Agent-based modeling of COVID-19 outbreaks for New York state, UK and Novosibirsk region
Olga Krivorotko, Mariia Sosnovskaia, Ivan Vashchenko, Cliff, Kerr, Daniel Lesnic

TL;DR
This study employs an agent-based model, Covasim, calibrated with regional data and machine learning, to simulate COVID-19 outbreaks in New York, UK, and Novosibirsk, providing region-specific epidemic forecasts.
Contribution
It introduces a region-specific calibration of Covasim using machine learning and optimization techniques for accurate COVID-19 outbreak simulation.
Findings
In New York and Novosibirsk, cases remain stable if testing and measures are maintained.
In the UK, the number of cases is projected to decrease with current measures.
Forecast accuracy varies by region, higher for hospitalizations in Novosibirsk.
Abstract
This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA), the UK, and the Novosibirsk region (Russia). Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on the region's demographic and geographical features, the containment measures introduced; they are calibrated to data about COVID-19 spread in the region of interest. At the first stage of our study, epidemiological data (numbers of people tested, diagnoses, critical cases, hospitalizations, and deaths) for each of the mentioned regions were analyzed. The data were characterized in terms of seasonality, stationarity, and dependency spaces, and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model. At the…
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Taxonomy
TopicsCOVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Zoonotic diseases and public health
