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Understanding the implications of under-reporting, vaccine efficiency and social behavior on the post-pandemic spread using physics informed neural networks: A case study of China.
- Source :
-
PLoS ONE . 11/16/2023, Vol. 18 Issue 11, p1-27. 27p. - Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *COVID-19 pandemic
*VACCINE effectiveness
*CHINA studies
*DEATH rate
*VACCINES
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 18
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 173669696
- Full Text :
- https://doi.org/10.1371/journal.pone.0290368