Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling

Bingbing Gao, Yongmin Zhong, Chengfan Gu, Kup Sze Choi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number104810
JournalComputers in Biology and Medicine
Volume137
DOIs
Publication statusPublished - Oct 2021

Keywords

  • And extended kalman filter
  • COVID-19 modelling
  • Re-infection
  • Social distancing
  • Stochastic epidemiological model

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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