ABSTRACT
Infectious disease outbreaks challenge societies by creating dynamic stochastic infection networks between human individuals in geospatial and demographical contexts. Minimizing human and socioeconomic costs of SARS-CoV-2 and future global pandemics requires data-driven and context-specific integrative modeling of detection-tracing, healthcare, and non-pharmaceutical interventions for decision-processes and reopening strategies. Traditional population-based epidemiological models cannot simulate temporal infection dynamics for individual human behavior in specific geolocations. We present an integrated geolocalized and demographically referenced spatio-temporal stochastic network- and agent-based model of COVID-19 dynamics for human encounters in real-world communities. Simulating intervention scenarios, we quantify effects of protection and identify the importance of early introduction of test-trace measures. Critically, we observe bimodality in SARS-CoV-2 infection dynamics so that the outcome of reopening can flip between good and poor outcomes stochastically. Furthermore, intervention effectiveness depends on strict execution and temporal control i.e. leaks can prevent successful outcomes. Schools are in many scenarios hubs for transmission, reopening scenarios are impacted by infection chain stochasticity and subsequent outbreaks do not always occur. This generalizable geospatial and individualized methodology is unique in precision and specificity compared to prior COVID-19 models [6, 16, 17, 19] and is applicable to scientifically guided decision processes for communities worldwide.
