Improving tornado casualty predictions in the US with population exposure data and a modified social vulnerability index

Bibliographic Data

ID22029348
AuthorsVikalp Mishra (0000-0003-1436-8044, University of Alabama in Huntsville), Eric R Anderson (0000-0001-5056-4347, Marshall Space Flight Center, corresponding author), Skyler Edwards (University of Alabama in Huntsville), Robert Griffin (0000-0001-5665-700X, University of Alabama in Huntsville)
Year2023
Volume87
Pages103588
Publication date2023-03-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Disaster Risk Reduction (JOURNAL)
Journal identifiersISSN: 2212-4209
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.ijdrr.2023.103588
OpenAlexW4319770568
LanguageEN
Citations received2
References cited59

Tornadoes are frequent and widespread events that often account for hundreds of injuries and fatalities, and millions of dollars in damages. Multiple studies have analyzed tornado climatology and associated exposure; however, fewer have focused on predicting fatalities and injuries by coupling geospatial data on tornado characteristics and underlying social and economic vulnerability. In this study, we test the ability of negative binomial regression models to predict tornado-induced injuries and fatalities by coupling data on physical characteristics of tornadoes, exposure of populations, and underlying social vulnerability. We also present a modified spatially weighted social vulnerability index ( S V I w t * ) . We used 10-year (2005–2014) tornado data over the continental United States for this analysis. The results of this study indicate that the tornado length, magnitude and nocturnality seem to be the major hazard-related indicators of fatalities (McFadden's Pseudo- R 2 ranged from 0.01 to 0.12). Population exposure and S V I w t * are positive and statistically significant in predicting tornado related fatalities and injuries (Pseudo R 2 of 0.11–0.18). Although combining S V I w t * with hazard variables does not substantially improve model fit when compared to adding population exposure, combining S V I w t * , hazard and exposure results in better predictions of both injuries and fatalities (Pseudo R 2 of 0.17) and is also an improvement on previous similar studies

Computer security · Environmental health · Forensic engineering · Geography · Meteorology · Poison control · Population · Psychological resilience · Social vulnerability · Statistics · Tornado · Climate Change and Health Impacts · Computer Science · Disaster Management and Resilience · Engineering · Environmental Science · Flood Risk Assessment and Management · Mathematics · Medicine · Psychology · Social Psychology