frontiers in Public Health | www.frontiersin.org 9September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
TABLE 1A | Continued
Country/Days
since the first
100 cases
were
confirmed
Detection
rankings
Confirmed Cases
(in thousands)
Estimated Cases
(in thousands)
Estimated
detection rate
(Percentage)
Number of deaths
(Count)
15 30 45 15 30 45 15 30 45 15 30 45 15 30 45
Morocco 49 42 35 1.0 3.0 5.2 34.8 39.6 42.6 2.9 7.7 12.3 70 143 191
South Sudan 50 40 43 0.3 1.3 1.9 12.0 16.4 18.7 2.8 8.0 10.1 6 14 34
Dominican R. 51 49 46 1.6 4.7 8.2 57.9 83.7 98.7 2.7 5.6 8.3 77 226 346
Canada 52 45 48 3.4 20.7 43.9 125.1 340.8 552.5 2.7 6.1 7.9 35 509 2,302
Hungary 53 52 53 0.7 1.9 3.0 25.5 38.6 45.9 2.7 5.0 6.6 32 189 351
Colombia 54 56 55 1.1 3.2 7.0 40.2 79.5 130.0 2.6 4.1 5.4 17 144 314
Niger 55 77 81 0.6 0.8 0.9 26.7 36.6 37.7 2.4 2.1 2.4 19 36 55
Pakistan 56 64 62 1.6 6.0 15.8 71.6 203.6 377.3 2.3 2.9 4.2 18 107 346
U. Arab E. 57 48 44 0.7 5.4 12.5 29.5 91.6 128.4 2.3 5.9 9.7 6 33 105
Sweden 58 60 56 1.6 6.4 14.4 77.9 197.1 287.1 2.1 3.3 5.0 16 373 1,540
Ecuador 59 74 64 2.3 7.9 24.9 118.0 359.2 652.6 2.0 2.2 3.8 79 388 900
Somalia 60 61 61 0.6 1.4 2.0 31.8 43.8 46.9 1.9 3.1 4.2 28 55 78
Bolivia 61 71 68 0.4 1.1 3.1 19.7 46.5 92.4 1.8 2.3 3.4 28 55 142
Burkina Faso 62 76 80 0.4 0.6 0.7 25.5 29.5 30.7 1.6 2.1 2.4 23 41 48
Honduras 63 87 76 0.4 0.7 2.0 24.8 44.4 70.7 1.6 1.5 2.8 25 61 116
Iraq 64 62 73 0.5 1.4 1.8 36.3 44.6 61.2 1.5 3.1 3.0 42 78 88
Sierra Leone 65 63 67 0.3 0.8 1.1 23.0 26.0 30.4 1.5 3.0 3.6 20 45 50
Kenya 66 86 85 0.2 0.4 0.8 16.1 27.4 45.7 1.5 1.5 1.8 11 21 50
Cameroon 67 73 77 0.8 1.8 2.8 57.1 82.5 107.3 1.4 2.2 2.6 12 59 136
D. R. Congo 68 79 71 0.3 0.6 1.4 19.3 31.1 42.0 1.4 1.8 3.3 22 31 61
Algeria 69 72 70 1.3 2.7 4.8 102.5 122.5 147.8 1.3 2.2 3.3 152 384 470
Mauritania 70 59 65 0.7 2.2 4.5 53.4 64.6 120.3 1.3 3.4 3.7 31 95 129
Netherlands 71 54 50 3.0 16.6 32.7 239.8 395.9 482.8 1.2 4.2 6.8 106 1,651 3,684
Iran 72 68 59 9.0 29.4 68.2 733.6 1,167.9 1,440.0 1.2 2.5 4.7 354 2,234 4,232
Mali 73 85 86 0.4 0.7 1.1 30.9 46.3 63.5 1.2 1.5 1.7 21 38 67
Chad 74 80 84 0.5 0.8 0.9 41.2 43.5 43.5 1.2 1.8 2.0 50 65 73
Afghanistan 75 83 74 0.6 1.5 4.7 48.8 96.5 159.4 1.1 1.6 2.9 18 57 122
Peru 76 58 54 1.1 11.5 37.0 106.5 319.1 627.9 1.0 3.6 5.9 30 254 1,051
Sudan 77 84 82 0.8 2.7 5.5 80.1 177.8 234.9 1.0 1.5 2.3 45 111 314
Philippines 78 67 69 1.1 4.6 7.8 118.9 177.0 234.0 0.9 2.6 3.3 68 297 511
Brazil 79 81 83 3.9 22.2 66.5 433.6 1,333.9 3,175.9 0.9 1.7 2.1 114 1,223 4,543
Indonesia 80 75 72 1.3 4.6 9.5 148.1 215.2 307.2 0.9 2.1 3.1 114 399 773
Italy 81 55 52 7.4 63.9 135.6 899.6 1,566.6 2,024.0 0.8 4.1 6.7 366 6,077 17,129
Spain 82 50 47 9.2 94.4 181.5 1,205.5 1,865.5 2,182.4 0.8 5.1 8.3 309 8,189 18,893
India 83 69 66 1.3 11.4 33.1 164.6 456.0 899.4 0.8 2.5 3.7 32 377 1,074
Egypt 84 82 78 0.6 2.2 5.0 77.0 136.9 203.1 0.7 1.6 2.5 36 164 359
Belgium 85 65 57 2.3 18.4 38.5 316.7 634.0 770.7 0.7 2.9 5.0 37 1,283 5,683
Nigeria 86 88 75 0.3 1.5 5.0 54.2 111.0 175.1 0.6 1.4 2.8 10 44 164
France 87 70 60 4.5 40.2 98.1 742.3 1,732.2 2,149.8 0.6 2.3 4.6 91 2,606 14,967
Mexico 88 89 87 1.4 6.3 20.7 251.9 691.8 1,525.6 0.5 0.9 1.4 37 486 1,972
U.K. 89 78 63 3.3 38.2 114.2 907.2 1,904.7 2,945.9 0.4 2.0 3.9 144 3,605 15,464
U.S. 90 57 49 4.7 189.6 639.7 1,547.1 5,216.8 8,058.6 0.3 3.6 7.9 85 4,079 30,985
Yemen 91 90 88 0.3 0.7 1.1 126.8 170.9 242.1 0.2 0.4 0.5 66 160 302
This ranking is made up of all countries with more than 30 deaths due to COVID-19 40 days after the pandemic outbreak. Countries are ranked by their detection rates 15 days after
the pandemic outbreak. Missing values in this table indicate that the country has not reached the requested number of days after its pandemic outbreak. Source: Own elaboration.
Frontiers in Public Health | www.frontiersin.org 10 September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
TABLE 1B | Synchronic descriptive statistics (60, 75, and 90 days after the pandemic outbreak).
Country/Days
since the first
100 cases
were
confirmed
Detection rankings Confirmed Cases (in
thousands)
Estimated Cases (in
thousands)
Estimated detection
rate (Percentage)
Number of deaths
(Count)
60 75 90 60 75 90 60 75 90 60 75 90 60 75 90
South Korea 6 7 6 10.7 10.8 11.1 26.9 27.9 28.8 39.7 38.7 38.6 236 254 263
Australia 1 1 1 6.9 7.1 7.3 10.8 10.8 11.0 63.8 65.7 65.9 97 101 102
Luxembourg 8 9 10 3.9 4.0 4.1 12.4 12.4 12.4 31.7 32.5 32.9 104 110 110
Thailand 3 4 4 3.0 3.1 3.1 7.3 7.3 7.3 41.4 42.0 42.9 56 57 58
Lithuania 14 16 15 1.6 1.7 1.8 6.1 6.4 6.5 25.8 26.4 27.6 60 71 76
Croatia 16 18 22 2.2 2.2 2.3 8.8 8.8 9.5 25.4 25.4 23.7 95 103 107
Estonia 9 10 9 1.7 1.8 2.0 5.9 5.9 5.9 29.6 31.2 33.4 61 66 69
Norway 7 8 8 7.8 8.3 8.4 23.3 24.0 24.7 33.6 34.3 34.1 208 233 237
Finland 19 19 17 6.0 6.6 7.0 25.6 26.1 26.3 23.3 25.3 26.7 267 308 324
Israel 5 6 5 16.5 16.8 18.4 41.0 42.7 45.9 40.3 39.3 40.0 258 281 299
Czech R. 10 11 12 8.1 9.0 9.8 29.6 30.5 32.1 27.5 29.5 30.4 280 317 328
Japan 13 12 14 11.1 15.4 16.4 42.8 54.5 57.6 26.0 28.2 28.5 186 543 777
Greece 24 25 24 2.7 2.9 3.1 13.4 14.0 14.3 20.3 20.6 21.3 151 172 183
Chile 27 33 27 37.0 90.6 167.4 209.2 608.1 866.7 17.7 14.9 19.3 358 944 3,101
Austria 12 13 16 15.7 16.3 16.8 58.1 58.9 61.0 26.9 27.7 27.5 608 633 672
Bosnia & H. 35 34 36 2.3 2.6 3.3 16.0 17.6 21.7 14.6 14.7 15.1 135 158 168
Albania 20 26 31 1.0 1.2 1.9 4.2 6.4 11.1 23.0 18.9 17.0 31 33 43
Slovenia 31 32 33 1.5 1.5 1.5 9.1 9.2 9.4 16.0 16.0 15.9 102 106 109
Bulgaria 33 35 34 2.2 2.5 3.5 14.1 17.7 22.2 15.8 14.2 15.6 110 144 181
Puerto Rico 11 5 3 3.3 5.3 6.9 12.2 12.8 15.3 27.2 41.7 44.9 129 143 151
Cuba 22 20 19 2.0 2.2 2.3 9.0 9.1 9.3 21.7 24.2 24.9 82 83 85
Malaysia 15 15 11 6.5 7.1 8.3 25.4 26.7 26.7 25.5 26.7 31.1 107 115 117
Tunisia 41 43 42 1.0 1.1 1.2 8.9 9.0 9.1 11.8 12.0 12.7 47 49 50
Serbia 2 2 7 10.6 11.4 12.4 24.3 25.5 34.2 43.7 44.8 36.4 230 244 256
Portugal 17 14 13 27.7 31.0 35.6 109.6 115.5 121.1 25.2 26.9 29.4 1,144 1,342 1,495
Switzerland 23 24 25 29.9 30.5 30.8 145.4 147.8 148.4 20.6 20.7 20.8 1,476 1,613 1,659
Moldova 39 38 35 6.7 9.2 14.0 55.1 73.4 89.8 12.2 12.6 15.5 233 323 464
South Africa 64 60 55 14.4 32.7 73.5 304.5 592.7 1,015.7 4.7 5.5 7.2 261 683 1,568
