A Multilevel Biosensor-Based Epidemic Simulation Model for COVID-19

Abstract

In order to design effective public health policies to combat the COVID-19 pandemic, local governments and organizations must be able to forecast the expected number of cases in their area. Although researchers have developed individual models for predicting COVID-19 based on sensor data without requiring a test, less research has been conducted on how to leverage those individual predictions in forecasting virus spread for determining hierarchical predictions from the community level to the state level. The multilevel adaptive and dynamic biosensor epidemic model, or m-ADBio, is designed to improve on the traditional susceptible–exposed–infectious–recovered (SEIR) model used to forecast the spread of COVID-19. In this study, the predictive performance of m-ADBio is examined at the state, county, and community levels through numerical experimentation. We find that the model improves over SEIR at all levels, but especially at the community level, where the m-ADBio model with sensor-based initial values yielded no statistically significant difference between the forecasted cases and the true observed data meaning that the model was highly accurate. Therefore, the m-ADBio model is expected to provide a more timely and accurate forecast to help policymakers optimize the pandemic management strategy.