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Investigating the dark figure of COVID-19 cases in Austria: Borrowing from the deCODE Genetics study in Iceland

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The number of undetected cases of SARS-CoV-2 infections is expected to be a multiple of the reported figures mainly due to the assumed high proportion of asymptomatic infections and to limited availability of trustworthy testing resources. Relying on the deCODE genetics study in Iceland, which offers large scale testing among the general population, we investigate the magnitude and uncertainty of the number of undetected cases COVID-19 cases in Austria. We formulate several scenarios relying on data on the number of COVID-19 cases which have been hospitalized, in intensive care, as well as on the number of deaths and positive tests in Iceland and Austria. We employ frequentist and Bayesian methods for estimating the dark figure in Austria based on the hypothesized scenarios and for accounting for the uncertainty surrounding this figure. Using data available on April 01, 2020, our study contains two main findings: First, we find the estimated number of infections to be on average around 8.35 times higher than the recorded number of infections. Second, the width of the uncertainty bounds associated with this figure depends highly on the statistical method employed. At a 95% level, lower bounds range from 3.96 to 6.83 and upper bounds range from 9.82 to 12.61. Overall, our findings confirm the need for systematic tests in the general population of Austria.
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AJS
Austrian Journal of Statistics
April 2020, Volume 49, 1–17.
http://www.ajs.or.at/
doi:10.17713/ajs.v49i4.1142
Investigating the Dark Figure of COVID-19 Cases
in Austria: Borrowing From the deCODE Genetics
Study in Iceland
Rainer Hirk
WU Vienna University of
Economics and Business
Gregor Kastner
WU Vienna University of
Economics and Business
Laura Vana
WU Vienna University of
Economics and Business
Abstract
The number of undetected cases of SARS-CoV-2 infections is expected to be a multi-
ple of the reported figures mainly due to the assumed high proportion of asymptomatic
infections and to limited availability of trustworthy testing resources. Relying on the
deCODE genetics study in Iceland, which offers large scale testing among the general
population, we investigate the magnitude and uncertainty of the number of undetected
cases COVID-19 cases in Austria.
We formulate several scenarios relying on data on the number of COVID-19 cases which
have been hospitalized, in intensive care, as well as on the number of deaths and positive
tests in Iceland and Austria. We employ frequentist and Bayesian methods for estimating
the dark figure in Austria based on the hypothesized scenarios and for accounting for the
uncertainty surrounding this figure.
Using data available on April 01, 2020, our study contains two main findings: First,
we find the estimated number of infections to be on average around 8.35 times higher
than the recorded number of infections. Second, the width of the uncertainty bounds
associated with this figure depends highly on the statistical method employed. At a 95%
level, lower bounds range from 3.96 to 6.83 and upper bounds range from 9.82 to 12.61.
Overall, our findings confirm the need for systematic tests in the general population of
Austria.
Keywords: Comparative Study, SARS-CoV-2, Uncertainty Quantification, Unreported Infec-
tions.
1. Introduction
The number of confirmed infections with the SARS-CoV-2 is a central figure which many
studies and models rely on for further analysis and also for evaluating whether the social
distancing measures have proven effective. The same figure is being reported by media outlets
and is the main information directed to the general public.
However, reports and scientific studies from different countries estimate the true number of
infections to be a multiple of the reported figures (Li, Pei, Chen, Song, Zhang, Yang, and
Shaman 2020;Maugeri, Barchitta, Battiato, and Agodi 2020;Zhao, Musa, Lin, Ran, Yang,
2Dark Figure of COVID-19 Cases in Austria
Wang, Lou, Yang, Gao, He et al. 2020). The number of undetected cases, the so-called
dark figure, is expected to be large mainly due to two reasons: i) the characteristics of the
disease and ii) the testing strategy (Czypionka and Reiss March 19, 2020). A dangerous
characteristic of the COVID-19 disease is that the population is highly susceptible due to
lack of antibodies and that transmission is likely to be carried out by infected individuals
who exhibit light to no symptoms. Several studies report that roughly 50% of the infections
are asymptomatic, which makes the early detection and isolation of the infected problematic
(see, e.g., Nishiura, Kobayashi, Miyama, Suzuki, Jung, Hayashi, Kinoshita, Yang, Yuan,
Akhmetzhanov et al. 2020;Day 2020;Shahan March 21, 2020;Castelfranco March 16, 2020).
This means that even if all the symptomatic infections were recorded, the dark figure would
be at least as high as the number of confirmed infections. Moreover, the infected individuals
with mild symptoms are likely not going to get in contact with the health care system and
will also not be recorded in official statistics. Therefore, extensive testing can be a viable
strategy for accurately estimating for the prevalence of the disease in the general population.
Nevertheless, countries around the world are struggling to set up such a strategy due to e.g.,
costs, testing capacity and availability of testing kits. Moreover, country-wide screenings with
PCR (polymerase chain reaction) tests only provide a current snapshot of the active infections
on a target date. As people who were infected but do not excrete the virus at this specific
date are tested negative, these screenings do not offer information on the total number of
individuals who have been infected with SARS-CoV-2.
