Publication Title
PloS One
Document Type
Article
Abstract/Description
In late 2019, the emergence of COVID-19 in Wuhan, China, led to the implementation of stringent measures forming the zero-COVID policy aimed at eliminating transmission. Zero COVID policy basically aimed at completely eliminating the transmission of COVID-19. However, the relaxation of this policy in late 2022 reportedly resulted in a rapid surge of COVID-19 cases. The aim of this work is to investigate the factors contributing to this outbreak using a new SEIR-type epidemic model with time-dependent level of immunity. Our model incorporates a time-dependent level of immunity considering vaccine doses administered and time-post-vaccination dependent vaccine efficacy. We find that vaccine efficacy plays a significant role in determining the outbreak size and maximum number of daily infected. Additionally, our model considers under-reporting in daily cases and deaths, revealing their combined effects on the outbreak magnitude. We also introduce a novel Physics Informed Neural Networks (PINNs) approach which is extremely useful in estimating critical parameters and helps in evaluating the predictive capability of our model.
Department
Mathematics
DOI
10.1371/journal.pone.0290368
Volume
18
Issue
11
ISSN
1932-6203
Date
11-16-2023
Citation Information
Ghosh, Samiran; Ogueda-Oliva, Alonso; Ghosh, Aditi; and Banerjee, Malay, "Understanding the Implications of Underreporting, Vaccine Efficiency and Social Behavior on the Post-pandemic Spread Using Physics Informed Neural Networks: A Case Study of China" (2023). Faculty Publications. 230.
https://digitalcommons.tamuc.edu/cose-faculty-publications/230