Ukraine 21 21 21 20.6 27.0 37.2 91.7 119.4 151.1 22.4 22.6 24.6 605 788 1,012
Macedonia 48 51 47 1.9 2.6 4.8 20.9 33.8 46.6 8.9 7.7 10.3 110 147 222
Denmark 25 22 23 10.1 11.2 11.9 50.2 52.5 53.2 20.1 21.4 22.4 514 561 587
El Salvador 46 49 2.9 4.6 – 31.1 53.3 9.4 8.7 – 53 107 –
Panama 37 37 37 9.4 13.5 21.4 73.6 99.2 151.6 12.8 13.6 14.1 269 336 448
Poland 30 29 26 16.9 22.5 28.2 105.0 125.4 142.1 16.1 17.9 19.8 839 1,028 1,215
Argentina 52 45 40 7.8 17.4 34.1 106.9 161.7 254.4 7.3 10.8 13.4 366 556 878
Bangladesh 38 31 32 57.6 105.5 159.7 460.1 638.5 965.1 12.5 16.5 16.5 781 1,388 1,997
Russia 4 3 2 262.8 396.6 529.0 639.9 896.2 1,146.1 41.1 44.2 46.2 2,418 4,555 6,948
Guatemala 78 76 7.1 13.8 239.1 417.0 3.0 3.3 252 547
China 29 40 43 81.1 82.4 83.8 480.8 667.4 667.6 16.9 12.3 12.5 3,241 3,316 4,636
Romania 36 36 41 15.8 18.6 21.2 119.2 136.2 158.9 13.2 13.7 13.3 1,002 1,219 1,369
Turkey 28 28 28 148.1 163.9 179.8 853.5 906.0 956.9 17.3 18.1 18.8 4,096 4,540 4,825
Saudi Arabia 26 27 30 44.8 80.2 119.9 227.4 435.6 678.5 19.7 18.4 17.7 273 441 893
Germany 18 17 18 157.6 172.2 180.5 626.6 662.7 680.8 25.2 26.0 26.5 6,115 7,723 8,450
Haiti 40 – – 6.1 – – 51.6 – – 11.8 – – 110 – –
Ireland 42 42 44 23.2 24.8 25.2 198.5 204.4 207.7 11.7 12.1 12.2 1,488 1,631 1,703
Morocco 32 30 29 7.1 8.0 9.6 44.7 46.4 52.7 16.0 17.3 18.2 194 208 213
Dominican R. 43 41 39 13.2 18.0 24.6 116.8 148.3 183.3 11.3 12.2 13.4 441 516 635
(Continued)
Frontiers in Public Health | www.frontiersin.org 11 September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
TABLE 1B | Continued
Country/Days
since the first
100 cases
were
confirmed
Detection rankings Confirmed Cases (in
thousands)
Estimated Cases (in
thousands)
Estimated detection
rate (Percentage)
Number of deaths
(Count)
60 75 90 60 75 90 60 75 90 60 75 90 60 75 90
Canada 45 44 45 67.7 84.7 96.2 700.1 784.8 820.9 9.7 10.8 11.7 4,693 6,424 7,835
Hungary 53 52 53 3.6 3.9 4.1 50.1 52.4 53.9 7.1 7.5 7.6 467 534 568
Colombia 54 54 60 14.9 29.4 53.1 233.8 429.7 809.2 6.4 6.8 6.6 562 939 1,726
Niger 81 80 1.0 1.0 – 38.9 39.5 2.5 2.6 – 65 67
Pakistan 60 62 52 37.2 66.5 139.2 686.1 1,230.3 1,658.4 5.4 5.4 8.4 803 1,395 2,632
U. Arab E. 34 23 20 21.8 33.9 42.3 145.2 159.4 171.5 15.0 21.3 24.7 210 262 289
Sweden 55 53 50 22.7 30.8 40.8 365.8 417.2 457.6 6.2 7.4 8.9 2,769 3,743 4,542
Ecuador 70 71 71 31.5 38.6 46.8 763.3 890.2 1,027.2 4.1 4.3 4.6 2,594 3,334 3,896
Somalia 57 59 2.6 2.9 – 48.0 51.8 5.5 5.7 – 88 90
Bolivia 62 61 65 8.4 16.9 30.7 160.1 310.3 534.0 5.2 5.5 5.7 293 559 970
Burkina Faso 80 78 74 0.8 0.9 0.9 30.7 30.7 30.9 2.7 2.9 2.9 52 53 53
Honduras 67 74 68 4.4 7.4 15.4 103.6 182.8 316.5 4.2 4.0 4.9 188 290 426
Iraq 82 82 76 3.0 5.5 17.8 124.6 442.8 1,081.1 2.4 1.2 1.6 115 179 496
Sierra Leone 68 – – 1.4 – – 33.3 – – 4.2 – – 59 – –
Kenya 79 73 72 2.0 3.7 6.4 68.7 90.8 147.6 2.9 4.1 4.3 64 104 148
Cameroon 73 64 64 5.4 9.2 12.6 158.6 173.4 215.9 3.4 5.3 5.8 177 273 313
D. R. Congo 65 63 63 3.0 4.8 6.9 66.6 89.8 117.8 4.5 5.3 5.9 69 106 167
Algeria 66 69 69 7.5 9.8 11.5 176.2 209.3 237.2 4.3 4.7 4.9 568 681 825
Netherlands 51 50 51 40.8 44.2 46.7 514.4 529.6 534.8 7.9 8.4 8.7 5,082 5,715 5,977
Iran 59 57 61 89.3 107.6 137.7 1,638.3 1,843.7 2,132.3 5.5 5.8 6.5 5,650 6,640 7,451
Mali 83 79 1.6 2.0 – 69.4 74.8 2.3 2.7 – 94 112 –
Chad 84 – – 0.9 – – 44.4 – – 2.0 – – 74 – –
Afghanistan 71 67 66 11.8 22.1 30.2 296.0 443.9 591.6 4.0 5.0 5.1 220 405 675
Peru 49 46 46 84.5 155.7 229.7 1,017.4 1,497.7 2,038.9 8.3 10.4 11.3 2,393 4,371 6,688
Sudan 75 77 8.3 9.7 – 266.9 311.7 3.1 3.1 – 506 604 –
Philippines 69 68 58 11.4 15.0 24.2 273.5 314.0 358.5 4.2 4.8 6.7 751 904 1,036
Brazil 76 66 54 177.6 411.8 802.8 5,729.2 8,243.9 10,800.0 3.1 5.0 7.4 12,400 25,598 40,919
Indonesia 72 72 70 15.4 24.5 36.4 425.5 583.9 762.3 3.6 4.2 4.8 1,028 1,496 2,048
Italy 50 48 49 187.3 215.9 228.7 2,287.7 2,412.9 2,485.6 8.2 8.9 9.2 25,085 29,958 32,616
Spain 47 47 48 215.2 230.7 239.4 2,381.4 2,247.5 2,345.9 9.0 10.3 10.2 24,824 27,563 27,127
India 63 65 59 82.0 173.8 320.9 1,675.2 3,312.0 4,874.1 4.9 5.2 6.6 2,649 4,971 9,195
Egypt 77 75 73 10.4 20.8 41.3 340.3 614.6 975.5 3.1 3.4 4.2 556 845 1,422
Belgium 56 55 57 50.3 55.8 58.7 823.7 847.7 857.3 6.1 6.6 6.8 7,924 9,108 9,522
Nigeria 74 70 67 8.9 15.2 24.1 266.1 339.2 486.8 3.4 4.5 4.9 259 399 558
France 61 58 62 126.8 140.7 149.1 2,350.2 2,424.9 2,468.3 5.4 5.8 6.0 23,660 27,074 28,662
Mexico 85 81 75 47.1 90.7 150.3 2,899.6 4,823.5 6,766.9 1.6 1.9 2.2 5,045 9,930 17,580
U.K. 58 56 56 186.6 246.4 278.0 3,403.7 3,778.5 3,971.6 5.5 6.5 7.0 28,446 34,796 39,369
U.S. 44 39 38 1,069.8 1,443.4 1,770.4 10,137.1 11,539.1 12,571.5 10.6 12.5 14.1 63,006 87,568 103,781
This ranking is made up of all countries with more than 30 deaths due to COVID-19 40 days after the pandemic outbreak. Countries are ranked by their detection rates 15 days after
the pandemic outbreak. Missing values in this table indicate that the country has not reached the requested number of days after its pandemic outbreak. Source: Own elaboration.
the above-mentioned countries, none of them is still ranked on
the top part of the ranking with 91 countries for which we have
full data (US in ranking 36, Spain 45, Italy 46, Belgium 55, UK
56, and France in place 58). Similarly, in this non-synchronic
ranking, with the exception of Russia, none of the first 10
countries accumulated more than 500 deaths on May 20.
In Table 3, we present the non-synchronic ranking as of 22
June. The US is in place 35, Spain 49, Italy 53, Belgium 63, UK
61, and France 67. It is noteworthy that, except for Russia, none
of the first 16 countries in this ranking have accumulated more
than 2,000 fatalities on 22 June. More importantly and despite
the incredible efforts to increase the tests amongst the more
developed countries, none of them were able to detect more than
16% of the estimated infections (the US detected 15.7% on 22
June). It implies that testing efforts need to be deployed at the
first stages of the pandemic process due to its cumulative nature.
Frontiers in Public Health | www.frontiersin.org 12 September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
TABLE 2 | Non-synchronic descriptive statistics as of 20 May.
