Journal of Safety Science and Resilience (Jun 2024)
An indicator model for assessing community resilience to the COVID-19 pandemic and its validation: A case study in Hong Kong
Abstract
The COVID-19 outbreak had a significant negative impact on the world, and the fifth wave of COVID-19 in Hong Kong brought a considerable shock to Chinese society. There is a growing call for more resilient cities. However, empirical evidence and validation of modeling studies of resilience indicators for urban community responses to the COVID-19 pandemic still need to be provided. In this study, a resilience assessment indicator model comprising 4 subsystems, 7 indicators, and 12 variables was developed to assess the resilience of Hong Kong communities in response to COVID-19 (i.e., Resilience Index). Furthermore, this study utilized regression models such as geographically weighted regression (GWR) and multiscale GWR (MGWR) to validate the resilience model proposed in this study at the model and variable levels. In the regression model, the Resilience Index and the individual variables in the resilience model are explanatory variables, and the outcomes of the COVID-19 pandemic (confirmed cases, confirmation rate, discharged cases, discharge rate) are dependent variables. The results showed that: (i) the resilience of Hong Kong communities to the COVID-19 pandemic was not strong in general and showed some clustered spatial distribution characteristics; (ii) the validation results at the model level showed that the Resilience Index did not explain the consequences of the COVID-19 pandemic to a high degree; (iii) the validation results at the variable level showed that the MGWR model was the best at identifying the relationships between explanatory variables and the dependent variable; and (iv) compared with the model-level assessment results, the variable-level assessment explained the consequences of the COVID-19 pandemic better than the model level assessment results. The above analysis and the spatial distribution maps of the resilience variables can provide empirically based and targeted insights for policymakers.
