Projecting COVID-19 cases and hospital burden in Ohio

KhudaBukhsh, Wasiur R.; Bastian, Caleb Deen; Wascher, Matthew; Klaus, Colin; Sahai, Saumya Yashmohini; Weir, Mark H.; Kenah, Eben; Root, Elisabeth; Tien, Joseph H.; Rempała, Grzegorz A.

Abstract

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Citation

KhudaBukhsh, W. R., Bastian, C. D., Wascher, M., Klaus, C., Sahai, S. Y., Weir, M. H., …Rempała, G. A. (2023). Projecting COVID-19 cases and hospital burden in Ohio. Journal of Theoretical Biology, 561, Article 111404. https://doi.org/10.1016/j.jtbi.2022.111404

• Journal Article Type: Article
• Acceptance Date: Dec 26, 2022
• Online Publication Date: Jan 13, 2023
• Publication Date: Mar 21, 2023
• Deposit Date: Jan 14, 2023
• Publicly Available Date: Jan 18, 2023
• Journal: Journal of Theoretical Biology
• Print ISSN: 0022-5193
• Electronic ISSN: 1095-8541
• Publisher: Elsevier BV
• Peer Reviewed: Peer Reviewed
• Volume: 561
• Article Number: 111404
• DOI: https://doi.org/10.1016/j.jtbi.2022.111404
• Keywords: Applied Mathematics; General Agricultural and Biological Sciences; General Immunology and Microbiology; General Biochemistry, Genetics and Molecular Biology; Modeling and Simulation; General Medicine; Statistics and Probability
• Public URL: https://nottingham-repository.worktribe.com/output/15940568
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0022519322003952?via%3Dihub