Country Detection
rate
ranking
Confirmed
Cases
Estimated
Cases
Number
of Deaths
Detection
rate
(Percentage)
Country Detection
rate ranking
Confirmed
Cases
Estimated
Cases
Number
of Deaths
Detection
rate
(Percentage)
Australia 1 7,068 10,803 99 65.4 Macedonia 47 1,839 20,629 106 8.9
Serbia 2 10,733 24,472 234 43.9 Bangladesh 48 25,121 284,758 370 8.8
Russia 3 299,941 713,396 2,837 42.0 Peru 49 99,483 1,137,309 2,914 8.7
Thailand 4 3,034 7,293 56 41.6 Netherlands 50 44,249 529,581 5,715 8.4
Israel 5 16,650 42,239 277 39.4 Argentina 51 8,796 114,004 393 7.7
South Korea 6 11,110 28,757 263 38.6 Sweden 52 30,799 417,234 3,743 7.4
Norway 7 8,257 24,041 233 34.3 Hungary 53 3,598 50,265 470 7.2
Luxembourg 8 3,958 12,368 109 32.0 Colombia 54 16,935 256,149 613 6.6
Estonia 9 1,791 5,891 64 30.4 Belgium 55 55,791 847,676 9,108 6.6
Czech R. 10 8,647 30,105 302 28.7 U.K. 56 248,818 3,792,371 35,341 6.6
Japan 11 16,385 57,455 771 28.5 Iran 57 124,603 1,992,740 7,119 6.3
Austria 12 16,257 58,599 632 27.7 France 58 143,427 2,444,441 28,022 5.9
Malaysia 13 6,978 26,056 114 26.8 Pakistan 59 45,898 844,102 985 5.4
Germany 14 176,007 671,716 8,090 26.2 India 60 106,750 2,054,585 3,303 5.2
Portugal 15 29,432 113,959 1,247 25.8 South Africa 61 17,200 363,475 312 4.7
Lithuania 16 1,562 6,060 60 25.8 Philippines 62 12,942 287,923 837 4.5
Croatia 17 2,232 8,763 96 25.5 Libya 63 68 1,544 3 4.4
Finland 18 6,399 26,036 301 24.6 Ecuador 64 34,151 784,137 2,839 4.4
Puerto Rico 19 2,805 11,951 124 23.5 Algeria 65 7,377 173,847 561 4.2
Albania 20 949 4,088 31 23.2 Brazil 66 271,628 6,890,826 17,971 3.9
Ukraine 21 18,876 86,905 548 21.7 Bolivia 67 4,481 115,342 189 3.9
Denmark 22 11,044 52,134 551 21.2 Indonesia 68 18,496 480,800 1,221 3.8
Cuba 23 1,887 8,926 79 21.1 D. R. Congo 69 1,731 47,886 61 3.6
Greece 24 2,840 13,671 165 20.8 Somalia 70 1,502 44,338 59 3.4
Switzerland 25 30,535 147,794 1,613 20.7 South Sudan 71 285 8,421 6 3.4
Saudi Arabia 26 59,854 303,501 329 19.7 Egypt 72 13,484 401,828 659 3.4
Turkey 27 151,615 862,911 4,199 17.6 Honduras 73 2,955 88,824 147 3.3
Poland 28 19,268 114,170 948 16.9 Afghanistan 74 7,653 230,911 178 3.3
U. Arab E. 29 25,063 150,496 227 16.7 Nigeria 75 6,401 203,510 192 3.1
Bulgaria 30 2,292 14,227 116 16.1 Cameroon 76 3,529 113,118 140 3.1
Slovenia 31 1,467 9,206 104 15.9 Haiti 77 596 19,339 22 3.1
Morocco 32 7,023 44,481 193 15.8 Burkina Faso 78 806 30,682 52 2.6
Bosnia & H. 33 2,319 15,813 133 14.7 Suriname 79 11 429 1 2.6
Chile 34 49,579 359,514 509 13.8 Niger 80 914 37,746 55 2.4
Romania 35 17,191 125,640 1,126 13.7 Sierra Leone 81 534 24,508 33 2.2
U.S. 36 1,528,568 11,884,244 91,921 12.9 Guatemala 82 2,133 103,309 43 2.1
Panama 37 9,867 77,349 281 12.8 Kenya 83 963 49,412 50 1.9
China 38 84,065 667,702 4,638 12.6 Iraq 84 3,611 199,779 131 1.8
Moldova 39 6,340 51,912 221 12.2 Mexico 85 54,346 3,289,790 5,666 1.7
Ireland 40 24,251 203,128 1,561 11.9 Mali 86 901 57,558 53 1.6
Tunisia 41 1,044 8,865 47 11.8 Sudan 87 2,591 169,575 105 1.5
El Salvador 42 1,498 12,905 31 11.6 Chad 88 545 42,352 56 1.3
Dominican R. 43 13,223 116,836 441 11.3 Mauritania 89 81 27,361 4 0.3
Canada 44 79,101 763,889 5,912 10.4 Yemen 90 167 67,359 28 0.2
Spain 45 232,555 2,247,533 27,888 10.3 Nicaragua 91 25 14,739 8 0.2
Italy 46 226,699 2,472,703 32,169 9.2
Countries are ranked by the detection rates of SARS-CoV-2 infections as of 20 May. Source: Own elaboration.
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Villalobos How SARS-CoV-2 Detection Saves Lives
TABLE 3 | Non-synchronic descriptive statistics as of 22 June.
Country Detection
rate
ranking
Confirmed
Cases
Estimated
Cases
Number
of Deaths
Detection
rate
(Percentage)
Country Detection
rate ranking
Confirmed
Cases
Estimated
Cases
Number
of Deaths
Detection
rate
(Percentage)
Australia 1 7,461 11,478 102 65.0 Sweden 47 56,043 479,232 5,053 11.7
Russia 2 584,680 1,271,052 8,111 46.0 Peru 48 254,936 2,272,406 8,045 11.2
Puerto Rico 3 6,525 14,521 149 44.9 Spain 49 246,504 2,357,978 28,324 10.5
Thailand 4 3,148 7,301 58 43.1 Macedonia 50 5,106 49,133 238 10.4
South Korea 5 12,438 30,176 280 41.2 South Sudan 51 1,882 18,688 34 10.1
Israel 6 20,778 51,835 306 40.1 Pakistan 52 181,088 1,908,427 3,590 9.5
Norway 7 8,708 25,098 244 34.7 Italy 53 238,499 2,533,481 34,634 9.4
Estonia 8 1,981 5,896 69 33.6 Netherlands 54 49,593 538,868 6,090 9.2
Serbia 9 12,894 38,735 261 33.3 El Salvador 55 4,626 53,327 107 8.7
Luxembourg 10 4,120 12,508 110 32.9 Brazil 56 1,085,038 12,484,118 50,617 8.4
Czech R. 11 10,498 32,666 336 32.1 Nicaragua 57 2,014 24,386 64 8.3
Malaysia 12 8,572 27,040 121 31.7 South Africa 58 97,302 1,269,375 1,930 7.7
Portugal 13 39,133 127,864 1,530 30.6 Hungary 59 4,102 54,281 572 7.6
Japan 14 17,916 61,665 953 29.1 Suriname 60 314 4,170 8 7.5
Austria 15 17,285 61,817 690 28.0 U.K. 61 304,331 4,151,851 42,632 7.3
Lithuania 16 1,798 6,497 76 27.7 India 62 425,282 6,033,057 13,699 7.0
Germany 17 190,359 699,154 8,885 27.2 Belgium 63 60,550 861,976 9,696 7.0
Finland 18 7,143 26,402 326 27.1 Philippines 64 30,052 438,038 1,169 6.9
U. Arab E. 19 44,925 178,155 302 25.2 Iran 65 204,952 3,050,048 9,623 6.7
Cuba 20 2,312 9,281 85 24.9 Colombia 66 68,652 1,030,695 2,237 6.7
Ukraine 21 36,560 148,376 1,002 24.6 France 67 160,377 2,515,344 29,640 6.4
Chile 22 242,355 991,336 4,479 24.4 D. R. Congo 68 5,826 98,916 130 5.9
Croatia 23 2,317 9,957 107 23.3 Cameroon 69 11,610 199,080 301 5.8
Denmark 24 12,391 53,638 600 23.1 Bolivia 70 24,388 424,522 773 5.7
Greece 25 3,266 14,533 190 22.5 Somalia 71 2,779 49,186 90 5.7
Poland 26 31,931 150,582 1,356 21.2 Afghanistan 72 28,833 565,246 581 5.1
Switzerland 27 31,209 148,912 1,680 21.0 Indonesia 73 45,891 905,257 2,465 5.1
Saudi Arabia 28 157,612 823,639 1,267 19.1 Nigeria 74 20,244 409,327 518 4.9
Turkey 29 187,685 984,358 4,950 19.1 Honduras 75 12,769 263,032 363 4.9
Morocco 30 9,977 55,386 214 18.0 Algeria 76 11,771 242,645 845 4.9
Albania 31 1,927 11,300 44 17.1 Egypt 77 55,233 1,182,338 2,193 4.7
Bulgaria 32 3,905 23,586 199 16.6 Ecuador 78 50,640 1,084,641 4,223 4.7
Bangladesh 33 112,306 678,767 1,464 16.5 Kenya 79 4,738 109,829 123 4.3
Slovenia 34 1,520 9,410 109 16.2 Libya 80 544 12,846 10 4.2
U.S. 35 2,280,912 14,248,772 119,975 15.7 Sierra Leone 81 1,327 31,692 55 4.2
Moldova 36 14,200 93,470 473 15.2 Mauritania 82 2,813 75,687 108 3.7
Bosnia & H. 37 3,354 22,225 169 15.1 Guatemala 83 13,145 398,078 531 3.3
Panama 38 26,030 180,819 501 14.4 Sudan 84 8,580 276,728 521 3.1
Argentina 39 42,772 303,341 1,011 14.1 Burkina Faso 85 903 30,773 53 2.9
Romania 40 24,045 177,775 1,512 13.5 Mali 86 1,961 73,306 111 2.7
Dominican R. 41 26,677 197,251 662 13.5 Niger 87 1,036 39,912 67 2.6
Tunisia 42 1,157 9,144 50 12.7 Mexico 88 180,545 7,666,945 21,825 2.4
China 43 84,572 668,564 4,639 12.6 Chad 89 858 43,988 74 2.0
Ireland 44 25,379 208,366 1,715 12.2 Iraq 90 30,868 1,582,972 1,100 2.0
Canada 45 101,326 845,149 8,430 12.0 Yemen 91 941 203,732 256 0.5
Haiti 46 5,211 44,065 88 11.8
Countries are ranked by the detection rates of SARS-CoV-2 infections as of 22 June. Source: Own elaboration.
