Estimation of Effective Reproductive Number to Improve Local Area Infectious Disease Forecasting

Oregon Convention Center 

Accurate estimation of region-specific infectious disease reproductive numbers is critical for disaster planning and resource allocation. However, estimates of the reproductive number can substantially vary by region, especially in the absence of quality data. We propose a "Bayesian spatiotemporal model (via Bayesian-INLA)" to improve estimation of the reproductive number by borrowing information from similar regions. We show that this method can lead to improved infectious disease forecasts over standard approaches. We employ an extended SEIR-type compartmental model for forecasting the COVID-19 disease at the county level in South Carolina. We employ the model in forecasting a wave of COVID-19 using the reproductive number estimate incorporating the information from the current wave and previous wave through the standard techniques and the proposed method, then assess the model performance for all the estimation methods using the percentage agreement metric. We also compare the results based on the percentage agreement of county ranking for identifying the most impacted areas. The proposed method is effective in small area infectious disease forecasting.

Infectious Disease Epidemiology

Modeling

COVID-19

Reproductive Number

Bayesian Methodology 

Section on Statistics in Epidemiology