Reports estimated the number of infected in Italy to be around 3.5 times higher than reported
as of February 29, 2020 (Tuite, Ng, Rees, and Fisman 2020). Slightly lower estimates have
been given for Germany (Kekul´e March 14, 2020). In Austria, the limited test capacity has
been an important set-back in the government’s testing strategy. The capacity of PCR testing
kits has been relatively low with the test capacity increasing slowly relatively to the number
of infections (Czepel March 26, 2020). Secondly, until around March 22, 2020, only people
with contact to confirmed infected people or coming from high risk areas such as Italy or
China were tested. These issues point to a substantial number of unrecorded infections in
Austria. Previous estimations place the number of infected in Austria between 16 000 to
55 000 as of March 18, 2020 (Czypionka and Reiss March 19, 2020;Sturn March 27, 2020;
ORF March 27, 2020). In the four day period between March 31, 2020 and April 03, 2020
a field study commissioned by the Austrian government will be conducted by the research
institute SORA, where 2000 individuals will be tested for COVID-19 in Austria. The results
of this study will shed light on the number of active infections in Austria by ensuring that
the sample of tested individuals is representative for the general population, but it does not
provide information on the accumulated number of SARS-CoV-2 infections in Austria (ORF
March 30, 2020). For this purpose reliable and accurate i.e., sensitive and specific antibody
tests would be necessary.
In the absence of an extensive field study, the attempts to quantify the dark figure of infections
can only produce rough estimates as they rely on several assumptions and/or on results from
different countries. The difficulty in estimating undetected infections arises mainly due to data
quality issues. The available data is highly dependent on the testing and reporting strategies
of the different countries. Comparisons are not straightforward as most of the countries apply
different testing approaches. Italy, for example, mostly focuses on testing in hospitals with
symptoms (Onder, Rezza, and Brusaferro 2020). This results in a high proportion of positive
tests and also the mortality rates are apparently higher in the tested sample due to more
severe cases being tested. The roughly 50% asymptomatic are not covered by this approach
so one can assume that the true mortality rate is lower than reported. Other countries have
delayed or performed very limited testing until very recently. This was the case in the US,
where less than 1000 cases were reported until March 10, 2020. Since then the number has
increased by a factor of 196 times as of March 31, 2020 (Dong, Du, and Gardner 2020).
While the data quality issue is one most countries are battling with, one can make use of
studies in other countries which have a better testing strategy. Iceland can be seen as a pioneer
Austrian Journal of Statistics 3
in this respect as they launched a large scale testing of general population (see Government
of Iceland March 21, 2020). The bio-pharmaceutical company deCODE genetics launched
a testing program, where they test people on a voluntary basis in order to get a better
understanding about the spread of the coronavirus across the country. As of April 01, 2020,
Iceland has tested a higher proportion of its citizens than almost any other country in the
world (except the Faroe Islands) and through the deCODE genetics study is in a position to
obtain a rough approximation of the unrecorded infections in Iceland (Nardelli and Ashton
March 21, 2020).
The goal of this article is to obtain rough estimates of the dark figure of infections in Austria by
borrowing information from the screening of the general population in Iceland. The approach
we propose in this article consists of defining several scenarios where we hypothesize on the
relationship between the observed figures in Iceland versus the observed figures in Austria.
While this study follows a rather naive approach and disregards most of the complexity of
the problem by making simplifying assumptions, it is also, to the best of our knowledge,
the only one making use of the Icelandic case study in other to extrapolate results to other
countries and should provide rough approximations of the unrecorded infections in Austria
while serving as a starting point for further research.
This remainder of the article is organized as follows: Section 2introduces the deCODE
genetics study in Iceland. Section 3provides a comparison of Austria and Iceland in terms of
COVID-19 figures. The different scenarios and our approach for estimating the dark figure
in Austria are presented in Section 4. Section 5discusses the findings and Section 6provides
concluding remarks.
2. Study performed by deCODE genetics
From March 15, 2020 until March 31, 2020, 10401 individuals were tested by the medical
research company deCODE genetics, who joined forces with the Icelandic authorities in per-
forming a large scale SARS-CoV-2 testing in Iceland. The company offers screening on a
voluntary basis among the general population, by offering a free coronavirus test to anyone
who fills in an online form. This approach to testing leads to a higher representation in the
sample of non-symptomatic non-quarantined individuals and the sample of tested people can
be considered to be more representative of the whole Icelandic population than the sample
of individuals tested by the authorities, who are mainly symptomatic or considered high risk.
Therefore, assuming that the deCODE genetics study covers a representative sample would
allow conclusions to be drawn about the prevalence of the disease in the general population.
The tests performed by deCODE genetics, as well as the ones performed by the Department
of Microbiology of the National University Hospital of Iceland (NUHI), use the technology
of polymerase chain reaction (PCR), which is considered to date to be the most accurate for
COVID-19 diagnosis.
The number of infections discovered by the deCODE genetics study in the above mentioned
time frame lies at 84. This constitutes a prevalence rate in the tested sample of 0.81%. Assum-
ing that the sample of deCODE genetics is representative for the whole Icelandic population,
0.81% can be used as an estimate for the prevalence of the COVID-19 disease in the gen-
eral population with a 95% frequentist bootstrap confidence interval (CI) of [0.64%,0.98%].
This would correspond to a number of 2941 infected people in Iceland (with a 95% CI of
[2314,3567]). When extrapolating from the deCODE genetics sample to the whole population,
these numbers would imply that, as of March 31, 2020, the official statistic of 1136 infections
reported by the NUHI is underestimating the number of cases by 1805 (95% CI: [1178,2431]),
with only 38.63% of the cases being recorded officially (95% CI: [31.85%,49.09%]). This in-
dicates that the number of infections is 2.59 times larger than officially reported (95% CI:
[2.04,3.14]).
4Dark Figure of COVID-19 Cases in Austria
Table 1: Statistics related to COVID-19 disease available as of April 01, 2020. Source:
Austrian Social Ministry and Government of Iceland.
Austria Iceland Austria (PMP) Iceland (PMP)
Deaths 146 4 16.40 10.98
Hospitalized 1071 35 120.30 96.12
Intensive care 215 11 24.15 30.21
Confirmed infections 10482 1136 1177.41 3119.73
Tests 55863 9115 6274.91 25031.99
Percentage confirmed 18.76% 12.46%
3. Comparison between Austria and Iceland
Iceland has a population of 364134 (reported by https://statice.is/ on January 1, 2020),
while Austria’s population of 8902600 (according to Statistik Austria) is 24.45 times larger.