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Villalobos How SARS-CoV-2 Detection Saves Lives
FIGURE 6 | Linear prediction for the natural logarithm of the cumulative number of deaths from a linear regression of ln (deathsi)on the detection rates (DRi)15 and
120 days after the pandemic outbreak (PO). (A) Detection rates and deaths 15 days after the PO. (B) Detection rates and deaths 120 days after the PO. (A) contains
all 91 countries (in Table 1A). (B) contains all 61 countries (in Table 1A) whose pandemic processes have more than 120 days since the PO. The dashed fitted line
excludes South Korea (KR). Source: Own elaboration.
Tables 2,3show that moving over time from relatively low to
relatively high cumulative detection rates is unlikely and probably
very expensive. This is due to the over proportional efforts
needed to expand testing relative to the exponentially growing
infections at the early stages of the pandemic. Consequently, from
the public health point of view, it is much more advantageous,
technically, and economically feasible, to implement mass testing
from the very beginning of the pandemic process. To achieve
this goal, health authorities and governments would require
understanding the linkages between the cumulative detection
rates and the minimization of the pandemic related fatalities and
economic damage.
Unconditional Analysis
In this analysis, we show the unconditional relationship between
detection rates and deaths. The fitted lines in Figure 6 are
obtained after regressing the natural logarithm of the cumulative
number of deaths in the country ion their estimated cumulative
detection rates (DRi). The results strongly suggest a negative
relationship between detection rates and the cumulative number
of deaths. This strong negative slope is in concordance with
the hypothesis that, by detecting a higher proportion of the
SARS-CoV-2 infected population, many lives can be saved, in
particular, the lives of the elderly and those individuals with
preexisting conditions.
The strong association between the number of deaths and
the estimated cumulative detection rates remains significant 15,
and 120 days after the PO. These associations are shown in
Figures 6A,B, respectively.
Figure 7 shows the relationship between detection rates (15
and 120 days after the PO) and deaths 120 days after the
PO. This descriptive result is of interest since it suggests
that, unconditionally, early detection is associated with death
outcomes 120 days after the PO to a greater extent than the
contemporary detection rates, that is, 120 days after the PO.
Although this information suggests the existence of a strong
relationship between detection rates and the cumulative number
of deaths, this slope may be confounded by the variables
mentioned before. Thus, in the next section, we show the results
of our conditional analysis as described earlier.
Multivariate Regression Analysis
Our results in Table 4 show that higher detection rates are
associated with a reduction in the number of deaths after
controlling for demography (age-structure of the population and
population size), economic performance (GDP per capita), and
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Villalobos How SARS-CoV-2 Detection Saves Lives
FIGURE 7 | Linear prediction for the natural logarithm of the cumulative number of deaths 120 days after the pandemic outbreak (PO) from a linear regression of
ln (deathsi)on the detection rates (DRi)15 and 120 days after the PO. (A) Deaths reported 120 days after the PO, and detection rates estimated 15 days after the
PO. (B) Deaths reported 120 days after the PO, and detection rates 120 days after the PO. Note: This figure contains all 61 countries (in Table 1A) whose pandemic
processes have more than 120 days since the pandemic outbreak. Source: Own elaboration.
the relative resources that the economies devote to their health
systems. Over time, the cross-sectional regressions increase in
explanatory power, from a R-squared of 0.71 in model 2 to 0.95
in model 8.
Based on these results, Figure 8 shows a strong conditional
gradient between detection rates and the cumulative number of
deaths. For instance, for a hypothetical country with average and
constant endowments, the cost in terms of deaths of detecting 5%
vs. 35% is about 1.81 natural logarithm points which corresponds
to exp1.81 =6.13. That is, the average country detecting 5%
is associated with a number of deaths about 6.1 times higher
when compared with the same country detecting 35% of all
SARS-CoV-2 infections.
To put this result in perspective, let us simulate what would
be the number of deaths in the U.S., if instead of detecting
16.02% 120 days after the pandemic outbreak, the country would
have detected with the same intensity as South Korea (41.01%).
Evaluating the number of deaths at the endowments of the U.S,
the country would have fewer deaths by 1.14 natural logarithm
points. It means that the current U.S deaths are now 3.13 times
higher than they would be if the country would have tested with
similar intensity as South Korea. Since the number of deaths 120
days after the pandemic outbreak reached 126,140, detecting at
the rate of South Korea would have saved about 85,794 lives in
the U.S. at that time.
Finally, looking at the regression coefficients in Table 3, it is
noteworthy the fact that during the pandemic outbreak, a 1%
higher detection rate is associated with more lives saved than a
1% increase in the health expenditure over the GDP. Our results
also suggest that the number of deaths, rather than depending
on the relative solvency of the health system, could depend in a
greater extent on the size and opportunity of the testing efforts.
The conclusion is the more tests the better. Although in
this study we employed an economics inspired approach to
figure out the importance of testing, our findings are also
endorsed by recent medical literature on coronavirus as well as by
another economics inspired models providing support to a causal
relationship between detection and saving lives (4750).
Robustness of the Results
Robust regressions provide estimates that are close to the ones
reported in Table 4. Consequently, it is unlikely that the results
reported in this study are outlier driven. Additionally, results are
robust to heteroscedasticity of unknown form for small samples.
Nevertheless, results should be interpreted with caution. The
few observations available for the regressions and lack of data
does not allow to rule out the possibility that there are omitted
variables that have the potential to bias the results.
It is important to keep in mind that results can be
biased if omitted variable problem exists. That is, there are
variables that are correlated with the explained outcome
but at the same time they are also correlated with the
explanatory variables of interest. For instance, one can think in
countries implementing lockdowns because lower detection rates
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Villalobos How SARS-CoV-2 Detection Saves Lives
TABLE 4 | Synchronic multiple linear regression of the natural logarithm of the cumulative number of deaths on the estimated detections rates.
Dependent Variable:
Ln(deaths)/Explanatory
Variables
Days since the first 100 cases were confirmed
15 60 120
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8)
Estimated detection rate 0.193*** 0.225*** 0.120*** 0.118*** – 0.100*** 0.0976*** –
(0.0358) (0.0269) (0.0368) (0.0435) (0.0220) (0.0217)
Estimated detection rate (Squared) 0.00410*** 0.00497*** 0.00135 0.00132 0.000948* 0.000931*
(0.00127) (0.000698) (0.000919) (0.00107) (0.000501) (0.000484)
Estimated detection rate 15 days
after PO
22.30*** 16.78***
– (6.266) – (5.479)
Estimated detection rate 15 days
after PO (squared)
– 47.64* 35.63
– (28.54) – (24.01)
Infection fatality rate 0.960*** 0.922*** 1.586*** 1.512*** 1.396*** 1.525*** 1.506*** 1.439***
(0.333) (0.328) (0.267) (0.270) (0.370) (0.172) (0.179) (0.329)
Population size (Ln) 0.150** 0.146** 0.0285 0.0179 0.0105 0.0699** 0.0649* 0.0267
(0.0656) (0.0688) (0.0856) (0.0780) (0.0787) (0.0345) (0.0356) (0.0518)
Confirmed cases (Ln) 0.860*** 0.773*** 0.943*** 0.910*** 0.705*** 0.931*** 0.929*** 0.849***
(0.0995) (0.108) (0.0696) (0.0640) (0.0796) (0.0324) (0.0334) (0.0639)
GDP per capita (Ln) 0.446*** 0.417*** 0.0399 0.0570 0.0742 0.181*** 0.168** 0.0194
(0.108) (0.103) (0.0842) (0.0913) (0.138) (0.0600) (0.0642) (0.154)
Health spending as % of GDP 0.0570* 0.0552 0.0147 0.0270 0.000955 0.00186 0.0115 0.00210
(0.0330) (0.0354) (0.0231) (0.0260) (0.0277) (0.0143) (0.0163) (0.0312)
BCG group 2 0.441 0.175 0.124 0.185* 0.196
(0.323) (0.161) (0.206) (0.101) (0.233)
BCG group 3 0.396 0.0449 0.0185 0.185 0.197
(0.284) (0.235) (0.305) (0.165) (0.332)
BCG group 4 0.704*** – 0.193 0.205 0.0324 0.131
(0.220) (0.184) (0.209) (0.102) (0.213)
BCG group 5 0.411* – 0.172 0.175 0.0200 0.0653
(0.210) (0.152) (0.176) (0.0856) (0.178)
Constant 4.530*** 5.355*** 2.365 2.204 1.313 5.437*** 5.174*** 2.425*
(1.391) (1.434) (1.431) (1.442) (1.629) (0.679) (0.776) (1.362)
Observations 87 87 84 84 84 74 74 74
R-squared 0.672 0.708 0.950 0.954 0.934 0.984 0.985 0.954
R-squared adjusted 0.643 0.666 0.945 0.947 0.924 0.983 0.983 0.946
F-test 26 21.86 342.3 274.9 110.5 594.5 404.1 137.8
Standard errors in parentheses. Significance levels: ***p<0.01, **p<0.05, *p<0.1. Source: Own elaboration.
(Argentina), or relaxed social distancing rules because higher
detection rates (Australia). Nevertheless, these non-observed
variables yield to an underestimation of the true association
between detection rates and the cumulative number of deaths.
Thus, detection matters.
DISCUSSION
In this study, we have proposed a method to estimate the number
of SARS-CoV-2 infections for the globe and also for all 91 major
countries covering more than 86% of the world population. On
June 22, we find that, worldwide, about 160 million individuals
have been infected by SARS-CoV-2. Moreover, only about
1 out of 11 these infections have been detected. We find
that detection rates are very unequally distributed across the
globe and that they also increased over time from about 1%
during the second and third weeks of March to about 9% on
June 22. In an information context in which population-based
seroepidemiological studies are not available, this study shows a
parsimonious alternative to provide estimates of the number of
SARS-CoV-2 infected individuals. By comparing our estimates
with those provided by the ENE-COVID study in Spain, we
confirm the utility of our approach keeping in mind that from the
public health point of view, it is preferable to be vaguely right than
precisely wrong.
