High-resolution agent-based modeling of COVID-19 spreading in a small town

TL;DR

This paper presents a high-resolution agent-based model for simulating COVID-19 spread in small towns, enabling detailed analysis of testing and vaccination strategies with validation on real outbreak data.

Contribution

The paper introduces a detailed, open-source agent-based modeling platform for COVID-19 in small communities, supporting scenario testing and policy evaluation.

Findings

Model validated on real data from New Rochelle, NY

Supports testing, vaccination, and treatment strategy analysis

Accounts for other illnesses with similar symptoms

Abstract

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY -- one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches -- in hospitals or drive-through facilities -- and vaccination strategies that could prioritize vulnerable groups. Decision making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.