In terms of health care systems, the two countries are in the top 15 health care systems in
the world based on a standardized set of metrics on health system performance, with Austria
being few places ahead of Iceland (around place 9).1
Table 1shows the number of deaths, number of hospitalized patients, among the hospitalized
the number of patients in intensive care, the number of confirmed infections and the number
of tests performed in each country both in absolute value and per million of population
(PMP). Note that the reported statistics for Iceland include only the results of the NUHI
testing for comparison purposes, as both the Austrian and the NUHI testing focus mostly on
symptomatic cases.
While in Iceland – as of April 01, 2020 – 4 people diagnosed with COVID-19 have died, at
the same time in Austria 146 fatalities have been recorded. Among the active cases, 35 are
currently hospitalized in Iceland and 1071 in Austria and 11 are reported to need intensive
care in Iceland, compared to 215 in Austria. The per million of population figures can be
compared among the two countries, with Iceland having a relatively higher number of intensive
care patients, higher confirmed infections and roughly 4 times more tests compared to Austria.
In Austria 18.76% of the tests are positive, in Iceland we observe a percentage of positive tests
of 12.46%. This might be due to the relatively higher number of conducted tests in Iceland.
Time series of the tests and the confirmed infections for the two countries are shown in the
Appendix, Figure 7and Figure 8.
Figure 1shows the age distribution of the population in the two countries. While Iceland
has relatively more people in the younger age groups, the percentage of people above 45 is
relatively higher in Austria with the largest different being in the older adult population above
65.
When comparing the age distribution of the infected people presented in Figure 2, we find
similar prevalence rates among most age groups. Only for the age groups from 35–44 and
65+ clear differences are observed. Within the 35–44 interval the proportion of infected
people is higher in Iceland, while in Austria relatively more people aged 65 and above are
infected among the tested individuals. Partly, this can be explained by Austria having a larger
proportion of older adult population but could also highlight the different testing strategies
of the two countries, where in Austria detection in non-risk groups might be less likely. Note
that the number of infections in Iceland is given by both the deCODE genetics and the NUHI
samples.
All data sources were updated on April 01, 2020 at 3:00 pm UTC+2.
1Source: Tandon, Murray, Lauer, and Evans (2000) and more recently https://worldpopulationreview.
com/countries/best-healthcare-in- the-world/
Austrian Journal of Statistics 5
0−4 5−14 15−24 25−34 35−44 45−54 55−64 65+
Austria
Iceland
Age groups
Percentage of population
0 5 10 15
Figure 1: Age distribution in the population.
4. Estimation of the dark figure in Austria
When trying to estimate the dark figure of SARS-CoV-2 infections in Austria, we consider five
different scenarios based on available figures from the ministries of health of the two countries.
In each of the scenarios we estimate the prevalence of COVID-19 in Austria by multiplying
the estimated prevalence of the deCODE genetics study by a procedure-specific multiplier k.
The scenarios focus on the number of hospitalized cases, the number cases in intensive care,
the observed deaths due to COVID-19 and the ratio of positive SARS-CoV-2 tests.
4.1. Defining the scenarios
1. Scenario I: Ratio of hospitalized per million inhabitants
In the first scenario the multiplier is calculated by the number of hospitalized people in
Austria divided by number of hospitalized people in Iceland (per million of population).
As of April 01, 2020, 1071 (120.3 per million) COVID-19 cases are hospitalized in
Austria, while 35 (96.12 per million) are hospitalized in Iceland. This gives a multiplier
of 1.25 for this scenario.
In this scenario, we estimate a number of 89988 infected people as of April 01, 2020. This
would mean that 79506 people were infected but not recorded and only 11.65% of the
estimated infections were recorded. This would indicate that the number of infections
is 8.59 times larger than officially reported.
2. Scenario II: Ratio of intensive care patients per million inhabitants
In the second scenario we calculate the multiplier by the number of COVID-19 cases
in intensive care in Austria divided by number of COVID-19 cases in intensive care in
Iceland. As of April 01, 2020, 11 (30.21 per million) persons are in intensive care in
Iceland, while 215 (24.15 per million) are in intensive care in Austria. This gives a
multiplier of 0.8 for this scenario. We do not account here for the difference in intensive
care units (ICUs) among the two countries, as this stage in the pandemic the ICUs are
not operating at full capacity.
This gives us an estimated number of infections of 57479. This would mean that 46997
people were infected but not recorded and only 18.24% of the estimated infections were
6Dark Figure of COVID-19 Cases in Austria
0−4 5−14 15−24 25−34 35−44 45−54 55−64 65+
Austria
Iceland
Age groups
Percentage of infected cases
0 5 10 15 20
Figure 2: Distribution of infections among the age groups. For Iceland infections recorded by
both deCODE genetics and NUHI are considered.
recorded as of April 01, 2020. This would indicate that the number of infections is 5.48
times larger than officially reported.
3. Scenario III: Ratio of deaths per million inhabitants
In this scenario the multiplier is given by the ratio of deaths in Austria and deaths in
Iceland. In both countries a deceased person who has previously tested positive for the
COVID-19 disease is counted as a death in the official statistics. 2As of April 01, 2020,
4 (10.98 per million) persons have died in Iceland, while 146 (16.4 per million) deaths
have been observed in Austria. This gives a multiplier of 1.49 for this scenario.
The estimated number of infections in Austria is 107339 in this scenario. Thus, as of
April 01, 2020, 96857 people were infected but not recorded in the official statistics and
only 18.24% of the estimated infections were recorded. This would indicate that the
number of infections is 10.24 times larger than officially reported.
