Understanding spatiotemporal variation of social vulnerabilities from longitudinal hurricane-pandemic data: A multilevel model of the Covid-19 pandemic during hurricane Sally in Florida

Date

2023-11-01

Author

Abazari, Seyedreza
Vanli, O. Arda
Alişan, Onur
Ozguven, Eren Erman

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

3
views

0
downloads

Cite This

An important question in the context of compound disasters is the degree to which geophysical disasters amplify the transmission of infectious diseases during pandemics and how this relation-ship is influenced by the social vulnerability of affected populations. This article proposes a spatiotemporal modeling approach to understand spatially varying social, demographic and health drivers of vulnerability during pandemics co-occurring with geophysical hazards. A multilevel mixed-effects model is developed to investigate the dynamic association between census tract -level Covid-19 case count trajectories co-occurring with a hurricane and demographic, socioeconomic and health factors. A state-level analysis is conducted to identify the distinct geographical regions in which significant changes are seen in the infection count trends due to the hurricane. A subsequent region-level analysis is performed to describe, at a higher spatial resolution, the im-pact of social vulnerability on the infection count trajectories at a community level. The method provides an approach to systematically study the effects of compound hazards and distinct pat-terns of infectious disease spread during hurricanes by quantifying (1) dynamic associations between infection counts and social factors and (2) spatial heterogeneities of these associations between communities. A case study for modeling the spatiotemporal variation of social vulnerability with data from Covid-19 pandemic and Hurricane Sally in Florida is presented to illustrate the application of the approach.

URI

https://hdl.handle.net/11511/114818

Collections

Graduate School of Natural and Applied Sciences, Article