On the relation between active population and infection rate of COVID-19

TL;DR

This study empirically examines how the active population density in Tokyo correlates with COVID-19 infection rates, revealing a linear relationship and behavioral impacts on infection dynamics.

Contribution

It provides empirical evidence linking active population density to infection rates and highlights behavioral effects on disease spread, challenging conventional quadratic assumptions.

Findings

Infection reporting delay is approximately 15 days.

Infection rate scales linearly with active population density.

Behavioral changes influence deviations from the scaling relation.

Abstract

The relation between the number of passengers in the main stations and the infection rate of COVID19 in Tokyo is empirically studied. Our analysis based on conventional compartment model suggests: 1) Average time from the true day of infection to the day the infections are reported is about $15$ days. 2) The scaling relation between the density of active population and the infection rate suggests that the increase of infection rate is linear to the active population rather than quadratic, as that is assumed in the conventional SIR model. 3) Notable deviations from the overall scaling relation seems to correspond to the change of the peoples's behavior in response to the public announcements of action regulation.