Quantifying predictors for the spatial diffusion of avian influenza virus in China

Abstract

BackgroundAvian influenza virus (AIV) causes both severe outbreaks and endemic disease among poultry and has caused sporadic human infections in Asia, furthermore the routes of transmission in avian species between geographic regions can be numerous and complex. Using nucleotide sequences from the internal protein coding segments of AIV, we performed a Bayesian phylogeographic study to uncover regional routes of transmission and factors predictive of the rate of viral diffusion within China.ResultsWe found that the Central area and Pan-Pearl River Delta were the two main sources of AIV diffusion, while the East Coast areas especially the Yangtze River delta, were the major targets of viral invasion. Next we investigated the extent to which economic, agricultural, environmental and climatic regional data was predictive of viral diffusion by fitting phylogeographic discrete trait models using generalised linear models.ConclusionsOur results highlighted that the economic-agricultural predictors, especially the poultry population density and the number of farm product markets, are the key determinants of spatial diffusion of AIV in China; high human density and freight transportation are also important predictors of high rates of viral transmission; Climate features (e.g. temperature) were correlated to the viral invasion in the destination to some degree; while little or no impacts were found from natural environment factors (such as surface water coverage). This study uncovers the risk factors and enhances our understanding of the spatial dynamics of AIV in bird populations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-016-0845-3) contains supplementary material, which is available to authorized users.

Agricultural and geographic factors shaped the North American 2015 highly pathogenic avian influenza H5N2 outbreak

“…In other words, counties with a larger proportion of surface water received virus more frequently compared to those with less surface water. Surface water resources have been associated with HPAI dispersal and prevalence in China and may signify movement of virus by migrating waterfowl that stopover in lakes, rivers and wetlands [40,51]. In our analysis, this variable was only weakly supported, had a relatively small effect size and was not supported once the model was adjusted for county group sample size.…”

“…The implementation of a GLM into a Bayesian discrete trait analysis has been previously applied to HPAI in China [40] and Egypt [41], providing evidence that environmental, agricultural and anthropogenic factors influence viral movement. Due to differences in social, governmental and agricultural systems, the generalization of these previous GLM results to other countries may not be appropriate.…”

Assessing the role of live poultry trade in community-structured transmission of avian influenza in China

“…Taking H5N1 as an example, a discrete phylogeographic analysis of HPAI H5N1 in Chinese poultry revealed many wellsupported viral lineage movements among provinces (as previously noted in ref. 16). In addition to this general pattern, we further observed that lineage movements occur between pairs of provinces that are adjacent or close ( Fig.…”

“…Based on the results of fully specified models, with and without distance, and sensitivity analysis (SI Appendix, Fig. S4), we recomputed these phylogeographic analyses using a much smaller set of predictors, specifically those that have been hypothesized by others to affect the large-scale dissemination of AIV (6,8,16,18): 1) the intensity of the poultry trade among locations in China, 2) the migration of wild birds among locations in China, and 3) distances along the road network in China (see Methods for details). Although minimum road distance is strongly inversely associated with the dissemination of AIV lineages across a range of model configurations ( Fig.…”

“…Given the detection of a continuous process of AIV geographic spread among poultry, our results indicate that at the national scale there are repeatable and potentially predictable patterns that can be used to shape future strategies for AIV control and prevention. diffusion of AIVs in endemic areas (16). However, it can be difficult to infer underlying transmission processes from such data due to the uneven sampling of virus genomes through time and space, and a reliance on passive surveillance means that many virus lineages may go unsampled.…”