  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.
