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A U-shaped relationship between influenza attack rate and urbanisation rate in urbanising mainland China: spatiotemporal analysis of the surveillance data from 2010 to 2017
Hao Lei;
Nan Zhang;
Beidi Niu;
Xiao Wang;
Shenglan Xiao;
Xiangjun Du;
Tao Chen;
Lei Yang;
Dayan Wang;
Benjamin J Cowling;
Yuguo Li;
Yuelong Shu
ABSTRACT
The whole world has been experiencing an unprecedented process of urbanisation, how the rapid urbanisation during the early or middle age affecting seasonal influenza transmission remains unknown. In this study, we performed spatiotemporal analyses of province-level influenza surveillance data collected in mainland China from 1 April 2010 to 31 March 2017. An agent-based model based on hourly human contact-related behaviours was also built to simulate influenza transmission dynamics and explore the potential mechanism. We observed persistent differences in epidemic attack rates between provinces across the 7-year study period, and the influenza epidemic attack rate in winter waves exhibited a U-shaped relationship with the urbanisation rates, with a turning point at 50–60% urbanisation across. Rapid Chinese urbanisation has led to increases in urban population density and percentage of the workforce, but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship. Our results highlight the complicated effects of urbanisation on seasonal influenza epidemics in China, a rapidly urbanising country. As the current urbanisation rate in China is approximately 59%, further urbanisation with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate.
Citation
Please cite as:
Lei H, Zhang N, Niu B, Wang X, Xiao S, Du X, Chen T, Yang L, Wang D, Cowling BJ, Li Y, Shu Y
Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017