4. Scenario IV: Similar prevalence in the population as in Iceland
We assume the percentage of infected population in Austria is equal to the one in
Iceland and use the prevalence of 0.81% estimated from deCODE genetics data to draw
conclusions about the dark figure in Austria. This assumption implies that the disease
had a similar development in the two countries.
When assuming a similar prevalence rate as in Iceland, we estimate a number of 71899
infected people as of April 01, 2020. This means that 61417 people were infected but
not recorded and only 14.58% of the estimated infections were recorded. This indicates
that the number of infections is 6.86 times larger than officially reported.
5. Scenario V: Ratio of the percentage positive SARS-CoV-2 tests
Throughout this scenario, we analyse and compare the testing behavior of both coun-
tries. A closer look at the testing results shows that Iceland has conducted roughly
3.99 times more tests per million people compared to Austria, but confirmed only 2.65
times as many infections per million people compared to Austria. Under the (strong)
assumption that the probability of obtaining a positive test result does not decrease with
2Sources: https://www.sozialministerium.at/Informationen-zum-Coronavirus/Coronavirus-- -Hae
ufig-gestellte-Fragen.html and https://www.covid.is/data
Austrian Journal of Statistics 7
Table 2: Estimated number of infections, the dark multiplier of confirmed infections (DM –
the ratio of the estimated infections and the confirmed infections), 95% frequentist bootstrap
confidence intervals for the number of infections and DM, and the multiplier kfor each of the
scenarios in Austria.
Scenario Est. Infections 95% CI DM 95% CI k
Sc.I: hospitalized 89988 [70822, 109155] 8.59 [6.76, 10.41] 1.25
Sc.II: intensive care 57479 [45237, 69722] 5.48 [4.32, 6.65] 0.80
Sc.III: deaths 107339 [84477, 130201] 10.24 [8.06, 12.42] 1.49
Sc.IV: similar prevalence 71899 [56585, 87212] 6.86 [5.40, 8.32] 1.00
Sc.V: pos. test ratio 109926 [86513, 133339] 10.49 [8.25, 12.72] 1.53
increasing testing size, we take the ratio of the percentage of positive tests in Austria
and the percentage of positive tests in Iceland of 1.53 as a multiplier of the prevalence
rate.
As of April 01, 2020, we estimate a number of 109926 infected people for this scenario.
This means that 99444 people were infected but not recorded and only 9.54% of the
estimated infections were recorded. This would indicate that the number of infections
10.49 times larger than officially reported.
4.2. Assessing the uncertainty in the estimation of the dark figure in a
simulation study
There are numerous sources of uncertainty in estimating the dark figure, some of which can be
appropriately accounted for in a statistical modeling framework: i) the plausibility (likelihood)
of the different scenarios, ii) the uncertainty related to the true value of the multiplier k, iii)
uncertainty in the prevalence of the disease in Iceland.
In this section we address these points in the following way. Regarding i), the assignment
of probabilities to each of the scenarios is a challenging task due to disagreement of experts
and the lack of data availability. Therefore we resort to simulation and in a first experiment,
simulate weights for the different scenarios from a prior distribution and assume the prevalence
of the disease in Iceland to be fixed. This allows us to obtain a distribution of the expected
value for the number of infections (and DM) over all scenarios. In a second experiment, we
propose a Bayesian statistical model for the prevalence rate in Iceland. Moreover, we shift
focus from the different scenarios to the multiplier kand propose a prior distribution for this
quantity.
Due to the limited testing availability in Austria, the number of recorded infections is an
unrealistic indicator when comparing the prevalence rate of Iceland and Austria. Testing data
in Austria cannot be assumed to represent a picture of the whole population and but rather
it displays the infections in a subgroup of the population. Currently, the most comparable,
trustworthy and informative indicators are observed figures in the hospitals like the number
of hospitalized people, the number of people in intensive care and the observed number of
deaths. Other meaningful scenarios are: assuming a similar prevalence rate like in Iceland
as the first recorded infections took place in both countries at a similar point in time; using
the ratio of the percentage of positive tests as a reasonable estimation/guess of a multiplier
of the prevalence rate. Therefore, we focus on these five scenarios for our statistical modeling
purposes.
Simulating the probability of the five scenarios
We choose a Dirichlet prior on the probability of the five scenarios:
p1, p2, p3, p4, p5Dir(α1, α2, α3, α4, α5),
8Dark Figure of COVID-19 Cases in Austria
0.0 0.2 0.4 0.6 0.8 1.0
01234
α = 1
0.0 0.2 0.4 0.6 0.8 1.0
0 10 20 30 40
α = 0.1
Figure 3: Marginal prior densities pi∼ B(αi,Pj6=iαj), i= 1,...,5, for α= 1 (left) and
α= 0.1 (right).
α = 1
DM
Frequency
6 7 8 9 10
0 500 1000 1500 2000
α = 0.1
DM
Frequency
5 6 7 8 9 10
0 500 1000 1500 2000
Figure 4: Histograms of the estimated DM for 10000 simulated scenario probabilities from
the Dirichlet distribution with hyper-parameters α= 1 and α= 0.1.
where α1=α2=. . . =α5αis a hyper-parameter to be chosen. Setting all the parameters
equal implies that in the long run we believe the scenarios to be equally likely. In the simu-
lation we quantify the variability of the DM with respect to the variability in the probability
of the scenarios. In this subsection we consider the prevalence in Iceland to be fixed to 0.81%
in this experiment.
