Methods for Infectious Disease Risk Assessments in Megacities Using the Urban Resilience Theory

Hao Wang, Changhao Cao (), Xiaokang Ma and Yao Ma
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Hao Wang: Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100830, China
Changhao Cao: Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100830, China
Xiaokang Ma: Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100830, China
Yao Ma: Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100830, China

Sustainability, 2023, vol. 15, issue 23, 1-16

Abstract: Since the 20th century began, the world has witnessed the emergence of contagious diseases such as Severe Acute Respiratory Syndrome (SARS), H1N1 influenza, and the recent COVID-19 pandemic. Conducting timely infectious disease risk assessments is of significant importance for preventing the spread of viruses, safeguarding public health, and achieving sustainable development. Most current studies on epidemic risk assessments focus on administrative divisions, making it challenging to reflect the risk disparities within these areas. Taking Shanghai as an example, this research introduces the concept of urban resilience frameworks and identifies the risk factors. By analyzing the interactions among different risk factors using geographic detectors, this study establishes the distribution relationship between the risk factors and newly reported cases using Geographically Weighted Regression. A risk assessment model is constructed to evaluate the infection risk within different regions of the administrative area. The results demonstrate that the central area of Shanghai exhibits the highest infection risk, gradually decreasing toward the periphery. The Spearman’s correlation coefficient ( p ) between the predicted and actual distribution of new cases reaches 0.869 ( p < 0.001), and the coefficient of determination (R2) is 0.938 ( p < 0.001), indicating a relatively accurate assessment of infection risk in different spatial areas. This research methodology can be effectively applied to infectious disease risk assessments during public health emergencies, thereby assisting in the formulation of epidemic prevention policies.

Keywords: geographic big data; GWR; risk assessment; data-driven (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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