Open AccessAn application of the ensemble Kalman filter in epidemiological modelling
Author(s) -
Rajnesh Lal,
Weidong Huang,
Zhenquan Li
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0256227
Subject(s) - ensemble kalman filter , kalman filter , covid-19 , estimation , computer science , estimation theory , filter (signal processing) , data assimilation , extended kalman filter , econometrics , data mining , statistics , algorithm , mathematics , artificial intelligence , geography , engineering , medicine , meteorology , disease , systems engineering , pathology , infectious disease (medical specialty) , computer vision
Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses’ dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter’s performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation.