A simple COVID-19 model applied to American states to simulate mitigation and containment strategies

Abstract

# Background The objective of the current work was to develop a simple predictive model of the spread of the novel coronavirus (i.e., 2019-nCoV or SARS-CoV-2) in the United States. The intent was to develop a simple, fast running model that could be used to make predictions regarding the effectiveness of various public policy proposals to mitigate or contain the spread of the virus. In the US, various states have adopted a patchwork of different strategies to manage the novel coronavirus disease (COVID-19) outbreak. The model can be used to predict medical resource needs to support comparison of possible strategies and forecast consequences in terms of virus-caused casualties. # Methods The simple model was constructed based on the SIR (Susceptible, Infected, Removed) epidemiological model. The current work builds on the work of Tang, et al., to enhance the SIR model with additional subpopulations. Accounting for different subpopulations in the model requires the use of additional differential equations to track the evolution of the subpopulations, but the basic principle is the same. The method tracks subpopulations of individuals that are exposed (but not yet showing symptoms) (E), asymptomatic carriers (A), and quarantined individuals (if quarantine measures are adopted as part of mitigation or containment strategies) (Q). The current model throughout this paper is referred to as the SEIR+AQ model. # Results Several SEIR+AQ models were constructed for different states in the US. In these different models, the only factors adjusted are specific to the state (e.g., population) and a small number of “cultural” parameters. The cultural parameters were adjusted to achieve good agreement with available validation data. Consistent good agreement over many states indicates that the model’s predictive efficacy is reasonable. Once the models were validated, the predictions were compared to the University of Washington Institute of Health Metrics and Evaluation (IHME) model predictions to verify the SEIR+AQ model through code-to-code benchmarking against a leading COVID-19 model. # Conclusions Two analytical case studies are presented. In the first, the model is used to predict the consequences of relaxing social distancing in favor of a particular containment strategy in the District of Columbia (DC). The predictions indicate that containment may be a viable strategy to reopen sectors of the economy in Washington, DC. In the second, the model is used to predict the consequences of relaxing social distancing in the state of Georgia (GA) without imposing any specific, new containment policies. The predictions show a dramatic increase in hospitalizations a short period after the relaxation of the social distancing orders.