COVID-19: $R_0$ is lower where outbreak is larger

TL;DR

This study finds that in Lombardy, larger initial COVID-19 outbreaks are associated with lower transmission rates, likely due to behavioral changes like increased social distancing, highlighting the importance of detailed epidemiological data.

Contribution

It demonstrates a negative correlation between initial outbreak size and $R_0$, suggesting behavior change impacts disease spread beyond herd immunity effects.

Findings

Higher initial cases lead to lower $R_0$ in municipalities.

Behavioral responses like social distancing increase with outbreak size.

Results emphasize importance of real-time epidemiological data.

Abstract

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.