Zero Hunger Lab, Department of Econometrics and Operations Research, Tilburg School of Economics and Management, Tilburg University, Tilburg, The Netherlands.
PLoS One. 2021 Mar 3;16(3):e0245519. doi: 10.1371/journal.pone.0245519. eCollection 2021.
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.
自 COVID-19 大流行以来，许多研究人员和健康咨询机构一直专注于通过流行病学模型预测病毒传播。这些模型依赖于病毒和疾病特征，而对于 SARS-CoV-2 来说，大多数特征都是不确定的，甚至是未知的。本研究使用流行病学模拟模型来验证各种假设的有效性。这项工作的贡献有两点。首先，我们表明，多种情景都导致了死亡和 ICU 入院人数的实际数字，这是两个可观察和可验证的指标。其次，我们测试了对受感染和免疫个体数量的估计的敏感性，并表明这些估计在不同情景之间有很大差异。请注意，本研究中测量的变化量仅仅是下限：流行病学建模包含比本研究中四个更多参数的不确定性，如果包括这些参数，将会导致更多可能的情景。由于人群中的感染和免疫水平对政策制定者尤为重要，因此进一步研究病毒和疾病进展特征至关重要。在那之前，流行病学模型研究不能得出结论性的结果，应该对病毒和疾病特征的几个情景进行仔细分析。
