An evaluative model for assessing pandemic resilience at the neighborhood level: The case of Tehran

Abstract

The spread of the COVID-19 virus, which has caused abundant mortalities in human settlements, has drawn the attention of urban planners and policy-makers to the necessity of improving resilience to future pandemics. In this study, a set of indicators related to pandemic resilience were identified and used to develop a composite multi-dimensional pandemic resilience index for Tehran's neighborhoods. The physical, infrastructural, socio-economic, and environmental dimensions of pandemic resilience were defined considering the conditions of 351 neighborhoods through the exploratory factor analysis method. Accordingly, the pandemic resilience (PR) score of the neighborhoods was calculated. Furthermore, the Pearson correlation analysis was used to validate the PR scores by examining the correlation between the neighborhood PR scores and the number of confirmed cases. For this purpose, we used a sample consisting of 43,000 confirmed COVID-19 patients in the first five months of its spread. The test shows a statistically significant negative correlation between neighborhoods’ resilience score and the cumulative number of confirmed patients in the neighborhoods (r= -.456, P<0.001). This study also tries to develop a new model to better understand health determinants of pandemic resilience. The proposed model can inform planners and policymakers to take appropriate measures to create more pandemic-resilient urban neighborhoods.

A neighborhood-level analysis of association between social vulnerability and COVID-19 in ahvaz, Iran

“…The findings of this study are consistent with the findings of [ 92 , 93 ]; and [ 94 ] in Iran and Tehran. They also found that neighborhoods with better economic and social conditions and more active economic and commercial sectors have a higher incidence.…”

Community resilience to pandemics: An assessment framework developed based on the review of COVID-19 literature

“…Besides, environmental features play an essential role in community mitigation and absorption capacity [ 59 , 70 ]. In addition, demographic resilience can influentially reduce community vulnerability to pandemics by mitigating the infection rate, minimizing death cases, and facilitating the process of recovery and inhibition of disease [ 36 , 71 ]. These dimensions are further discussed in the following sections.…”

“…While they have explored different capitals, their study falls short of exploring how each capital and capacity factors could contribute to community pandemic resilience. To the same, Lak et al [ 36 ] have evaluated the pandemic resilience of the Tehran neighborhoods considering physical, demographic, environmental, infrastructural, economic, and social dimensions. This study did not include the institutional aspect of community pandemic resilience.…”

“…This study did not include the institutional aspect of community pandemic resilience. Besides, it is needed to further explore how each evaluation factor affects the community resilience to pandemics [ 36 ].…”

Predicting onset risk of COVID-19 symptom to support healthy travel route planning in the new normal of long-term coexistence with SARS-CoV-2

“…In order to assist people in healthy travel planning, it is necessary to be able to predict and identify the spatiotemporal risk hotspots of COVID-19 ( Ghosh and Cartone, 2020 ; Sharifi and Khavarian-Garmsir, 2020 ). Since the outbreak of COVID-19, a series of studies on predicting and identifying risk hotspots have emerged ( Das et al, 2021 ; Guo et al, 2021 ; Islam et al, 2021 ; Lak et al, 2021 ; Sannigrahi et al, 2020 ). For example, Jia et al used a spatiotemporal risk source model to identify the areas within high transmission risk and statistically derive the geographical spread and growth pattern of COVID-19 in China ( Jia et al, 2020 ).…”