Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling. (October 2021) - Zhu, Xinhe; Gao, Bingbing; Zhong, Yongmin; Gu, Chengfan; Choi, Kup-Sze
Abstract: This paper presents a new stochastic-based method for modelling and analysis of COVID-19 spread. A new deterministic Susceptible, Exposed, Infectious, Recovered (Re-infected) and Deceased-based Social Distancing model, named SEIR(R)D-SD, is proposed by introducing the re-infection rate and social distancing factor into the traditional SEIRD (Susceptible, Exposed, Infectious, Recovered and Deceased) model to account for the effects of re-infection and social distancing on COVID-19 spread. The deterministic SEIRD(R)D-SD model is further converted into the stochastic form to account for uncertainties involved in COVID-19 spread. Based on this, an extended Kalman filter (EKF) is developed based on the stochastic SEIR(R)D-SD model to simultaneously estimate both model parameters and transmission state of COVID-19 spread. Simulation results and comparison analyses demonstrate that the proposed method can effectively account for the re-infection and social distancing as well as uncertain effects on COVID-19 spread, leading to improved accuracy for prediction of COVID-19 spread. Highlights: It is difficult to track and predict the COVID-19 propagation based on a deterministic model due to the uncertain effects. A stochastic framework based on classical SEIRD model is developed to investigate the transition between compartments. A corresponded extended Kalman filter algorithm for stochastic model is also applied for evaluating the model performance. The improved understanding of the mechanism of spread can contribute to improve the effectiveness of public health measures.