The total number of COVID-19 infections is critical information for decision makers when assessing the progress of the pandemic, its implications, and policy options . Despite efforts to carefully monitor the COVID-19 pandemic, the reported number of confirmed cases is likely to underestimate the actual number of infections. We aim to estimate the total number of COVID-19 infections in a straightforward manner using a demographic scaling model. This model is broadly applicable as it is based on little input data : deaths attributable to COVID-19 , COVID-19 infection fatality rates  , and life tables. As many countries lack reliable estimates of age-specific infection fatality rates, we map them from a reference country onto countries of interest based on remaining life expectancy . This scaling accounts for cross-country differences in the age structure, the health status, and the health care system. We also introduce easy to apply formulas to quantify the bias that would be required in death counts and infection fatality rates in order to reproduce a certain estimate of infections. Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of  ]  confirmed cases. The uncertainty, however, is high, as the lower bound of the 95\% prediction interval suggests on average twice as many infections than confirmed cases, and the upper bound 10 times as many. Comparing our results with findings from local seroprevalence studies and applying our bias formulas shows that some of their infection estimates would only be possible if just a small fraction of COVID-19 related deaths were recorded, indicating that these seroprevalence estimates might not be representative for the total population.
Understanding how widely COVID-19 has spread is critical for examining the pandemic's progression . Despite efforts to carefully monitor the pandemic, the number of confirmed cases may underestimate the total number of infections. We introduce a demographic scaling model to estimate COVID-19 infections using an broadly applicable approach that is based on minimal data requirements: COVID-19 related deaths, infection fatality rates (IFRs) , and life tables. As many countries lack reliable estimates of age-specific IFRs, we scale IFRs between countries using remaining life expectancy as a marker to account for differences in age structures, health conditions, and medical services. Across 10 countries with most COVID-19 deaths as of May 13 , 2020, the number of infections is estimated to be four 95\% prediction interval: 2-11] times higher than the number of confirmed cases. Cross-country variation is high. The estimated number of infections is 1.4 million (six times the number of confirmed cases) for Italy; 3.1 million (2.2 times the number of confirmed cases) for the U.S.; and 1.8 times the number of confirmed cases for Germany, where testing has been comparatively extensive. Our prevalence estimates, however, are markedly lower than most others based on local seroprevalence studies . We introduce formulas for quantifying the bias that is required in our data on deaths in order to reproduce estimates published elsewhere. This bias analysis shows that either COVID-19 deaths are severely underestimated, by a factor of two or more; or alternatively, the seroprevalence based results are overestimates and not representative for the total population.
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The total number of
Understanding how widely
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infections is critical information for decision makers when assessing the progress of the pandemic, its implications, and policy options
has spread is critical for examining the pandemic's progression
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COVID-19
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reported
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is likely to underestimate the actual
may underestimate the total
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aim to estimate the total number of
introduce a demographic scaling model to estimate
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in a straightforward manner using a demographic scaling model. This model is broadly applicable as it
using an broadly applicable approach that
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little input data : deaths attributable to
minimal data requirements:
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, COVID-19
related deaths,
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(IFRs)
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infection fatality rates, we map them from a reference country onto countries of interest based on
IFRs, we scale IFRs between countries using
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. This scaling accounts for cross-country differences in the age structure, the health status, and the health care system. We also introduce easy to apply formulas to quantify the bias that would be required in death counts and infection fatality rates in order to reproduce a certain estimate of infections. Across the
as a marker to account for differences in age structures, health conditions, and medical services. Across
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April 17
May 13
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our estimates suggest that the total number of infected is approximately 4 times
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infections is estimated to be four
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95\% prediction interval: 2-11
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times higher than the number of
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The uncertainty, however, is high, as the lower bound of the 95\% prediction interval suggests on average twice as many infections than confirmed cases, and the upper bound 10 times as many. Comparing our results with findings from
Cross-country variation is high. The estimated number of infections is 1.4 million (six times the number of confirmed cases) for Italy; 3.1 million (2.2 times the number of confirmed cases) for the U.S.; and 1.8 times the number of confirmed cases for Germany, where testing has been comparatively extensive. Our prevalence estimates, however, are markedly lower than most others based on
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and applying our bias formulas shows that some of their infection estimates would only be possible if just a small fraction of
. We introduce formulas for quantifying the bias that is required in our data on deaths in order to reproduce estimates published elsewhere. This bias analysis shows that either
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related deaths were recorded, indicating that these seroprevalence estimates might not be
deaths are severely underestimated, by a factor of two or more; or alternatively, the seroprevalence based results are overestimates and not