Agent-based model using GPS analysis for infection spread and inhibition mechanism of SARS-CoV-2 in Tokyo

TL;DR

This study uses GPS-based agent modeling to analyze SARS-CoV-2 spread in Tokyo, revealing how infection dynamics depend on city activities and suggesting targeted measures can effectively curb the pandemic.

Contribution

It introduces an agent-based model incorporating real GPS data for Tokyo to analyze infection spread and evaluate targeted intervention strategies.

Findings

Infection primarily spread in service industry areas like restaurants.

Reducing outings or improving facility measures can significantly curb infection.

Targeted measures in service facilities are effective alternatives to city-wide lockdowns.

Abstract

Analyzing the SARS-CoV-2 pandemic outbreak based on actual data while reflecting the characteristics of the real city provides beneficial information for taking reasonable infection control measures in the future. We demonstrate agent-based modeling for Tokyo based on GPS information and official national statistics and perform a spatiotemporal analysis of the infection situation in Tokyo. As a result of the simulation during the first wave of SARS-CoV-2 in Tokyo using real GPS data, the infection occurred in the service industry, such as restaurants, in the city center, and then the infected people brought back the virus to the residential area; the infection spread in each area in Tokyo. This phenomenon clarifies that the spread of infection can be curbed by suppressing going out or strengthening infection prevention measures in service facilities. It was shown that pandemic measures in Tokyo could be achieved not only by strong control, such as the lockdown of cities, but also by thorough infection prevention measures in service facilities, which explains the curb phenomena in real Tokyo.