In order to provide reliable estimates of the number of SARS-
CoV-2 infections and of the cumulative detection rates, it is
necessary that governments provide real-time information about
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Villalobos How SARS-CoV-2 Detection Saves Lives
FIGURE 8 | Predictive Margins of the cumulative number of deaths at different
detection rates of SARS-CoV-2 infections after 120 days of the pandemic
outbreak. Source: Own elaboration.
the number of COVID-19 deaths. This study supports the view
that an accurate communication of the fatality cases can have
consequences on the development of the pandemic itself. Thus,
it is also a call for allowing international comparison following
WHO international norms and standards for medical certificates
of COVID-19 cause of death and International Classification of
Diseases (ICD) mortality coding.
Additionally, in our empirical analysis, we have presented
parsimonious evidence, that higher detection rates are associated
with saving lives. Our conditional analysis shows, for example,
that if the US would have had the same detection rate trajectory
as South Korea, about two-thirds of the reported deaths could
have been avoided (about 85,000 lives).
We find that detection rates at the very early stages of the
pandemic seem to explain the great divergence in terms of deaths
between countries. Moreover, we showed evidence that moving
from relatively low to high cumulative detection rates (and thus
saving lives) is unlikely and difficult. This is probably due to
the high level of efforts needed to expand testing relative to
the exponentially growing infections at the early and middle
stages of the pandemic. Thus, from the public health point
of view, it is better to deploy testing efforts at the first stages
of the pandemic process. To do this would be much more
advantageous, in terms of saved lives, but also it would be
technically, and economically feasible.
Already, many developed countries with well-developed
health sectors were not able to avoid unnecessary deaths by
their inaction in terms of promoting mass testing to counter the
pandemic outbreak at early stages.
To achieve the goal of implementing mass testing from the
very beginning of the pandemic outbreak, governments need
to understand the consequences of not doing that. Thus, the
evidence presented in this paper offers a rigorous macro-level
linkage between detection rates and the cumulative number of
deaths which may be useful in future pandemics. This evidence
also supports the implementation of mass testing in the likely
coming secondary pandemic outbreak (so-called second waves).
Further research should be devoted to understanding why the
detection capacity in many advanced countries was too weak,
late, and also so weakly correlated (if correlated) with the income
levels. In this paper, we claim that governments have incentives
against test because the public opinion tends to primarily react
to the report of the cumulative and the marginal numbers of
detected (reported) cases. The contradiction is that something
good, such as the increase in the testing efforts by governments,
can be perceived by the general public as something negative
(due to the increase in detections). In consequence, are low
detection rates in developed countries simply a management
failure, or are there long-run incentives that promoted this
behavior among many rich countries? It is clear that during the
ongoing pandemic, improving detection rates is a race against
time, but are there institutional and/or technological constraints
that hamper detection improvements that can save lives? All these
questions are relevant for this and future pandemics. This study
claims that all countries in the world should be able to respond
to a pandemic outbreak with massive testing in the very short
run. This would be an efficient approach since it is also likely that
higher detection rates are also associated with a lesser impact of
the pandemic on the economy.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
AUTHOR CONTRIBUTIONS
CV conceived this research, performed the background work,
collected the data, performed all statistical analyses, and wrote
the paper.
ACKNOWLEDGMENTS
The author would like to thank and acknowledge Dr. Carlos
Chavez for comments of a very early version of this paper. I
would also like to thank M.Sc. Andrea Torres for their comments
on the implications of this research. The author also would like
to recognize the suggestions and comments provided by the
participants at the Doctoral Seminar at Facultad de Economía y
Negocios, Universidad de Talca.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpubh.
2020.00489/full#supplementary-material
Supplementary Set of Figures. Estimated number of SARS-
CoV-2 infections by country.
Supplementary Table 1 | Synchronic multiple linear regression of the natural
logarithm of the cumulative number of deaths on the estimated detections rates
(linear specification).
Frontiers in Public Health | www.frontiersin.org 18 September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
Supplementary Table 2 | Synchronic multiple linear regression of the natural
logarithm of the cumulative number of deaths on the estimated number of
SARS-CoV-2 infections.
Supplementary Table 3 | Synchronic robust multiple linear regression of the
natural logarithm of the cumulative number of deaths on the estimated detections
rates (linear specification).
Supplementary Table 4 | Synchronic robust multiple linear regression of the
natural logarithm of the cumulative number of deaths on the estimated detections
rates (non-linear specification).
Supplementary Table 5 | Synchronic multiple linear regression of the natural
logarithm of the cumulative number of deaths on the estimated number of
SARS-CoV-2 infections.
REFERENCES
1. Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, et al. Substantial undocumented
infection facilitates the rapid dissemination of novel coronavirus (SARS-
CoV2). Science. (2020) 493:489–93. doi: 10.1126/science.abb3221
2. Jombart T, van Zandvoort K, Russell T, Jarvis C, Gimma A, Abbott S, et
al. Inferring the number of COVID-19 cases from recently reported deaths.
Wellcome Open Res. (2020) 5:78. doi: 10.12688/wellcomeopenres.15786.1
3. Lourenco J, Paton R, Ghafari M, Kraemer M, Thompson C, Simmonds P, et al.
Fundamental principles of epidemic spread highlight the immediate need for
large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic.
medRxiv. (2020) 2020.03.24.20042291. doi: 10.1101/2020.03.24.20042291
4. Thorpe DG, Lyberger K. Estimating the number of SARS-
CoV-2 infections in the United States. medRxiv. (2020)
2020:04.13.20064519. doi: 10.1101/2020.04.13.20064519
5. Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, et al.
Estimating the effects of non-pharmaceutical interventions on COVID-19 in
Europe. Nature. (2020) 584:257–61. doi: 10.1038/s41586-020-2405-7
6. Xu J, Li Y, Gan F, Du Y, Yao Y. Salivary glands: potential
reservoirs for COVID-19 asymptomatic infection. J Dent Res. (2020)
99:989. doi: 10.1177/0022034520918518
7. Zou L, Ruan F, Huang M, Liang L, Huang H, Hong Z, et al. SARS-CoV-2 viral
load in upper respiratory specimens of infected patients. N Engl J Med. (2020)
382:1177–9. doi: 10.1056/NEJMc2001737
8. Aguilar JB, Faust JS, Westafer LM, Gutierrez JB. Investigating the
impact of asymptomatic carriers on COVID-19 transmission. medRxiv.
(2020) doi: 10.1101/2020.03.18.20037994
9. Nishiura H, Linton NM, Akhmetzhanov AR. Serial interval of novel
coronavirus (COVID-19) infections. Int J Infect Dis. (2020) 93:284–
6. doi: 10.1016/j.ijid.2020.02.060
10. Day M. Covid-19: identifying and isolating asymptomatic
people helped eliminate virus in Italian village. BMJ. (2020)
368:m1165. doi: 10.1136/bmj.m1165
11. Bai Y, Yao L, Wei T, Tian F, Jin DY, Chen L, et al. Presumed
asymptomatic carrier transmission of COVID-19. JAMA. (2020) 323:1406–
7. doi: 10.1001/jama.2020.2565
12. Hu Z, Song C, Xu C, Jin G, Chen Y, Xu X, et al. Clinical
characteristics of 24 asymptomatic infections with COVID-19 screened
among close contacts in Nanjing, China. Sci China Life Sci. (2020)
63:706–11. doi: 10.1007/s11427-020-1661-4
13. Tian S, Hu N, Lou J, Chen K, Kang X, Xiang Z, et al.
Characteristics of COVID-19 infection in Beijing. J Infect. (2020)
80:401–6. doi: 10.1016/j.jinf.2020.02.018
14. Lin C, Ding Y, Xie B, Sun Z, Li X, Chen Z, et al. Asymptomatic
novel coronavirus pneumonia patient outside Wuhan: the value of
CT images in the course of the disease. Clin Imaging. (2020) 63:7–
9. doi: 10.1016/j.clinimag.2020.02.008
15. Lai CC, Liu YH, Wang CY, Wang YH, Hsueh SC, Yen MY,et al. Asymptomatic
carrier state, acute respiratory disease, and pneumonia due to severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2): facts and myths. J
Microbiol Immunol Infect. (2020) 53:404–12. doi: 10.1016/j.jmii.2020.02.012
16. Jin J-M, Bai P, He W, Wu F, Liu X-F, Han D-M, et al. Gender differences in
patients with COVID-19: focus on severity and mortality. Front Public Health.
(2020) 8:152. doi: 10.1101/2020.02.23.20026864
17. Chen Y, Li L. SARS-CoV-2: virus dynamics and host response. Lancet Infect
Dis. (2020) 20:15–6. doi: 10.1016/S1473-3099(20)30235-8
18. Guo YR, Cao QD, Hong ZS, Tan YY, Chen SD, Jin HJ, et al. The
origin, transmission and clinical therapies on coronavirus disease 2019
(COVID-19) outbreak- an update on the status. Military Med Res. (2020)
7:11. doi: 10.1186/s40779-020-00240-0
19. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical
characteristics of coronavirus disease 2019 in China. N Engl J Med. (2020)
7:11. doi: 10.1101/2020.02.06.20020974
20. Jiang F, Deng L, Zhang L, Cai Y, Cheung CW, Xia Z. Review
of the clinical characteristics of coronavirus disease 2019 (COVID-
19). J Gen Intern Med. (2020) 35:1545–9. doi: 10.1007/s11606-020-
05762-w
21. Wax RS, Christian MD. Practical recommendations for critical care
and anesthesiology teams caring for novel coronavirus (2019-nCoV)
patients. Can J Anesth. (2020) 67:568–76. doi: 10.1007/s12630-020-
01591-x
22. The Lancet. COVID-19: protecting health-care workers. Lancet. (2020)
395:922. doi: 10.1016/S0140-6736(20)30644-9
23. Lipsitch M, Donnelly CA, Fraser C, Blake IM, Cori A, Dorigatti
I, et al. Potential biases in estimating absolute and relative
case-fatality risks during outbreaks. PLoS Negl Trop Dis. (2015)
9:e0003846. doi: 10.1371/journal.pntd.0003846
24. Garske T, Legrand J, Donnelly CA, Ward H, Cauchemez S, Fraser C, et al.
Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ. (2009)
339:b2840. doi: 10.1136/bmj.b2840
25. Donnelly CA, Ghani AC, Leung GM, Hedley AJ, Fraser C, Riley S,
et al. Epidemiological determinants of spread of causal agent of severe
acute respiratory syndrome in Hong Kong. Lancet. (2003) 361:1761–
6. doi: 10.1016/S0140-6736(03)13410-1
26. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al.
