Comparing micro-level and macro-level models for epidemic diffusion in the metro system

Abstract

ABSTRACT Few studies focus on how the ground transport system has increased COVID-19 transmission; the details of its spread remain unclear. The absence of station-level data obstructs healthcare professionals from effectively targeting anti-epidemic measures. This study employs agent-based modeling through GAMA software to identify Taipei metro stations implicated in initial transmission. In addition, a macro-level estimator is applied as a baseline model to compare COVID-19 arrival sequences at each station. Utilizing electronic metro ticket data, passenger travel patterns are discerned. We found (1) the average infection order of all stations, according to both models were not significantly different; (2) however, this difference between two model results became significant when the sample size was decreased. (3) Of all the stations, Taipei Main Station was the first because it has the highest passenger volume and the most connections. These early infected stations are near Taipei Main station and commercial or hub stations.