论文标题
多组SEIR模型中的异质社交互动和COVID-19锁定结果
Heterogeneous social interactions and the COVID-19 lockdown outcome in a multi-group SEIR model
论文作者
论文摘要
我们研究了SEIR模型的变体，用于解释法国Covid-19的统计学的某些定性特征。标准SEIR模型基本上区分了两个方案：该疾病受到控制，感染者的数量迅速减少，或者疾病传播并污染很大一部分人口，直到获得群疫苗。锁定后，乍一看，社会距离似乎不足以控制爆发。我们在这里讨论一个可能的解释，即锁定是在创造社会异质性：即使大多数人口符合封锁规则，一小部分人民仍然必须维持正常或高的社交互动，例如卫生工作者，例如，基本服务的提供者，基本服务的提供者等。这在显而易见的流行病水平传播中，基于RE-SECTIMSIMETIOD的高度概述。但是，这些措施仅限于平均值，而人口内部的差异在流行病爆发的峰值和大小中起着至关重要的作用，并且倾向于降低这两个指标。我们提供理论和数值结果以维持这种观点。
We study variants of the SEIR model for interpreting some qualitative features of the statistics of the Covid-19 epidemic in France. Standard SEIR models distinguish essentially two regimes: either the disease is controlled and the number of infected people rapidly decreases, or the disease spreads and contaminates a significant fraction of the population until herd immunity is achieved. After lockdown, at first sight it seems that social distancing is not enough to control the outbreak. We discuss here a possible explanation, namely that the lockdown is creating social heterogeneity: even if a large majority of the population complies with the lockdown rules, a small fraction of the population still has to maintain a normal or high level of social interactions, such as health workers, providers of essential services, etc. This results in an apparent high level of epidemic propagation as measured through re-estimations of the basic reproduction ratio. However, these measures are limited to averages, while variance inside the population plays an essential role on the peak and the size of the epidemic outbreak and tends to lower these two indicators. We provide theoretical and numerical results to sustain such a view.
