Abstract
Since the onset of the COVID-19 pandemic many researchers and health advisory institutions have focused on virus spread prediction through epidemiological models. Such models rely on virus- and disease characteristics of which most are uncertain or even unknown for SARS-CoV-2. This study addresses the validity of various assumptions using an epidemiological simulation model. The contributions of this work are twofold. First, we show that multiple scenarios all lead to realistic numbers of deaths and ICU admissions, two observable and verifiable metrics. Second, we test the sensitivity of estimates for the number of infected and immune individuals, and show that these vary strongly between scenarios. Note that the amount of variation measured in this study is merely a lower bound: epidemiological modeling contains uncertainty on more parameters than the four in this study, and including those as well would lead to an even larger set of possible scenarios. As the level of infection and immunity among the population are particularly important for policy makers, further research on virus and disease progression characteristics is essential. Until that time, epidemiological modeling studies cannot give conclusive results and should come with a careful analysis of several scenarios on virus- and disease characteristics.
Highlights
The COVID-19 pandemic has disrupted society all across the world
The epidemiological models underlying these studies heavily rely on virus and disease characteristics such as the case fatality ratio (CFR)
Assessing the likelihood of scenarios based on number of deaths and ICU admissions To assess the validity of each of the scenarios, we first compared the death toll of COVID-19 predicted by the simulation model with an estimate of the actual death toll in the Netherlands
Summary
The COVID-19 pandemic has disrupted society all across the world. Many countries across the world followed when the virus reached them a few weeks or months later. Since many researchers and national health institutions have focused on predicting the course of the epidemic, assessing the effects of non-medical interventions in the form of social distancing, and evaluating the possibilities of an exit strategy [1,2,3]. The epidemiological models underlying these studies heavily rely on virus and disease characteristics such as the case fatality ratio (CFR). Within just a few months researchers made great progress in estimating these characteristics [4,5,6,7], and a plethora of data sources and scientific studies rapidly became available
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