We choose two values of hyper-parameters of the Dirichlet distribution: α= 1 which cor-
responds to assigning evenly distributed probabilities to the scenarios, and α= 0.1 which
corresponds to assigning most mass to one of the scenarios. For a visualization of the implied
marginal prior distributions of pi, i = 1,...,5, see Figure 3.
In our simulation, we draw 10000 samples from the Dirichlet distribution and weigh the
estimated number of infections in each scenario by the sampled weights in order to obtain
the expected value of the DM. For both values of the hyper-parameters we obtain the same
mean of 8.34 for the DM. Figure 4shows the distribution of the DM under variability of the
scenarios. More mass is assigned to the tails of the distribution under the case α= 0.1. This
is due the fact that for this set of hyper-parameters the more extreme scenarios get more
weight because the probability of one of the scenarios is close to one and the others close to
zero. The modes correspond to the estimated multipliers calculated in the scenarios.
Austrian Journal of Statistics 9
Statistical modeling of the prevalence rate
The prevalence of the disease in Iceland’s general population has been considered fixed in the
previous experiment. In order to account for the uncertainty surrounding this quantity, we
make use of the beta-binomial model. In this Bayesian model, a Beta prior is assumed on the
prevalence rate in Iceland pISL:
pISL ∼ B(a, b),
where aand bare hyper-parameters to be chosen. The prior distribution on the prevalence
of the disease should assign high mass on small values of pISL. Suitable hyper-parameters
could be e.g., a= 1 and b= 50 which implies a prior mean and a prior standard deviation of
roughly 0.02.
The number infected people in the deCODE genetics sample NISL
inf is then modeled as a
binomial distribution with size equal to the number of tested individuals by deCODE genetics
NISL
test and probability pISL:
NISL
inf |pISL Bin(NISL
test , pISL).
We use the deCODE genetics results in order to estimate the true number of infected in
Austria. We assume that NAUT
inf , the true number of infected individuals in Austria is a
binomial random variable with size equal to the number of inhabitants of Austria NAUT
pop and
probability which is a multiple of the probability in Iceland k·pISL:
NAUT
inf |pISL Bin(NAUT
pop , k ·pISL),(1)
where kis a multiplier. For kfixed, this would represent the posterior beta-binomial predictive
distribution, where the posterior probability of pISL would be given by:
pISL|NISL
test , N ISL
inf ∼ B(a+NISL
inf , b +NISL
test NISL
inf ).(2)
However, there is also uncertainty coming from the value of k. We consider here two prior
probability distributions for the multiplier k.
Prior 1: Discrete probability We consider each of the five values of kpresented in Table 2
equally likely.
P(k= 1.25) = P(k= 0.8) = P(k= 1.49) = P(k= 1) = P(k= 1.53) = 1/5.
Prior 2: Mixture of gamma distributions It is unrealistic to assume that konly has
5 possible values. A more realistic assumption is that kis a continuous variable coming
from a mixture of five distributions which have their mean at the values computed in the
different scenarios. Moreover, the variance of the different components should differ among
the scenarios, due to the few observations observed in some of the scenarios. For example, the
value of kfor the third scenario is computed by using data on only 4 deaths in Iceland, so this
component can be expected to have higher variance as one additional death can change the
multiplier significantly. Given that the multipliers are positive values, we assume a gamma
distribution for the components:
p(k) =
S
X
s=1
φsG(αs, βs),
where Sdenotes the number of scenarios, φsdenote the probability of scenario s,αs>0 is
the shape and βs>0 is the rate parameter of the gamma distribution for scenario s. Table 3
presents the chosen hyper-parameters each of the gamma distributions corresponding to the
scenarios. We choose the hyper-parameters αsand βssuch that the mean of the distribution
equal the value of the multiplier estimated in each of the scenarios and the variance equals
predetermined values.
The mixture of gamma prior for the multiplier kis displayed in Figure 5.
10 Dark Figure of COVID-19 Cases in Austria
Table 3: Parameter choices for the gamma distributions in the mixture model.
Scenario φsαsβsµs=αssσs=αss
Sc.I: hospitalized 0.20 156.65 125.16 1.25 0.10
Sc.II: intensive care 0.20 31.96 39.97 0.80 0.14
Sc.III: deaths 0.20 44.58 29.86 1.49 0.22
Sc.IV: similar prevalence 0.20 100.00 100.00 1.00 0.10
Sc.V: pos. test ratio 0.20 233.76 152.89 1.53 0.10
Multiplier k
Density
0.5 1.0 1.5 2.0
0.0 0.2 0.4 0.6 0.8 1.0
Figure 5: Mixture of gamma prior for the multiplier k.
Sampling After simulating 10000 values for kfrom the priors introduced above, we sample
pISL from the distribution in Equation 2. Finally, NAUT
inf is simulated from the binomial
distribution given in Equation 1where we replace kand pISL with the sampled values. Figure 6
provides the predictive distributions for the DM with the two prior specification. The average
DM lies at 8.39 for prior 1 and at 8.38 for prior 2.
4.3. Results
Table 4displays a summary of the ratio of estimated infections and confirmed infections with
corresponding intervals for quantifying the uncertainty for all five modeling approaches. We
find that the mean estimates of the DM are between 8.3 and 8.4 for all five approaches. In the
frequentist approach, where we average over the equally weighted scenarios, we find a mean
estimate of 8.33 with a 95% frequentist bootstrap confidence interval of [6.56, 10.11]. When
simulating the probabilities of the five scenarios with a Dirichlet distribution with α= 1,
we find similar results like in the frequentist approach, while a hyper-parameter of α= 0.1
increases the uncertainty bounds as more mass is assigned to one of the scenarios in each of
the 10000 simulations. When accounting for both the uncertainty in the prevalence in Iceland
and the value of the multiplier in a beta-binomial model, the mean estimates are 8.39 for
prior 1 and 8.38 for the mixture of gamma prior on the multiplier k. As expected, the range
of the credible intervals increases as more uncertainty is being acounted for. Especially for
the mixture of gamma prior, where a plausible continuous range for the multiplier kis being
accounted for, the uncertainty bounds are the widest. This final approach also incorporates
uncertainty regarding the calculation of the multipliers in the scenarios.