Estimates of the severity of coronavirus disease 2019: a model-based analysis.
Lancet Infect Dis. (2020) 3099:1–9. doi: 10.1016/s1473-3099(20)30243-7
27. Jung S, Akhmetzhanov AR, Hayashi K, Linton NM, Yang Y, Yuan B,
et al. Real-time estimation of the risk of death from novel coronavirus
(COVID-19) infection: inference using exported cases. J Clin Med. (2020)
9:523. doi: 10.3390/jcm9020523
28. Wu P, Hao X, Lau EHY, Wong JY,Leung KSM, Wu JT, et al. Real-time tentative
assessment of the epidemiological characteristics of novel coronavirus
infections in Wuhan, China, as at 22 January (2020). Eurosurveillance. (2020)
25:20000444. doi: 10.2807/1560-7917.ES.2020.25.3.2000044
29. World Health Organization. Medical Certification, ICD Mortality Coding, and
Reporting Mortality Associated With COVID-19. Tech Note. (2020). p. 1–
13. Available online at: https://www.who.int/publications/i/item/WHO-2019-
nCoV-mortality-reporting- 2020-1
30. Meyerowitz-Katz G, Merone L. A systematic review and meta-analysis of
published research data on COVID-19 infection-fatality rates. MedRxiv.
(2020) 2020:05.03.20089854. doi: 10.1101/2020.05.03.20089854
31. Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The
incubation period of coronavirus disease 2019. (COVID-19) from publicly
reported confirmed cases: estimation and application. Ann Intern Med. (2020).
172:577–82. doi: 10.7326/M20-0504
32. Bommer C, Vollmer S. Average Detection Rate of SARS-CoV-2 Infections Is
Estimated Around Nine Percent. University of Goettingen (2020). Available
online at: http://www.uni-goettingen.de/en/606540.html
33. Jing Q-L, Liu M-J, Yuan J, Zhang Z-B, Zhang A-R, Dean NE, et al. Household
secondary attack rate of COVID-19 and associated determinants. medRxiv.
(2020) 2020:04.11.20056010. doi: 10.1016/S1473-3099(20)30471-0
34. Streeck H, Schulte B, Kuemmerer B, Richter E, Hoeller T, Fuhrmann
C, et al. Infection fatality rate of SARS-CoV-2 infection in a
German community with a super-spreading event. medRxiv. (2020)
2020:05.04.20090076. doi: 10.1101/2020.05.04.20090076
Frontiers in Public Health | www.frontiersin.org 19 September 2020 | Volume 8 | Article 489
Villalobos How SARS-CoV-2 Detection Saves Lives
35. 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. medRxiv. (2020) doi: 10.1101/2020.04.24.20075291
36. Ioannidis J. The infection fatality rate of COVID-
19 inferred from seroprevalence data. medRxiv. (2020)
2020:05.13.20101253. doi: 10.1101/2020.05.13.20101253
37. World Health Organization. Bacille Calmette-Guérin (BCG) vaccination and
COVID-19. Sci Br. (2020). 36:2019–20. doi: 10.15557/PiMR.2020.0025
38. Singh S. BCG vaccines may not reduce COVID-19 mortality rates. medRxiv.
(2020) 2020:04.11.20062232. doi: 10.1101/2020.04.11.20062232
39. Bodova K, Boza V, Brejova B, Kollar R, Mikusova K, Vinar T.
Time-adjusted analysis shows weak associations between BCG
vaccination policy and COVID-19 disease progression. medRxiv. (2020)
2020:05.01.20087809. doi: 10.1101/2020.05.01.20087809
40. Klinger D, Blass I, Rappoport N, Linial M. Significantly improved COVID-19
outcomes in countries with higher BCG vaccination coverage: a multivariable
analysis. Vaccines. (2020) 8:378. doi: 10.3390/vaccines8030378
41. Szigeti R, Kellermayer D, Kellermayer R. BCG protects
against COVID-19? A word of caution. medRxiv. (2020)
2020:04.09.20056903. doi: 10.1101/2020.04.09.20056903
42. Paredes Mogica JA, Nava V, Torres J. COVID-19 related
mortality: is the BCG vaccine truly effective? medRxiv. (2020)
2020:05.01.20087411. doi: 10.1101/2020.05.01.20087411
43. Long JS, Ervin LH. Using heteroscedasticity consistent standard
errors in the linear regression model. Am Stat. (2010) 54:217–
24. doi: 10.1080/00031305.2000.10474549
44. Hamilton L. How robust is robust regression. Stata Tech Bull. (1991) 2:21–26.
45. 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)
396:P313–19. doi: 10.1016/S0140-6736(20)31304-0
46. Pollán M, Pérez-Gómez B, Pastor-Barriuso R, Oteo J, Hernán MA, Pérez-
Olmeda M, et al. Prevalence of SARS-CoV-2 in Spain (ENE-COVID):
a nationwide, population-based seroepidemiological study. Lancet. (2020)
6736:1–11. doi: 10.1016/S0140-6736(20)31483-5
47. Huff HV, Singh A. Asymptomatic transmission during the COVID-19
pandemic and implications for public health strategies. Clin Infect Dis. (2020)
ciaa654. doi: 10.1093/cid/ciaa654
48. Baggett TP, Keyes H, Sporn N, Gaeta JM. Prevalence of SARS-CoV-2 infection
in residents of a large homeless shelter in Boston. JAMA. (2020) 323:2191–
2. doi: 10.1001/jama.2020.6887
49. Marcel S, Christian AL, Richard N, Silvia S, Emma H, Jacques F, et al. COVID-
19 epidemic in Switzerland: on the importance of testing, contact tracing
and isolation. Swiss Med Wkly. (2020) 150:12–5. doi: 10.4414/smw.2020.
20225
50. Brotherhood L, Kircher P, Santos C, Tertilt M. An economic model of the
Covid-19 epidemic: the importance of testing and age-specific policies. CRC
TR 224 Discussion Paper Series crctr224_2020_175. University of Bonn and
University of Mannheim (2020). Available online at: https://www.iza.org/
publications/dp/13265/an-economic- model-of- the-covid-19- epidemic-the-
importance-of- testing-and- age-specific- policies
Conflict of Interest: The author declares that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Villalobos. This is an open-access article distributed under the
terms of the Creative Commons Attribution License (CC BY). The use, distribution
or reproduction in other forums is permitted, provided the original author(s) and
the copyright owner(s) are credited and that the original publication in this journal
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... However, for instance in Germany, delays between isolation/testing of index cases and notification of a contact are expected to be larger (see Methods), while only 40% of users uploaded positive test results to the app [25]. Similarly, the success of the intervention relies on successful identification and isolation of index cases [10,20,22] and many studies assumed that around 50% or more of infected individuals would be identified/isolated and could potentially trigger digital contact tracing [14-16, 18, 19], yet ascertainment rates were suspected to be of lower value in many countries [26][27][28]. ...
... Apart from symptom-based testing and DCT, we do not consider the influence of other specific pharmaceutical or non-pharmaceutical interventions on the under-ascertainment factor. [26][27][28], see Fig A ii in S2 Text) we find that DCT alone leads to reductions in outbreak sizes on the order of � 5 − 8%. The only network with larger effects (� 13%) is the WS network, indicating that high local clustering increases the effects of DCT. ...
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Digital contact tracing (DCT) applications have been introduced in many countries to aid the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). However, no country was able to prevent larger outbreaks without falling back to harsher NPIs. Here, we discuss results of a stochastic infectious-disease model that provide insights in how the progression of an outbreak and key parameters such as detection probability, app participation and its distribution, as well as engagement of users impact DCT efficacy informed by results of empirical studies. We further show how contact heterogeneity and local contact clustering impact the intervention’s efficacy. We conclude that DCT apps might have prevented cases on the order of single-digit percentages during single outbreaks for empirically plausible ranges of parameters, ignoring that a substantial part of these contacts would have been identified by manual contact tracing. This result is generally robust against changes in network topology with exceptions for homogeneous-degree, locally-clustered contact networks, on which the intervention prevents more infections. An improvement of efficacy is similarly observed when app participation is highly clustered. We find that DCT typically averts more cases during the super-critical phase of an epidemic when case counts are rising and the measured efficacy therefore depends on the time of evaluation.
... Previous research has shown that contact tracing barely reduces viral propagation when the detection rate is only 13%, and the effectiveness of contact tracing increases significantly when the detection rate increases to 26% or 37% [40]. In June 2020, the average detection rate worldwide was <10% and varied significantly among countries [41]. Therefore, the use of CTAs may not be effective in reducing viral propagation in most countries. ...
