Impacts of Road Traffic Network and Socioeconomic Factors on the Diffusion of 2009 Pandemic Influenza A (H1N1) in Mainland China.

Bo Xu,Bing Xu,Huaiyu Tian,Clive Sabel

doi:10.3390/ijerph16071223

Abstract

The 2009 pandemic influenza virus caused the majority of the influenza A virus infections in China in 2009. It arrived in several Chinese cities from imported cases and then spread as people travelled domestically by all means of transportation, among which road traffic was the most commonly used for daily commuting. Spatial variation in socioeconomic status not only accelerates migration across regions but also partly induces the differences in epidemic processes and in responses to epidemics across regions. However, the roles of both road travel and socioeconomic factors have not received the attention they deserve. Here, we constructed a national highway network for and between 333 cities in mainland China and extracted epidemiological variables and socioeconomic factors for each city. We calculated classic centrality measures for each city in the network and proposed two new measures (SumRatio and Multicenter Distance). We evaluated the correlation between the centrality measures and epidemiological features and conducted a spatial autoregression to quantify the impacts of road network and socioeconomic factors during the outbreak. The results showed that epidemics had more significant relationships with both our new measures than the classic ones. Higher population density, higher per person income, larger SumRatio and Multicenter Distance, more hospitals and college students, and lower per person GDP were associated with higher cumulative incidence. Higher population density and number of slaughtered pigs were found to advance epidemic arrival time. Higher population density, more colleges and slaughtered pigs, and lower Multicenter Distance were associated with longer epidemic duration. In conclusion, road transport and socioeconomic status had significant impacts and should be considered for the prevention and control of future pandemics.

Highlights

The 2009 Influenza A (H1N1) pandemic posed one of the most serious global public health challenges in recent years [1]
MATLAB [23] and ArcGIS Desktop [24] were used to extract information concerning the cumulative incidence which is the cumulative number of daily reported cases during the above time period divided by the population at risk, the onset week which is defined here as the week when the first case in a city was reported, and the duration which is the time from onset to the first epidemic peak, all in a certain prefecture-level city
We assumed that the highway passenger capacity within a city accounted for 90% of the total highway passenger volume and that the outflow highway passenger capacity accounted for 5%. We propose another new node centrality measure called Multicenter Distance, which refers to the shortest distance along national highways from a certain city to one of the eight center cities (Chengdu, Jinan, Beijing, Guangzhou, Shanghai, Fuzhou, Wenzhou, and Changsha city) where the epidemics arrived the earliest

Summary

Introduction

The 2009 Influenza A (H1N1) pandemic posed one of the most serious global public health challenges in recent years [1]. With the ease and speed of global travel in the 21st century, the world is a global village in terms of epidemic transmission [2]. The Influenza A (H1N1) virus can transmit from humans to humans by direct body contact or respiratory droplets. The infectious disease can spread widely as people carrying pathogens travel and commute between cities by multiple means of transport. Intercity travel is important for the diffusion of viruses [3,4,5]. Res. Public Health 2019, 16, 1223; doi:10.3390/ijerph16071223 www.mdpi.com/journal/ijerph

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