COVID-19 infection spread and human mobility

Abstract

Given that real-world infection-spread scenarios pose many uncertainties, and predictions and simulations may differ from reality, this study explores factors essential for more realistically describing an infection situation. It furnishes three approaches to the argument that human mobility can create an acceleration of the spread of COVID-19 infection and its cyclicality under the simultaneous relationship. First, the study presents a dynamic model comprising the infection–mobility trade-off and mobility demand, where an increase in human mobility can cause infection explosion and where, conversely, an increase in new infections can be made temporary by suppressing mobility. Second, using time-series data for Japan, it presents empirical evidence for a stochastic trend and cycle in new infection cases. Third, it employs macroeconometrics to ascertain the feasibility of our model’s predictions. Accordingly, from March 2020 to May 2021, the sources of COVID-19 infection spread in Japan varied significantly over time, and each change in the trend and cycle of new infection cases explained approximately half the respective variation.