Model Shows Minimal Benefit in Vaccinating High-Risk Population First
Smaller cities and towns with a short supply of intensive care units and tight budgets need effective precautionary measures.
Investigators from the NYU Tandon School of Engineering have developed a novel open-source platform that is able to create predictive models of the coronavirus disease 2019 (COVID-19) based on aggregated data from numerous observations across different strata of society. The study was published in the journal Advanced Theory and Simulations.
The study was conducted in the town of New Rochelle in New York, picked because of its comparative size to other cities in the United States and due to the fact that it was one of the first outbreaks registered in the country.
"We chose New Rochelle not only because of its place in the COVID timeline, but because agent-based modelling for mid-size towns is relatively unexplored despite the U.S. being largely composed of such towns and small cities," Maurizio Porfiri, the leader behind the research team said.
The investigators used the agent-based model (ABM) to replicate the geographic and demographic information gathered about the town from U.S. Census statistics. They then superimposed a temporal and spatial high-resolution representation of the COVID-19 pandemic at an individual level, incorporating features like physical locations, behavioral trends and local mobility patterns.
Findings from the study suggest that the prioritization of high-risk individuals for vaccination only has a marginal impact on the total number of deaths caused by the COVID-19 virus. Investigators stated that large fractions should be vaccinated to see significant improvements. They also showed that restrictive measures like social distancing, mask wearing, and mobility restrictions far outweigh any selective vaccination scenarios.
The model used is unique because it has the potential to explore different approaches for testing in hospitals and drive-through facilitates and strategies that could prioritize more vulnerable groups.
"We think decision making by public authorities could benefit from this model, not only because it is 'open source,' but because it offers a 'fine-grain' resolution at the level of the individual and a wide range of features," Porfiri said.