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Two methods, System Dynamics (SD) and Agent-Based Modeling (ABM), have been frequently used in recent years to investigate the complex nature of infectious diseases and their potential containment strategies.
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Using computational modeling, we have been able to identify common patterns in infectious diseases allowing us to leverage lessons learned through investigating past widespread disease events to predict who may get infected, where vaccination efforts should be prioritized, and how to limit the spread of infectious diseases in future events 4, 5, 6, 7. As the COVID-19 pandemic has spread across the globe since early 2020, researchers have identified gaps in data and our understanding of ways in which the disease spreads within and between communities including its potential impacts on general and at-risk populations 1, 2.Ĭomputational modeling has been long employed to further increase our understanding of complex infectious diseases as well as their development, spread dynamics, and potential treatments 3.
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The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.ĭuring the current COVID-19 pandemic, global efforts have taken place to contain the spread of the virus and develop effective non-therapeutic (e.g., social distancing, partial and full lockdowns) and therapeutic treatments (e.g., vaccination). Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The model’s two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended.
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We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. We outlined several case studies to demonstrate the model’s state- and local-level projection capabilities. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease.