Influence of Absolute Humidity, Temperature and Population Density on COVID-19 Spread and Decay Durations: Multi-prefecture Study in Japan

TL;DR

This study examines how absolute humidity, temperature, and population density influence COVID-19 spread and decay durations across Japanese prefectures, providing insights for future pandemic intervention planning.

Contribution

It identifies key environmental and demographic factors affecting COVID-19 dynamics, with a focus on the roles of absolute humidity and population density, in a multi-prefecture Japanese context.

Findings

Population density correlates with spread and decay durations.

Maximum absolute humidity influences decay duration normalized by density.

Multivariate model explains 53% of variation in spread duration.

Abstract

This study analyzed the spread and decay durations of the COVID-19 pandemic in different prefectures of Japan. During the pandemic, affordable healthcare was widely available in Japan and the medical system did not suffer a collapse, making accurate comparisons between prefectures possible. For the 16 prefectures included in this study that had daily maximum confirmed cases exceeding ten, the number of daily confirmed cases follow bell-shape or log-normal distribution in most prefectures. A good correlation was observed between the spread and decay durations. However, some exceptions were observed in areas where travelers returned from foreign countries, which were defined as the origins of infection clusters. Excluding these prefectures, the population density was shown to be a major factor affecting the spread and decay patterns, with R2=0.39 (p<0.05) and 0.42 (p<0.05), respectively, approximately corresponding to social distancing. The maximum absolute humidity was found to affect the decay duration normalized by the population density (R2>0.36, p <0.05). Our findings indicate that the estimated pandemic spread duration, based on the multivariate analysis of maximum absolute humidity, ambient temperature, and population density (adjusted R2=0.53, p-value<0.05), could prove useful for intervention planning during potential future pandemics, including a second COVID-19 outbreak.