Compound hazards: An examination of how hurricane protective actions could increase transmission risk of COVID-19

Dargin, Jennifer; Li, Qingchun; Jawer, Gabrielle; Xiao, Xiangning; Mostafavi, Ali

doi:10.1016/j.ijdrr.2021.102560

Cited by 21 publications

(6 citation statements)

References 41 publications

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“…The effectiveness of people's preparedness actions is directly linked their ability to stay shelter-in-place and cope with the impacts of hazards (Bullock et at., 2017;Lindell et al, 2006;Bronfman et al, 2019). As part of preparedness action, people would visit critical facilities to purchase items necessary items for hazard preparation, including visits to grocery stores, gas stations, and other points of interest (POIs) (Dargin et al, 2021). Accordingly, we included a preparedness feature as one of the protective action features in our study.…”

Section: Coupled Human-infrastructure Systems Performance Featuresmentioning

confidence: 99%

Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human- infrastructure Systems Performance

Li,

Mostafavi

2024

Preprint

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While current approaches primarily focus on anticipatory or predictive resilience assessments of natural events, there is a limitation in the literature of data-driven analyses for the ex-post evaluation of community risk and resilience, particularly using features related to the performance of coupled human-infrastructure systems. To address this gap, in this study we created a machine learning-based method for the ex-post assessment of community risk and resilience and their interplay based on features related to the coupled human-infrastructure systems performance. Utilizing feature groups related to population protective actions, infrastructure/building performance features, and recovery features, we examined the risk and resilience performance of communities in the context of the 2017 Hurricane Harvey in Harris County, Texas. These features related to the coupled human-infrastructure systems performance were processed using the K-means clustering method to classify census block groups into four distinct clusters then, based on feature analysis, these clusters were labeled and designated into four quadrants of risk-resilience archetypes. Finally, we analyzed the disparities in risk-resilience status of spatial areas across different clusters as well as different income groups. The findings unveil the risk-resilience status of spatial areas shaped by their coupled human-infrastructure systems performance and their interactions. The results also inform about features that contribute to high resilience in high-risk areas. For example, the results indicate that in high-risk areas, evacuation rates contributed to a greater resilience, while in low-risk areas, preparedness contributed to greater resilience. In addition, the findings reveal disparities in the risk and resilience status of spatial areas where low-income residents reside. The outcomes of this study provide researchers and practitioners with new data-driven and machine intelligence-based methods and insights to better evaluate the risk and resilience status of communities during a disaster to inform future plans and policies.

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Section: Coupled Human-infrastructure Systems Performance Featuresmentioning

confidence: 99%

Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human- infrastructure Systems Performance

Li,

Mostafavi

2024

Preprint

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“…In addition to disease-related features and socio-demographics, pandemic spread is influenced by population activities and social interactions. Several features, including social distancing [20][21][22], visits to points of interest [23][24][25], trip distance [26], and interpersonal contact density [27,28] have been shown to have positive relationships with the COVID-19 spread risk.…”

Section: Features Influencing the Spread Of Covid-19mentioning

confidence: 99%

Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes

et al. 2023

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The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their specific pandemic spread trajectories. To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features related to population activities, human mobility, socio-demographic features, disease attribute, and social interaction to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories. We collected and computed location intelligence features from 2,787 counties from March 3 to June 29, 2020 (initial wave). Second, we constructed a human visitation network, which incorporated county features as node attributes, and visits between counties as network edges. Our attributed network embeddings approach integrates both typological characteristics of the cross-county visitation network, as well as heterogeneous features. We conducted clustering analysis on the attributed network embeddings to reveal four archetypes of spread risk trajectories corresponding to four clusters of counties. Subsequently, we identified four features—population density, GDP, minority status, and POI visits—as important features underlying the distinctive transmission risk patterns among the archetypes. The attributed network embedding approach and the findings identify and explain the non-homogenous pandemic risk trajectories across counties for predictive pandemic monitoring. The study also contributes to data-driven and deep learning-based approaches for pandemic analytics to complement the standard epidemiological models for policy analysis in pandemics.

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“…We assume that preparedness for Hurricane Ida will lead to significant increases in POI visits (Dargin et al 2021). Therefore, we use the seven-day-rolling average POI visits from August 1 to 14 August 2021, which is two weeks before the week of Hurricane Ida as a baseline to reflect the normal situation of visiting POIs.…”

Section: Investigate Preparedness and Related Pois In Three Statesmentioning

confidence: 99%

Exploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint data

Li,

Ramaswami,

Lin

2023

Environ. Res. Lett.

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Preparedness for adverse events is critical to building urban resilience to climate-related risks. While most extant studies investigate preparedness patterns based on survey data, this study explores the potential of big digital footprint data (i.e. population visits to points of interest (POI)) to investigate preparedness patterns in the real case of Hurricane Ida (2021). We further investigate income and racial inequality in preparedness by combining the digital footprint data with demographic and socioeconomic data. A clear pattern of preparedness was seen in Louisiana with aggregated visits to grocery stores, gasoline stations, and construction supply dealers increasing by nearly 9%, 12%, and 10% respectively, representing three types of preparedness: survival, mobility planning, and hazard mitigation. Preparedness for Hurricane Ida was not seen in New York and New Jersey states. Inequality analyses for Louisiana across census block groups (CBGs) demonstrate that CBGs with higher income have more (nearly 8% greater) preparedness in visiting gasoline stations, while CBGs with a larger percentage of the white population have more preparedness in visiting grocery stores (nearly 12% more) in the lowest income groups. The results indicate that income and racial inequality differ across different preparedness in terms of visiting different POIs.

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Compound hazards: An examination of how hurricane protective actions could increase transmission risk of COVID-19

Cited by 21 publications

References 41 publications

Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human- infrastructure Systems Performance

Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human- infrastructure Systems Performance

Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes

Exploring income and racial inequality in preparedness for Hurricane Ida (2021): insights from digital footprint data

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