5. Discussion
The figures used in computing the multipliers between Austria and Iceland in the different
Austrian Journal of Statistics 11
Discrete prior
DM
Frequency
4 6 8 10 12 14 16
0 500 1000 1500
Mixture of gamma prior
DM
Frequency
5 10 15
0 500 1000 1500
Figure 6: Histograms of the estimated DM for 10000 simulated scenarios with discrete prob-
abilities and the mixture of gamma prior for k.
Table 4: DM with uncertainty bounds (95% bootstrap confidence intervals for the frequentist
approach and 95% highest density intervals for the Bayesian approaches).
Approach DM 95% Intervals
avg. frequentist approach 8.33 [6.56, 10.11]
Dirichlet α= 1 8.34 [6.83, 9.82]
Dirichlet α= 0.1 8.34 [5.57, 10.49]
Beta-Binomial – discrete probability 8.39 [4.71, 12.01]
Beta-Binomial – mixture of gamma 8.38 [3.96, 12.61]
scenarios are compared on the same date, April 01, 2020, which assumes the same temporal
development of the disease in the two countries. Following the suggestion of the reviewers,
we investigated the robustness of the results when comparing the figures at different time
points, rather then contemporaneously. For this purpose, we assumed a three day lag of the
Icelandic figures, as the first infection in Iceland was recorded on February 28, 2020 while the
first confirmed infection in Austria happened on February 25, 2020. This is also confirmed by
the modes of active infections in the two countries. We computed the multipliers by taking
the ratios of the Austrian figures on March 29, 2020 and the Icelandic figures on April 01,
2020 and obtain an average multiplier of 8.75. For Austrian figures on April 01, 2020 and the
Icelandic figures on April 04, 2020 the average multiplier lies at 8.17.
At the time of the revision of the paper, the aforementioned field study conducted by the
research institute SORA has been completed on 1544 Austrian individuals (this corresponds
to 173 tests per million inhabitants). Among these, 0.33% active infections were confirmed
(Ogris and Hofinger April 10, 2020). This figure provides a snapshot for active SARS-CoV-2
infections for the period April 01, 2020 to April 06, 2020. Note, however, that the SORA
study is not directly comparable with the deCODE genetics study for several reasons. First,
the sample size is much larger in the deCODE genetics study, with 16886 tests (46373 tests per
million inhabitants) performed by April 06, 2020. Second, the sample selection is voluntary in
the deCODE study but more complex in the SORA study (Ogris and Hofinger April 10, 2020).
Third, the studies are conducted over different time-periods, with the deCODE genetics study
being still ongoing since March 15, 2020. This is important because infected who recovered
before the beginning of each testing period are not accounted for. With the population being
widely compliant with the social-distancing measures imposed by the Austrian government on
March 13, it is plausible that the number of newly infected started to decrease immediately.
12 Dark Figure of COVID-19 Cases in Austria
Hence, we suspect a higher prevalence rate in Austria one or two weeks before April 06.3
6. Conclusion
Our findings suggest that the number of undetected infections of SARS-CoV-2 in Austria, both
active and recovered, is a multiple of the reported figures, with an average ratio of estimated
infections and confirmed infections of around 8.36. However, the uncertainty surrounding this
estimate is significant. We employ several frequentist and Bayesian methods to appropriately
account for the uncertainty and find that plausible estimates may well lie in the interval [3.96,
12.61]. The analysis relies on the deCODE genetics study in Iceland, which offers large scale
testing among the general population. We investigate the magnitude and uncertainty of the
dark figure of the SARS-CoV-2 infections in Austria by formulating several scenarios relying
on data on the number of COVID-19 cases which have been hospitalized, in intensive care, as
well as on the number of deaths and positive tests in Iceland and Austria.
We provide simulation experiments in a statistical framework for accounting for different
sources of uncertainty such as the likelihood of the different scenarios, the prevalence of
COVID-19 in Iceland and the uncertainty regarding the multiplier between the prevalence in
Austria and the prevalence in Iceland.
One of the primary limitations of the proposed framework is the assumption that the deCODE
genetics study in Iceland consists of a representative sample of the entire population. It is to
be noted that the application for testing occurs on a voluntary basis through an online form,
which can be a source of self-selection bias leading to the underrepresentation of some age
groups or to an over-proportional percentage of infections in the sample. A second limitation
is the reliance of the study on key figures reported by the two governments where a lag
in reporting is likely. Moreover, while the data is mostly comparable, there might still be
differences in the definitions of the reported figures. Third, we assume a static setting for
modeling the prevalence rate in both countries, which does not control for time span over
which the deCODE genetics study has recorded the observations. Last, other factors such as
the handling of the COVID-19 disease might to some extent vary among the countries. While
some of the performed experiments are able to account at least partially for these issues, we
stress that our results remain dependent on the assumptions made throughout the analysis.
Acknowledgments
We thank Matthias Templ (editor) as well as Georg Heinze (reviewer) and Peter Filzmoser
(reviewer) for their insightful and timely comments and suggestions which helped to improve
the paper and in particular sharpen the discussion about its limitations.
The authors acknowledge funding from the Austrian Science Fund (FWF) for the project
“High-dimensional statistical learning: New methods to advance economic and sustainability
policies” (ZK 35), jointly carried out by WU Vienna University of Economics and Business,
Paris Lodron University Salzburg, TU Wien, and the Austrian Institute of Economic Research
(WIFO).
