Abstract
Governments worldwide have faced unprecedented challenges in managing the COVID-19 pandemic, particularly in implementing effective lockdown policies and devising transportation plans. As infections continue to surge exponentially, the need for carefully regulating travel has become paramount. However, existing research has struggled to address this issue comprehensively for India, a country characterized by diverse transportation networks and a vast population spread across different states. This study aims to fill this crucial research gap by analyzing the spread of infection, recovery, and mortality in the state of Goa, India, over a twenty-eight-day period. Through the use of agent-based simulations, we investigate how individuals interact and transmit the virus while utilizing trains, flights, and buses in two key scenarios: unrestricted and restricted local movements. By conducting a detailed comparison of all transportation modes in these two distinct lockdown settings, we examine the speed and intensity of infection spread. Our findings reveal that trains contribute to the highest transmission rates within the state, followed by flights and then buses. Notably, the combined effect of all modes of transport is not merely additive, emphasizing the urgent need for analysis to prevent infections from surpassing critical thresholds.
R. Godse and S. Bhat—These authors contributed equally to this work.
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Godse, R., Bhat, S., Mestry, S., Naik, V. (2024). Estimating the Spread of COVID-19 Due to Transportation Networks Using Agent-Based Modeling. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2023. Lecture Notes in Computer Science(), vol 14546. Springer, Cham. https://doi.org/10.1007/978-3-031-55326-4_2
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