论文标题
通过随机SEIR模型对法国和意大利的第二波Covid-19模型进行建模
Modelling the second wave of COVID-19 infections in France and Italy via a Stochastic SEIR model
论文作者
论文摘要
Covid-19在世界上几个国家都采取了隔离措施。事实证明，这些措施可有效地显着降低病毒的流行。迄今为止，尚无有效的治疗或疫苗。为了保护公共卫生以及经济和社会质地，法国和意大利政府已部分发布了锁定措施。在这里，我们使用易感暴露感染的反射（SEIR）模型来推断两国流行病的长期行为，其中参数在随机上扰动以处理Covid-19的估计值的不确定性。我们的结果表明，参数和初始条件的不确定性在模型中迅速传播，并且可能导致流行病的不同结果导致或不会引起第二波感染。利用实际知识，两国的COVID-19患病率的渐近估计可能会波动100万单位的秩序。
COVID-19 has forced quarantine measures in several countries across the world. These measures have proven to be effective in significantly reducing the prevalence of the virus. To date, no effective treatment or vaccine is available. In the effort of preserving both public health as well as the economical and social textures, France and Italy governments have partially released lockdown measures. Here we extrapolate the long-term behavior of the epidemics in both countries using a Susceptible-Exposed-Infected-Recovered (SEIR) model where parameters are stochastically perturbed to handle the uncertainty in the estimates of COVID-19 prevalence. Our results suggest that uncertainties in both parameters and initial conditions rapidly propagate in the model and can result in different outcomes of the epidemics leading or not to a second wave of infections. Using actual knowledge, asymptotic estimates of COVID-19 prevalence can fluctuate of order of ten millions units in both countries.
