Abstract
Background:A Susceptible–Exposed–Infected–Removed (SEIR) model was developed to forecast the spread of the novel coronavirus (SARS-CoV-2) in the United States and the implications of re-opening and hospital resource utilization. The model relies on the specification of various parameters that characterize the virus and the population being modeled. However, several of these parameters can be expected to vary significantly between states. Therefore, a genetic algorithm was developed that adjusts these population-dependent parameters to fit the SEIR model to data for any given state. Methods:Publicly available data was collected from each state in terms of the number of positive COVID-19 cases and the number of COVID-19-caused deaths and used as inputs into a SEIR model to predict the spread of COVID infections in a given population. A genetic algorithm was designed where the genes are the state-dependent parameters from the model. The algorithm operates by determining the fitness of a given set of genes, applying selection, using selected agents to reproduce with cross-over, applying random mutation, and simulating several generations. Findings and Conclusions:Use of the genetic algorithm produces exceptionally good agreement between the model and available data. Deviations in the parameters were examined to see if the trends were reasonable.
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