Development of an interactive, agent-based local stochastic model of COVID-19 transmission and evaluation of mitigation strategies illustrated for the state of Massachusetts, USA

Abstract

Since its discovery in the Hubei province of China, the global spread of the novel coronavirus SARS-CoV-2 has resulted in millions of COVID-19 cases and hundreds of thousands of deaths. The spread throughout Asia, Europe, and the Americas has presented one of the greatest infectious disease threats in recent history and has tested the capacity of global health infrastructures. Since no effective vaccine is available, isolation techniques to prevent infection such as home quarantine and social distancing while in public have remained the cornerstone of public health interventions. While government and health officials were charged with implementing stay-at-home strategies, many of which had little guidance as to the consequences of how quickly to begin them. Moreover, as the local epidemic curves have been flattened, the same officials must wrestle with when to ease or cease such restrictions as to not impose economic turmoil. To evaluate the effects of quarantine strategies during the initial epidemic, an agent based modeling framework was created to take into account local spread based on geographic and population data with a corresponding interactive desktop and web-based application. Using the state of Massachusetts in the United States of America, we have illustrated the consequences of implementing quarantines at different time points after the initial seeding of the state with COVID-19 cases. Furthermore, we suggest that this application can be adapted to other states, small countries, or regions within a country to provide decision makers with critical information necessary to best protect human health.

Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data

“…The model-produced values outside the calibration interval (i.e. after July 12, 2020) are still in a good agreement with the reported data which indicate the predicting abilities of the model [41].…”

“…The Ukrainian territory can also be geographically covered with the local incidence stochastic modeling framework that has been presented earlier and illustrated in the context of the COVID-19 epidemic in the state of Massachusetts, USA [41], with some model adjustments and modifications for Ukraine. The adjustments are necessary since the country territory, the available incidence and mortality data quality and data reporting mechanisms do vary from country to country.…”

“…The local stochastic modeling framework is utilized to investigate the COVID-19 epidemic in Ukraine and assess the course of the epidemic under different quarantine scenarios. The framework is represented by an agent-based model in which every newly infected individual is represented as a programming language object with multiple characteristics of that individual [41]. This analytic framework and the corresponding model have been adopted for Ukraine, since the available surveillance data and country geography are different.…”

“…Those measures were slightly softened on April 24, 2020 and many services resumed even though the quarantine itself lasted till the end of June 2020 [40]. The initially reported cases from March 12, 2020 until April 12, 2020 represented the first set of cases and were retrospectively stochastically incorporated into the model as the model initial conditions [41]. For the second set of reported cases the model-produced outputs from April 22, 2020 until July 12, 2020 were used for model calibration, while the outputs from July 13, 2020 until August 1, 2020 were used for model validation and comparison with the reported data.…”

“…The additional details about the model formulation, parametrization, and calibration methods have been presented earlier [41] while the corresponding model code and the tool for the Ukrainian epidemic have been made public on GitHub [47].…”