论文标题
COVID-19：$ r_0 $在爆发更大的地方较低
COVID-19: $R_0$ is lower where outbreak is larger
论文作者
论文摘要
我们使用来自COVID-19爆发影响最大的意大利地区伦巴第（Lombardy）的每日数据，以分别校准每个市政当局的SIR模型。这些都被相同的卫生系统所涵盖，在我们关注的锁骨后阶段，所有这些都遵守相同的社会疏远法规。我们发现，在分析的期间开始时，病例数较高的市政当局的扩散率较低，这不能归因于牛群的免疫力。特别是，与$ r_0 $作为\ emph {preadiveor}的作用相反，估计的基本繁殖数量（$ r_0 $）与初始爆发大小之间存在牢固且强烈的负相关。我们探讨了这种现象的不同可能的解释，并得出结论，较高的案件会导致行为变化，例如对人群中社会疏远措施的采用更严格，从而减少了蔓延。该结果要求对详细的流行病学数据进行透明的实时分布，因为这些数据会影响爆发影响的地区人口的行为。
We use daily data from Lombardy, the Italian region most affected by the COVID-19 outbreak, to calibrate a SIR model individually on each municipality. These are all covered by the same health system and, in the post-lockdown phase we focus on, all subject to the same social distancing regulations. We find that municipalities with a higher number of cases at the beginning of the period analyzed have a lower rate of diffusion, which cannot be imputed to herd immunity. In particular, there is a robust and strongly significant negative correlation between the estimated basic reproduction number ($R_0$) and the initial outbreak size, in contrast with the role of $R_0$ as a \emph{predictor} of outbreak size. We explore different possible explanations for this phenomenon and conclude that a higher number of cases causes changes of behavior, such as a more strict adoption of social distancing measures among the population, that reduce the spread. This result calls for a transparent, real-time distribution of detailed epidemiological data, as such data affects the behavior of populations in areas affected by the outbreak.
