Impact of Globally Fair COVID-19 Vaccination: An Analysis based on Agent-Based Simulation

Open Access
Article
Conference Proceedings
Authors: Kashif ZiaMuhammad Shafi

Abstract: In this paper, an Agent-Based Model (ABM) is proposed to evaluate the impact of COVID-19 vaccination drive in different settings. The main focus is to evaluate the counter-effectiveness of disparity in vaccination drive among different regions/countries. The proposed model is simple yet novel in the sense that it captures the spatial transmission-induced activity into consideration, through which we are able to relate the transmission model to the mutated variations of the virus. Some important what-if questions are asked in terms of the number of deaths, the time required, and the percentage of population needed to be vaccinated before the pandemic is eradicated. The simulation results have revealed that it is necessary to maintain a global (rather than regional or country-oriented) vaccination provisioning in case of a new pandemic or continual efforts against COVID-19, instead of a self-centered approach.A simplistic agent-based model of virus transmission is used consisting of minimal states of susceptible, vaccinated, infected, and recovered. A moving agent in one of these states is tightly bound to the underlying space, where the space is divided into regions to evaluate the region-based vs. global vaccination drive. Additionally, the virus gets mutated, where the extent of mutation is directly related to spatial activity representing the transmissions. And the inactivity is directly proportional to the mutated variant at a location. The results of the simulation suggest that it is necessary to maintain a global (rather than regional or country-oriented) vaccination drive in case of a new pandemic or continual efforts against COVID-19. It results in a lesser number of deaths, time, and quantity of vaccination required.

Keywords: COVID-19, vaccination, agent-based model, disparity, equity, multi-agent simulation

DOI: 10.54941/ahfe1001362

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