TY - JOUR
T1 - Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling
AU - Gao, Bingbing
AU - Zhong, Yongmin
AU - Gu, Chengfan
AU - Choi, Kup Sze
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
KW - And extended kalman filter
KW - COVID-19 modelling
KW - Re-infection
KW - Social distancing
KW - Stochastic epidemiological model
UR - http://www.scopus.com/inward/record.url?scp=85113998317&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2021.104810
DO - 10.1016/j.compbiomed.2021.104810
M3 - Journal article
C2 - 34478923
AN - SCOPUS:85113998317
SN - 0010-4825
VL - 137
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 104810
ER -