An AHP-based regional COVID-19 vulnerability model and its application in China

Abstract

Since the COVID-19 outbreak, four cities-Wuhan, Beijing, Urumqi and Dalian-have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed.

Supplementary information: The online version contains supplementary material available at 10.1007/s40808-021-01244-y.

Keywords: AHP; COVID-19; Correlation coefficient; Regional disease vulnerability index.

Fig. 1

The four study regions shown in black

Fig. 2

The process of the study

Fig. 3

Hierarchical model diagram of the factors influencing the disease vulnerability index

Fig. 4

Weight of the indicator layer to the target layer (computed based on the matrix of Table 5)

Fig. 5

Predictive vulnerable index of each provincial administrative region in China

Fig. 6

The predictive vulnerable index of each city in Zhejiang