ArticleSN computer science2022
Testing Different COVID-19 Vaccination Strategies Using an Agent-Based Modeling Approach.
Article in SN computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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0 citing papers in PubMed, 5 citations in OpenAlex.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vaccination has been the long-awaited solution ever since the COVID-19 pandemic started. But the problem is that vaccine shots cannot be delivered at the same time to all populations, because of their limited quantity from one side, and their high demand from the other side. Therefore, countries need a way to test the effect of different distribution strategies before applying them. But how can they do this? To assist countries with this task, we built an agent-based model that runs on top of the Monte Carlo algorithm. This model simulates the spread of COVID-19 in a country where we can apply different NPIs at different times, and we can supply different kinds of vaccines using different strategies. In this study, we tested the outcomes of four vaccination strategies: older first, younger first, a mixed strategy, and a random strategy. We simulated these strategies in two different countries: France and Colombia. Then, we performed a comparative analysis to find which strategy might be the best for each country. Our results show that what is good for a country is not necessarily the best for the other one. Therefore, we proved that a vaccination strategy should be adapted to the structure of the population we are vaccinating. The system we built helps countries in this direction by allowing them to test the outcomes of their strategies before applying them in real life to select the one that minimizes human losses (deaths and infections).
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