SEIR-SEI-EnKF: A New Model for Estimating and Forecasting Dengue Outbreak Dynamics

Abstract

Dengue fever is an acute mosquito-borne viral infection that results in a heavy social burden in many tropical and subtropical regions. Accurate forecasts of dengue outbreak allow the local health officials to take proactive action such as positioning mosquito control equipment or preparing medical resources. We developed a new model for dengue outbreak estimation and forecast that adopts the vector-borne disease model SEIR-SEI with compartments Susceptible-Exposed-Infectious-Recovered (for host) and Susceptible-Exposed-Infectious (for vector) into the ensemble Kalman filtering (EnKF) assimilation method. The SEIR-SIR-EnKF model was first validated using synthetic dengue outbreak in twin experiments. Then, the model produced good performance when applied to estimate and forecast the dengue outbreak dynamics with real historical time-series cases in 3 different cities. Furthermore, we compared the accuracy of the real-time predictions between SEIR-SEI-EnKF model, SEIR-EnKF model, and SIR-EnKF model; we found the SEIR-SEI-EnKF model had the most accurate predictions.

Keywords

• Ensemble Kalman filter
• estimation of disease outbreak dynamics
• vector-borne disease modeling
• forecast of dengue cases