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Austrian Journal of Statistics 15
A. Figures
A.1. Number of tests per million inhabitants
2020−02−25
2020−02−26
2020−02−27
2020−02−28
2020−02−29
2020−03−01
2020−03−02
2020−03−03
2020−03−04
2020−03−05
2020−03−06
2020−03−07
2020−03−08
2020−03−09
2020−03−10
2020−03−11
2020−03−12
2020−03−13
2020−03−14
2020−03−15
2020−03−16
2020−03−17
2020−03−18
2020−03−19
2020−03−20
2020−03−21
2020−03−22
2020−03−23
2020−03−24
2020−03−25
2020−03−26
2020−03−27
2020−03−28
2020−03−29
2020−03−30
2020−03−31
2020−04−01
Austria
Iceland
tests per mio.
0
500
1000
1500
2000
Figure 7: Times series of the number of tests per million inhabitants.
A.2. Number of confirmed infections per million inhabitants
2020−02−25
2020−02−26
2020−02−27
2020−02−28
2020−02−29
2020−03−01
2020−03−02
2020−03−03
2020−03−04
2020−03−05
2020−03−06
2020−03−07
2020−03−08
2020−03−09
2020−03−10
2020−03−11
2020−03−12
2020−03−13
2020−03−14
2020−03−15
2020−03−16
2020−03−17
2020−03−18
2020−03−19
2020−03−20
2020−03−21
2020−03−22
2020−03−23
2020−03−24
2020−03−25
2020−03−26
2020−03−27
2020−03−28
2020−03−29
2020−03−30
2020−03−31
2020−04−01
Austria
Iceland
infections per mio.
0
50
100
150
200
250
Figure 8: Times series of the number of confirmed infections per million inhabitants.
16 Dark Figure of COVID-19 Cases in Austria
B. Data
B.1. SARS-CoV-2 tests
Table 5: Statistics on the number of SARS CoV-2 tests performed in Austria and Iceland
(tests performed by NUHI).
Total Tests Total Tests p. mio Perc. Positive Tests
Date AUT ISL AUT ISL ISL/AUT AUT ISL
2020-02-25 218 24.49 0.92%
2020-02-26 321 41 36.06 112.60 3.12 0.62% 0%
2020-02-27 447 64 50.21 175.76 3.50 0.67% 0%
2020-02-28 763 77 85.71 211.46 2.47 0.39% 1.3%
2020-02-29 1649 109 185.23 299.34 1.62 0.55% 0.92%
2020-03-01 1826 131 205.11 359.76 1.75 0.77% 3.05%
2020-03-02 2120 179 238.13 491.58 2.06 0.85% 5.59%
2020-03-03 2683 224 301.37 615.16 2.04 0.78% 6.7%
2020-03-04 3138 305 352.48 837.60 2.38 0.92% 9.51%
2020-03-05 3711 367 416.84 1007.87 2.42 1.1% 10.08%
2020-03-06 4000 424 449.31 1164.41 2.59 1.38% 11.08%
2020-03-07 4308 482 483.90 1323.69 2.74 1.83% 11.2%
2020-03-08 4509 519 506.48 1425.30 2.81 2.31% 11.37%
2020-03-09 4734 617 531.75 1694.43 3.19 2.77% 11.02%
2020-03-10 5026 786 564.55 2158.55 3.82 3.62% 10.43%
2020-03-11 5362 935 602.30 2567.74 4.26 4.59% 11.34%
2020-03-12 5869 1188 659.25 3262.54 4.95 5.15% 10.27%
2020-03-13 6582 1545 739.33 4242.94 5.74 7.66% 9.19%
2020-03-14 7467 1868 838.74 5129.98 6.12 8.77% 8.89%
2020-03-15 8167 1987 917.37 5456.78 5.95 10.53% 8.91%
2020-03-16 8490 2278 953.65 6255.94 6.56 11.99% 8.43%
2020-03-17 10278 2823 1154.49 7752.64 6.72 12.96% 8.5%
2020-03-18 11977 3243 1345.34 8906.06 6.62 13.74% 9.56%
2020-03-19 13724 3699 1541.57 10158.35 6.59 14.67% 10.03%
2020-03-20 15613 4197 1753.76 11525.98 6.57 15.29% 10.25%
2020-03-21 18545 4517 2083.10 12404.77 5.95 15.17% 11.49%
2020-03-22 21368 4700 2400.20 12907.34 5.38 15.18% 11.51%
2020-03-23 23429 5057 2631.70 13887.74 5.28 16.75% 12.02%
2020-03-24 28391 5564 3189.07 15280.09 4.79 17.17% 12.8%
2020-03-25 32407 6025 3640.17 16546.11 4.55 17.16% 12.48%
2020-03-26 35995 6417 4043.20 17622.63 4.36 17.77% 12.98%
2020-03-27 39552 6921 4442.75 19006.74 4.28 18.71% 13%
2020-03-28 42750 7280 4801.97 19992.64 4.16 18.7% 13.1%
2020-03-29 46441 7790 5216.57 21393.22 4.10 18.38% 13.03%
2020-03-30 49455 8300 5555.12 22793.81 4.10 18.96% 12.8%
2020-03-31 52344 9115 5879.63 25031.99 4.26 19.05% 12.46%
2020-04-01 55863 6274.91 18.76%
B.2. SARS-CoV-2 confirmed infections
Austrian Journal of Statistics 17
Table 6: Statistics on the number of SARS CoV-2 infections in Austria and Iceland (infections
in Iceland are reported by NUHI).
