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

TL;DR

The paper addresses the need for fine-grained epidemiological insight in small towns during COVID-19 by developing a high-resolution agent-based model with single-individual resolution. Its approach integrates detailed geospatial population synthesis, multi-location interactions, diverse testing and treatment pathways, and vaccination strategies, all calibrated to New Rochelle data and released as open-source. Key contributions include novel features such as drive-through testing, explicit modeling of COVID-19-like symptoms from other illnesses, and explicit hospital-worker dynamics, along with a vaccination study that highlights the potential and limits of targeted immunization. The work provides a practical, town-scale platform for policy analysis and scenario testing, offering quantitative guidance on how non-pharmaceutical interventions and vaccine deployment can mitigate early pandemic waves in mid-sized communities.

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.

Figures (7)

• Figure 1: Map of New Rochelle, NY, which highlights the residential and public buildings included in the database.
• Figure 2: Select characteristics of the created (virtual) households: a) sizes of residential buildings across town; b) percentage of households of a given size (Census data in brackets); c) distribution of employed members per family (Census data in brackets); and d) age distribution of the population.
• Figure 3: Schematic representation of modeled agent states and their possible transitions. Agent in the model can be in one of the following states: susceptible ($S$); exposed ($E$); symptomatic ($Sy$); removed - dead ($D$); removed - healthy/recovered ($R$); Agents in different states can undergo testing in a test car ($T_C$), or a hospital ($T_{Hs}$) after which they can be treated through home isolation ($I_{Hm}$), normal hospitalization ($H_N$), or hospitalization in an intensive care unit, ICU ($H_{ICU}$). In addition to symptomatic agents, exposed agents and agents who have COVID-19-like symptoms but are not infected can also be tested. Except for the symptomatic agents, all positive test results, including false positives, will lead to home isolation.
• Figure 4: Comparison of the modeled COVID-19 epidemic and officially reported data: a) The cumulative number of infections; b) New infections detected within a week; c) Active cases averaged over each week; d) The total number of deaths; e) Number of deaths in each week. The grey lines represent each of the simulation's 100 realizations, the blue line is the average value, and black circles are the reported data.
• Figure 5: Comparison of modeled and reported testing practices: a) The total number of performed tests; b) Fraction of positive test results, including false positives: the grey lines represent each of 100 realizations of the simulation, the blue line is the average value, and dashed black line is the reported data.