Modeling the spread of COVID-19 in Germany: Early assessment and possible scenarios
Modeling the spread of COVID-19 in Germany: Early assessment and possible scenarios
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DOI：
10.1371/journal.pone.0238559
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发表时间：
2020-09-04
期刊：
影响因子：
3.7
通讯作者：
Lippert, Thomas
中科院分区：
文献类型：
--
作者：
Barbarossa, Maria Vittoria;Fuhrmann, Jan;Lippert, Thomas
新型冠状病毒(SARS-CoV-2)于2019年12月底在中国身上发现，并导致新冠肺炎疾病，同时导致全球爆发，截至2020年4月17日，已有约220万确诊病例，超过15万人死亡。在这项工作中，考虑到实际和假设的非药物干预的影响，使用数学模型来再现德国新冠肺炎疫情早期演变的数据。塞尔型微分方程组被扩展到考虑未检测到的感染、感染阶段和年龄组。这些模型是根据4月5日之前的数据进行校准的。4月6日至14日的数据用于模型验证。我们模拟了缓解当前疫情的不同可能战略，减缓了病毒的传播，从而减少了每日确诊病例的高峰、住院或重症监护病房的需求，并最终减少了死亡人数。我们的结果表明，如果同时进一步增加检测活动，严格隔离发现的病例，并减少与风险群体的接触，部分(和逐步)取消已引入的控制措施可能很快就会成为可能。
The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020. In this work, mathematical models are used to reproduce data of the early evolution of the COVID-19 outbreak in Germany, taking into account the effect of actual and hypothetical non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended to account for undetected infections, stages of infection, and age groups. The models are calibrated on data until April 5. Data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases, and reduced contact to risk groups.