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dynamic_survival_analysis

swMATH ID: 44446
Software Authors: 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.
Description: Projecting COVID-19 cases and hospital burden in Ohio. 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””.
Homepage: https://www.sciencedirect.com/science/article/pii/S0022519322003952
Source Code:  https://github.com/wasiur/dynamic_survival_analysis
Dependencies: Python
Keywords: COVID-19; dynamical survival analysis; SIR model; prediction
Related Software: GitHub; Python
Cited in: 1 Publication

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