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

“…To investigate the COVID-19 epidemic in Ukraine and to assess its dynamics under different mitigation scenarios, we utilized our general stochastic agent-based modeling framework ( Kirpich et al, 2021 ). The model was adjusted to the Ukrainian settings and fit into the observed Ukrainian data.…”

“…The reported data was separated into three parts. The initially reported cases from March 12, 2020 to April 12, 2020 were retrospectively incorporated into the model as the initial conditions ( Kirpich et al, 2021 ). The reported and model-produced data from April 22, 2020 to July 12, 2020 were used for model calibration, and from July 13, 2020 to August 1, 2020 – for model validation.…”

“…Optimization of model parameters has been performed by minimizing the sum of squared differences between the model-produced outputs (across multiple runs) and the calibration data using the Nelder-Mead numerical minimization method ( Nelder and Mead, 1965 ). The population basic reproduction number Milligan and Barrett (2015) has been estimated from the model-produced distribution quantiles (5%, median, 95%) of the reproduction numbers of individuals and summarized across multiple stochastic runs ( Kirpich et al, 2021 ). The estimates for were produced from the model fit to real data with the assumption that interventions have started almost immediately after the virus introduction.…”

“…The additional details about the model can be found in our earlier study ( Kirpich et al, 2021 ), and the model implementation tailored to Ukrainian data is available at https://github.com/quantori/COVID19-Ukraine-Transmission .…”

Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic

“…Second, our estimated instantaneous reproduction numbers are closely aligned with the daily mobility pattern, suggesting that the lock-down and stay-at-home order are effective for controlling the COVID-19 outbreak in Massachusetts. Previous research also found the NPIs, including lock-down and stay-at-home order, were effective for containing the COVID-19 outbreak and reducing the public healthcare burden in the state of Massachusetts [32], in the United States [33] and worldwide [34,35]. Third, we observed the daily mobility was slowly increasing from May 2020, and this could explain why the reproduction number estimates rose again around May 11, at which point the reopening plan was unveiled for Massachusetts.…”