Tang, Biao, Ma, Kexin, Liu, Yan, Wang, Xia, Tang, Sanyi, Xiao, Yanni and Cheke, Robert ORCID: https://orcid.org/0000-0002-7437-1934 (2024) Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: a mechanistic and deep learning study. PLoS Computational Biology, 20 (9):e1012497. ISSN 1553-734X (doi:10.1371/journal.pcbi.1012497)

Preview

PDF (VoR (OA)) 48297 CHEKE_Managing_Spatio-temporal_Heterogeneity_of_Susceptibles_by_Embedding_it_into_an_Homogeneous_Model_A_Mechanistic_and_Deep_Learning_Study_(OA)_2024.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview

Abstract

Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modelling framework integrating the spatio-temporal heterogeneity of susceptible individuals into homogeneous models, by introducing a continuous recruitment process for the susceptibles. A neural network approximates the recruitment rate to develop a Universal Differential Equations (UDE) model. Simultaneously, we pre-set a specific form for the recruitment rate and develop a mechanistic model. Data from a COVID Omicron variant outbreak in Shanghai are used to train the UDE model using deep learning methods and to calibrate the mechanistic model using MCMC methods. Subsequently, we
project the attack rate and peak of new infections for the first Omicron wave in China after the adjustment of the dynamic zero-COVID policy. Our projections indicate an attack rate and a peak of new infections of 80.06

Item Type:	Article
Uncontrolled Keywords:	neural network, machine learning, COVID-19, heterogeneity, model, Shanghai, epidemiology
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Natural Resources Institute Faculty of Engineering & Science > Natural Resources Institute > Centre for Sustainable Agriculture 4 One Health Faculty of Engineering & Science > Natural Resources Institute > Centre for Sustainable Agriculture 4 One Health > Behavioural Ecology
Last Modified:	15 Oct 2024 14:11
URI:	http://gala.gre.ac.uk/id/eprint/48297

Actions (login required)

View Item

Downloads