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Background: In the post-coronavirus disease (COVID-19) pandemic era, many countries launched apps to trace contacts of COVID-19 infections. Each contact tracing application (CTA) faces a variety of issues owing to different national policies or technologies to trace contacts. Objective: We aimed to investigate all CTAs used to trace contacts in various countries worldwide, including the technology used by each CTA, the availability of knowledge information from official websites, the interoperability of the CTAs in various countries, the infection detection rates and policies of the specific country, and to summarize the current problems of the application based on the information collected. Methods: We investigated CTAs launched in all countries through Google, Google Scholar, and PUBMED. After eliminating CTAs that did not meet the exclusion criteria, we experimented with all applications that could be installed and complemented with information about applications that could not be installed or used by consulting official websites and previous literature. We compared the collected information on CTAs with relevant previous literature to understand and analyze the data. Results: After screening 166 COVID-19 applications developed in 197 countries worldwide, we selected 98 applications from 95 countries, among these, 63 apps were usable. The methods of contact tracing are divided into three main categories: Bluetooth, geolocation, and QR codes. Each method is further categorized depending on the protocol. At the technical level, CTAs face three problems. First, the distance and time for Bluetooth/geolocation-based CTAs to record contact are generally set to 2m/15min; however, this distance should be lengthened and the time should be shortened for more infectious variants. Second, Bluetooth/geolocation-based CTAs also face the problem of lack of accuracy. For example, individuals in two adjacent vehicles during traffic jams may be at a distance of ≤ 2 m to make CTA trace contact, and the two users may actually be separated car doors, which could prevent transmission and infection. This study identified several existing techniques that can improve the accuracy of CTA. Additionally, we investigated infection detection rates in 33 countries, 16 of which had significantly low infection detection rates and wherein CTAs could have lacked effectiveness in reducing virus propagation. Regarding policy, CTAs in most countries can only be used in their own countries and lack interoperability among others countries. In addition, seven countries have already discontinued CTAs, but we believe that it is too early to discontinue them. Regarding user acceptance, 28 out of 98 CTAs had no official source of knowledge, which could reduce user acceptance. Conclusions: We surveyed all CTAs worldwide and identified their technical, policy, and acceptance issues, and provided solutions for each of the issues we identified. This study aimed to provide useful guidance and suggestions for updating existing CTAs and the subsequent development of new CTAs. Clinicaltrial:
... A myriad of studies has been conducted to investigate the distribution, spread and rate of SARS-CoV-2 infection throughout the world, revealing differences in pandemic mitigation strategies between countries, populations, and regions that all urgently need to be addressed (41)(42)(43)(44). Meanwhile, a multitude of factors contributing to the transmission of infection and disease outcomes continue to emerge. ...
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Sardinia has one of the lowest incidences of hospitalization and related mortality in Europe and yet a very high frequency of the Neanderthal risk locus variant on chromosome 3 (rs35044562), considered to be a major risk factor for a severe SARS-CoV-2 disease course. We evaluated 358 SARS-CoV-2 patients and 314 healthy Sardinian controls. One hundred and twenty patients were asymptomatic, 90 were pauci-symptomatic, 108 presented a moderate disease course and 40 were severely ill. All patients were analyzed for the Neanderthal-derived genetic variants reported as being protective (rs1156361) or causative (rs35044562) for severe illness. The β°39 C>T Thalassemia variant (rs11549407), HLA haplotypes, KIR genes, KIRs and their HLA class I ligand combinations were also investigated. Our findings revealed an increased risk for severe disease in Sardinian patients carrying the rs35044562 high risk variant [OR 5.32 (95% CI 2.53 - 12.01), p = 0.000]. Conversely, the protective effect of the HLA-A*02:01, B*18:01, DRB*03:01 three-loci extended haplotype in the Sardinian population was shown to efficiently contrast the high risk of a severe and devastating outcome of the infection predicted for carriers of the Neanderthal locus [OR 15.47 (95% CI 5.8 – 41.0), p < 0.0001]. This result suggests that the balance between risk and protective immunogenetic factors plays an important role in the evolution of COVID-19. A better understanding of these mechanisms may well turn out to be the biggest advantage in the race for the development of more efficient drugs and vaccines.
... Since the publishing of the Liang study, and the data analysis for this current study, there have been other studies with coinciding results suggesting that higher testing frequency has the potential to significantly lower COVID-19 case-fatality rate and prevent a significant number of deaths [8][9][10][11][12][13][14]. However, none of these studies specifically focus on the worldwide association between testing and COVID-19 fatality or the association in all Latin American countries, which is why we chose to focus on the Liang study and use their methods as a foundation for our study and a point of comparison to study this association as the pandemic evolved. ...
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SARS-CoV-2 has infected over one hundred million people worldwide and has affected Latin America particularly severely in terms of both cases and deaths. This study aims to determine the association between SARS-CoV-2 testing and COVID-19 fatality rate worldwide over 8 months and to examine how this relationship differs between Latin America and all other countries. This cross-sectional study used March 2021 data from 169 countries. Multivariate regressions predicted COVID-19 fatality (outcome) from the number of SARS-CoV-2 tests (exposure), while controlling for other predictors. Results for March 2021 were compared to results from June 2020. Additionally, results for Latin America were also compared to all other countries except Latin American for March 2021. SARS-CoV-2 testing was associated with a significant decrease in COVID-19 fatality rate in both June 2020 and March 2021 (RR = 0.92; 95% CI 0.87-0.96 and RR = 0.86; 95% CI 0.74-1.00, respectively). SARS-CoV-2 testing was associated with a significant decrease in COVID-19 fatality rate in Latin American countries but not in all other countries (RR = 0.45; 95% CI 0.23-0.89 and RR = 0.95; 95% CI 0.82-1.11, respectively). However, the difference between the risk ratios for June 2020 and March 2021 and between the risk ratios for Latin America and all other countries were not statistically significant. Increased SARS-CoV-2 testing may be a significant predictor of lower COVID-19 case fatality rate, specifically in Latin American countries, due to the existence of a strong association, which may have driven the worldwide results.
... Consequently, only half of poly-symptomatic agents trigger contact tracing, the other half is not tested/detected despite being self-isolated. 2. IDR 2 : (26% on average) This IDR is only reported for 18 out of the 91 countries 12 . In SERIA, corresponds to detecting all symptomatic agents. ...
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A plethora of measures are being combined in the attempt to reduce SARS-CoV-2 spread. Due to its sustainability, contact tracing is one of the most frequently applied interventions worldwide, albeit with mixed results. We evaluate the performance of digital contact tracing for different infection detection rates and response time delays. We also introduce and analyze a novel strategy we call contact prevention, which emits high exposure warnings to smartphone users according to Bluetooth-based contact counting. We model the effect of both strategies on transmission dynamics in SERIA, an agent-based simulation platform that implements population-dependent statistical distributions. Results show that contact prevention remains effective in scenarios with high diagnostic/response time delays and low infection detection rates, which greatly impair the effect of traditional contact tracing strategies. Contact prevention could play a significant role in pandemic mitigation, especially in developing countries where diagnostic and tracing capabilities are inadequate. Contact prevention could thus sustainably reduce the propagation of respiratory viruses while relying on available technology, respecting data privacy, and most importantly, promoting community-based awareness and social responsibility. Depending on infection detection and app adoption rates, applying a combination of digital contact tracing and contact prevention could reduce pandemic-related mortality by 20–56%.
... Multiple models have been proposed to evaluate CCF using, for instance, the number of deceased patients 29,30 , but in all those studies, the results depend on the models used or on estimates of country-specific parameters, such as the age dependence or the Infection fatality rate. We have presented a method to estimate the fraction of undiagnosed infected from the fraction of infected with a known contaminator (out of all infected). ...
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In times of outbreaks, an essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals. An accurate estimate of this fraction is crucial for the assessment of the situation and the establishment of protective measures. In most current studies using epidemics models, the total number of infected is either approximated by the number of diagnosed individuals or is dependent on the model parameters and assumptions, which are often debated. We here study the relationship between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected. We show that those two are approximately the same in exponential models and across most models currently used in the study of epidemics, independently of the model parameters. As an application, we compute an estimate of the effective number of infected by the SARS-CoV-2 virus in various countries.
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An important unknown during the coronavirus disease-2019 (COVID-19) pandemic has been the infection fatality rate (IFR). This differs from the case fatality rate (CFR) as an estimate of the number of deaths and as a proportion of the total number of cases, including those who are mild and asymptomatic. While the CFR is extremely valuable for experts, IFR is increasingly being called for by policy makers and the lay public as an estimate of the overall mortality from COVID-19. Methods Pubmed, Medline, SSRN, and Medrxiv were searched using a set of terms and Boolean operators on 25/04/2020 and re-searched on 14/05/2020, 21/05/2020 and 16/06/2020. Articles were screened for inclusion by both authors. Meta-analysis was performed in Stata 15.1 by using the metan command, based on IFR and confidence intervals extracted from each study. Google/Google Scholar was used to assess the grey literature relating to government reports. Results After exclusions, there were 24 estimates of IFR included in the final meta-analysis, from a wide range of countries, published between February and June 2020. The meta-analysis demonstrated a point estimate of IFR of 0.68% (0.53%–0.82%) with high heterogeneity (p < 0.001). Conclusion Based on a systematic review and meta-analysis of published evidence on COVID-19 until July 2020, the IFR of the disease across populations is 0.68% (0.53%–0.82%). However, due to very high heterogeneity in the meta-analysis, it is difficult to know if this represents a completely unbiased point estimate. It is likely that, due to age and perhaps underlying comorbidities in the population, different places will experience different IFRs due to the disease. Given issues with mortality recording, it is also likely that this represents an underestimate of the true IFR figure. More research looking at age-stratified IFR is urgently needed to inform policymaking on this front.
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The COVID-19 pandemic, caused by type 2 Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), puts all of us to the test. Epidemiologic observations could critically aid the development of protective measures to combat this devastating viral outbreak. Recent observations, linked nation based universal Bacillus Calmette-Guerin (BCG) vaccination to potential protection against morbidity and mortality from SARS-CoV-2, and received much attention in public media. We wished to validate the findings by examining the country based association between COVID-19 mortality per million population, or daily rates of COVID-19 case fatality (i.e. Death Per Case/Days of the endemic [dpc/d]) and the presence of universal BCG vaccination before 1980, or the year of the establishment of universal BCG vaccination. These associations were examined in multiple regression modeling based on publicly available databases on both April 3rd and May 15th of 2020. COVID-19 deaths per million negatively associated with universal BCG vaccination in a country before 1980 based on May 15th data, but this was not true for COVID-19 dpc/d on either of days of inquiry. We also demonstrate possible arbitrary selection bias in such analyses. Consequently, caution should be exercised amidst the publication surge on COVID-19, due to political/economical-, arbitrary selection-, and fear/anxiety related biases, which may obscure scientific rigor. We argue that global COVID-19 epidemiologic data is unreliable and therefore should be critically scrutinized before using it as a nidus for subsequent hypothesis driven scientific discovery.
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Significance In early 2020, delays in availability of diagnostic testing for COVID-19 prompted questions about the extent of unobserved community transmission in the United States. We quantified unobserved infections in the United States during this time using a stochastic transmission model. Although precision of our estimates is limited, we conclude that many more thousands of people were infected than were reported as cases by the time a national emergency was declared and that fewer than 10% of locally acquired, symptomatic infections in the United States may have been detected over a period of a month. This gap in surveillance during a critical phase of the epidemic resulted in a large, unobserved reservoir of infection in the United States by early March.
