Authors:
Shikha Bhat
;
Ruturaj Godse
;
Shruti Mestry
and
Vinayak Naik
Affiliation:
BITS Pilani, Goa, India
Keyword(s):
Agent-Based Simulation, COVID-19, Artificial Intelligence.
Abstract:
The COVID-19 pandemic has posed challenges for governments concerning lockdown policies and transportation plans. The exponential rise in infections has highlighted the importance of managing restrictions on travel. Previous research around this topic has not been able to scale and address this issue for India, given its diversity in transportation networks and population across different states. In this study, we analyze the patterns of the spread of infection, recovery, and death specifically for the state of Goa, India, for twenty-eight days. Using agent-based simulations, we explore how individuals interact and spread the disease when traveling by trains, flights, and buses in two significant settings - unrestricted and restricted local movements. Our findings indicate that trains cause the highest spread of infection within the state, followed by flights and then buses. Contrary to what may be assumed, we find that the effect of combinations of all modes of transport is not addi
tive. With multiple modes of transport activities, the cases rise exponentially faster. We present equivalence points for the number of vehicles running per day in unrestricted and restricted movement settings, e.g., one train a day in unrestricted movement spreads the disease as eight trains a day in restricted movement.
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