Infections Infections p. mio
Date AUT ISL AUT ISL ISL/AUT
2020-02-25 2 0 0.22 0.00 0.00
2020-02-26 2 0 0.22 0.00 0.00
2020-02-27 3 0 0.34 0.00 0.00
2020-02-28 3 1 0.34 2.75 8.15
2020-02-29 9 1 1.01 2.75 2.72
2020-03-01 14 4 1.57 10.98 6.99
2020-03-02 18 10 2.02 27.46 13.58
2020-03-03 21 15 2.36 41.19 17.46
2020-03-04 29 29 3.26 79.64 24.45
2020-03-05 41 37 4.61 101.61 22.06
2020-03-06 55 47 6.18 129.07 20.89
2020-03-07 79 54 8.87 148.30 16.71
2020-03-08 104 59 11.68 162.03 13.87
2020-03-09 131 68 14.71 186.74 12.69
2020-03-10 182 82 20.44 225.19 11.02
2020-03-11 246 106 27.63 291.10 10.53
2020-03-12 302 122 33.92 335.04 9.88
2020-03-13 504 142 56.61 389.97 6.89
2020-03-14 655 166 73.57 455.88 6.20
2020-03-15 860 177 96.60 486.08 5.03
2020-03-16 1018 192 114.35 527.28 4.61
2020-03-17 1332 240 149.62 659.10 4.41
2020-03-18 1646 310 184.89 851.33 4.60
2020-03-19 2013 371 226.11 1018.86 4.51
2020-03-20 2388 430 268.24 1180.88 4.40
2020-03-21 2814 519 316.09 1425.30 4.51
2020-03-22 3244 541 364.39 1485.72 4.08
2020-03-23 3924 608 440.77 1669.71 3.79
2020-03-24 4876 712 547.71 1955.32 3.57
2020-03-25 5560 752 624.54 2065.17 3.31
2020-03-26 6398 833 718.67 2287.62 3.18
2020-03-27 7399 900 831.11 2471.62 2.97
2020-03-28 7995 954 898.05 2619.91 2.92
2020-03-29 8536 1015 958.82 2787.44 2.91
2020-03-30 9377 1062 1053.29 2916.51 2.77
2020-03-31 9974 1136 1120.35 3119.73 2.78
2020-04-01 10482 1177.41
Affiliation:
Rainer Hirk, Gregor Kastner, Laura Vana
Institute for Statistics and Mathematics
WU Vienna University of Economics and Business
Building D4, Level 4
1020 Vienna, Austria
E-mail: Rainer.Hirk@wu.ac.at,Gregor.Kastner@wu.ac.at,Laura.Vana@wu.ac.at
Austrian Journal of Statistics http://www.ajs.or.at/
published by the Austrian Society of Statistics http://www.osg.or.at/
Volume 49 Submitted: 2020-04-15
April 2020 Accepted: 2020-04-18
ResearchGate has not been able to resolve any citations for this publication.
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In the midst of the novel coronavirus (SARS-CoV-2) epidemic, examining reported case data could lead to biased speculations and conclusions. Indeed, estimation of unreported infections is crucial for a better understanding of the current emergency in China and in other countries. In this study, we aimed to estimate the unreported number of infections in China prior to 23 March 2020 restrictions. To do that, we developed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model which estimated unreported cases and infections from the reported number of deaths. Our approach relied on the fact that observed deaths were less likely to be affected by reporting biases than reported infections. Interestingly, we estimated that R0 was 2.43 (95%CI= 2.42 – 2.44) at the beginning of the epidemic, and that 92.9% (95%CI= 92.5% - 93.1%) of total cases were not reported. Similarly, the proportion of unreported new infections by day ranged from 52.1% to 100%, with a total of 91.8% (95%CI= 91.6% - 92.1%) unreported infections. Agreement between our estimates and those from previous studies proved that our approach was reliable to estimate prevalence and incidence of undocumented SARS-CoV2 infections. Once tested on Chinese data, our model could be applied on other countries with different surveillance and testing policies.
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Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%–90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%–62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.
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The number of novel coronavirus (COVID-19) cases worldwide continues to grow, and the gap between reports from China and statistical estimates of incidence based on cases diagnosed outside China indicates that a substantial number of cases are underdiagnosed (Nishiura et al., 2020a). Estimation of the asymptomatic ratio—the percentage of carriers with no symptoms—will improve understanding of COVID-19 transmission and the spectrum of disease it causes, providing insight into epidemic spread. Although the asymptomatic ratio is conventionally estimated using seroepidemiological data (Carrat et al., 2008, Hsieh et al., 2014), the collection of these data requires significant logistical effort, time, and cost. Instead, we propose a method of estimating the asymptomatic ratio by using information on Japanese nationals who were evacuated from Wuhan, China on charter flights.
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Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R0, of 2019-nCoV. Methods: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R0. Results: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403-540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18-25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R0 of 2019-nCoV at 2.56 (95% CI: 2.49-2.63). Conclusion: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation.
The number of US coronavirus cases has topped 13,000 as testing becomes more available
  • Dakin Andone
  • Christina Maxouris
  • Steve Almasy
  • Jen Christensen
Dakin Andone, Christina Maxouris, Steve Almasy, and Jen Christensen. The number of US coronavirus cases has topped 13,000 as testing becomes more available, March 19, 2020. https://edition.cnn.com/2020/03/19/healt h/us-coronavirus-case-updates-thursday/index.html [Accessed March 31, 2020].
The Hard Lessons of Italy's Devastating Coronavirus Outbreak
  • S Castelfranco
Castelfranco S (March 16, 2020). "The Hard Lessons of Italy's Devastating Coronavirus Outbreak." http://www.rfi.fr/en/europe/20200316-the-hard-lessons-of-italys-devastating-coronavirus-outbreak/ [Accessed March 31, 2020].