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The COVID-19 pandemic that started in China has spread within 3 months to the entire globe. We tested the hypothesis that the vaccination against tuberculosis by Bacille Calmette–Guérin vaccine (BCG) correlates with a better outcome for COVID-19 patients. Our analysis covers 55 countries complying with predetermined thresholds on the population size and number of deaths per million (DPM). We found a strong negative correlation between the years of BCG administration and the DPM along with the progress of the pandemic, corroborated by permutation tests. The results from multivariable regression tests with 23 economic, demographic, health-related, and pandemic restriction-related quantitative properties, substantiate the dominant contribution of BCG years to the COVID-19 outcomes. The analysis of countries according to an age-group partition reveals that the strongest correlation is attributed to the coverage in BCG vaccination of the young population (0–24 years). Furthermore, a strong correlation and statistical significance are associated with the degree of BCG coverage for the most recent 15 years, but no association was observed in these years for other broadly used vaccination protocols for measles and rubella. We propose that BCG immunization coverage, especially among the most recently vaccinated population, contribute to attenuation of the spread and severity of the COVID-19 pandemic.
Article
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Background: The pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has tremendous consequences for our societies. Knowledge of the seroprevalence of SARS-CoV-2 is needed to accurately monitor the spread of the epidemic and to calculate the infection fatality rate (IFR). These measures may help the authorities to make informed decisions and adjust the current societal interventions. The objective was to perform nationwide real-time seroprevalence surveying among blood donors as a tool to estimate previous SARS-CoV-2 infections and the population based IFR. Methods: Danish blood donors aged 17-69 years giving blood April 6 to May 3 were tested for SARS-CoV-2 immunoglobulin M and G antibodies using a commercial lateral flow test. Antibody status was compared between geographical areas and an estimate of the IFR was calculated. The seroprevalence was adjusted for assay sensitivity and specificity taking the uncertainties of the test validation into account when reporting the 95% confidence intervals (CI). Results: The first 20,640 blood donors were tested and a combined adjusted seroprevalence of 1.9% (CI: 0.8-2.3) was calculated. The seroprevalence differed across areas. Using available data on fatalities and population numbers a combined IFR in patients younger than 70 is estimated at 89 per 100,000 (CI: 72-211) infections. Conclusions: The IFR was estimated to be slightly lower than previously reported from other countries not using seroprevalence data. The IFR is likely several fold lower than the current estimate. We have initiated real-time nationwide anti-SARS-CoV-2 seroprevalence surveying of blood donations as a tool in monitoring the epidemic.
Article
Background Spain is one of the European countries most affected by the COVID-19 pandemic. Serological surveys are a valuable tool to assess the extent of the epidemic, given the existence of asymptomatic cases and little access to diagnostic tests. This nationwide population-based study aims to estimate the seroprevalence of SARS-CoV-2 infection in Spain at national and regional level. Methods 35 883 households were selected from municipal rolls using two-stage random sampling stratified by province and municipality size, with all residents invited to participate. From April 27 to May 11, 2020, 61 075 participants (75·1% of all contacted individuals within selected households) answered a questionnaire on history of symptoms compatible with COVID-19 and risk factors, received a point-of-care antibody test, and, if agreed, donated a blood sample for additional testing with a chemiluminescent microparticle immunoassay. Prevalences of IgG antibodies were adjusted using sampling weights and post-stratification to allow for differences in non-response rates based on age group, sex, and census-tract income. Using results for both tests, we calculated a seroprevalence range maximising either specificity (positive for both tests) or sensitivity (positive for either test). Findings Seroprevalence was 5·0% (95% CI 4·7–5·4) by the point-of-care test and 4·6% (4·3–5·0) by immunoassay, with a specificity–sensitivity range of 3·7% (3·3–4·0; both tests positive) to 6·2% (5·8–6·6; either test positive), with no differences by sex and lower seroprevalence in children younger than 10 years (<3·1% by the point-of-care test). There was substantial geographical variability, with higher prevalence around Madrid (>10%) and lower in coastal areas (<3%). Seroprevalence among 195 participants with positive PCR more than 14 days before the study visit ranged from 87·6% (81·1–92·1; both tests positive) to 91·8% (86·3–95·3; either test positive). In 7273 individuals with anosmia or at least three symptoms, seroprevalence ranged from 15·3% (13·8–16·8) to 19·3% (17·7–21·0). Around a third of seropositive participants were asymptomatic, ranging from 21·9% (19·1–24·9) to 35·8% (33·1–38·5). Only 19·5% (16·3–23·2) of symptomatic participants who were seropositive by both the point-of-care test and immunoassay reported a previous PCR test. Interpretation The majority of the Spanish population is seronegative to SARS-CoV-2 infection, even in hotspot areas. Most PCR-confirmed cases have detectable antibodies, but a substantial proportion of people with symptoms compatible with COVID-19 did not have a PCR test and at least a third of infections determined by serology were asymptomatic. These results emphasise the need for maintaining public health measures to avoid a new epidemic wave. Funding Spanish Ministry of Health, Institute of Health Carlos III, and Spanish National Health System.
Article
Background As of June 8, 2020, the global reported number of COVID-19 cases had reached more than 7 million with over 400 000 deaths. The household transmissibility of the causative pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains unclear. We aimed to estimate the secondary attack rate of SARS-CoV-2 among household and non-household close contacts in Guangzhou, China, using a statistical transmission model. Methods In this retrospective cohort study, we used a comprehensive contact tracing dataset from the Guangzhou Center for Disease Control and Prevention to estimate the secondary attack rate of COVID-19 (defined as the probability that an infected individual will transmit the disease to a susceptible individual) among household and non-household contacts, using a statistical transmission model. We considered two alternative definitions of household contacts in the analysis: individuals who were either family members or close relatives, such as parents and parents-in-law, regardless of residential address, and individuals living at the same address regardless of relationship. We assessed the demographic determinants of transmissibility and the infectivity of COVID-19 cases during their incubation period. Findings Between Jan 7, 2020, and Feb 18, 2020, we traced 195 unrelated close contact groups (215 primary cases, 134 secondary or tertiary cases, and 1964 uninfected close contacts). By identifying households from these groups, assuming a mean incubation period of 5 days, a maximum infectious period of 13 days, and no case isolation, the estimated secondary attack rate among household contacts was 12·4% (95% CI 9·8–15·4) when household contacts were defined on the basis of close relatives and 17·1% (13·3–21·8) when household contacts were defined on the basis of residential address. Compared with the oldest age group (≥60 years), the risk of household infection was lower in the youngest age group (<20 years; odds ratio [OR] 0·23 [95% CI 0·11–0·46]) and among adults aged 20–59 years (OR 0·64 [95% CI 0·43–0·97]). Our results suggest greater infectivity during the incubation period than during the symptomatic period, although differences were not statistically significant (OR 0·61 [95% CI 0·27–1·38]). The estimated local reproductive number (R) based on observed contact frequencies of primary cases was 0·5 (95% CI 0·41–0·62) in Guangzhou. The projected local R, had there been no isolation of cases or quarantine of their contacts, was 0·6 (95% CI 0·49–0·74) when household was defined on the basis of close relatives. Interpretation SARS-CoV-2 is more transmissible in households than SARS-CoV and Middle East respiratory syndrome coronavirus. Older individuals (aged ≥60 years) are the most susceptible to household transmission of SARS-CoV-2. In addition to case finding and isolation, timely tracing and quarantine of close contacts should be implemented to prevent onward transmission during the viral incubation period. Funding US National Institutes of Health, Science and Technology Plan Project of Guangzhou, Project for Key Medicine Discipline Construction of Guangzhou Municipality, Key Research and Development Program of China.
Article
Background Assessing the burden of COVID-19 on the basis of medically attended case numbers is suboptimal given its reliance on testing strategy, changing case definitions, and disease presentation. Population-based serosurveys measuring anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) antibodies provide one method for estimating infection rates and monitoring the progression of the epidemic. Here, we estimate weekly seroprevalence of anti-SARS-CoV-2 antibodies in the population of Geneva, Switzerland, during the epidemic. Methods The SEROCoV-POP study is a population-based study of former participants of the Bus Santé study and their household members. We planned a series of 12 consecutive weekly serosurveys among randomly selected participants from a previous population-representative survey, and their household members aged 5 years and older. We tested each participant for anti-SARS-CoV-2-IgG antibodies using a commercially available ELISA. We estimated seroprevalence using a Bayesian logistic regression model taking into account test performance and adjusting for the age and sex of Geneva's population. Here we present results from the first 5 weeks of the study. Findings Between April 6 and May 9, 2020, we enrolled 2766 participants from 1339 households, with a demographic distribution similar to that of the canton of Geneva. In the first week, we estimated a seroprevalence of 4·8% (95% CI 2·4–8·0, n=341). The estimate increased to 8·5% (5·9–11·4, n=469) in the second week, to 10·9% (7·9–14·4, n=577) in the third week, 6·6% (4·3–9·4, n=604) in the fourth week, and 10·8% (8·2–13·9, n=775) in the fifth week. Individuals aged 5–9 years (relative risk [RR] 0·32 [95% CI 0·11–0·63]) and those older than 65 years (RR 0·50 [0·28–0·78]) had a significantly lower risk of being seropositive than those aged 20–49 years. After accounting for the time to seroconversion, we estimated that for every reported confirmed case, there were 11·6 infections in the community. Interpretation These results suggest that most of the population of Geneva remained uninfected during this wave of the pandemic, despite the high prevalence of COVID-19 in the region (5000 reported clinical cases over <2·5 months in the population of half a million people). Assuming that the presence of IgG antibodies is associated with immunity, these results highlight that the epidemic is far from coming to an end by means of fewer susceptible people in the population. Further, a significantly lower seroprevalence was observed for children aged 5–9 years and adults older than 65 years, compared with those aged 10–64 years. These results will inform countries considering the easing of restrictions aimed at curbing transmission. Funding Swiss Federal Office of Public Health, Swiss School of Public Health (Corona Immunitas research program), Fondation de Bienfaisance du Groupe Pictet, Fondation Ancrage, Fondation Privée des Hôpitaux Universitaires de Genève, and Center for Emerging Viral Diseases.