The risk of H7N9 infection is mapped in Asia
Since 2013, two epidemic waves of avian influenza H7N9 human infections have affected China, with over 400 human cases reported to date, a case fatality rate of approximately 30%, and no human to human transmission established to date. The large majority of human cases have been linked to exposure in live-bird markets, where people sell and buy poultry. But what makes some of those markets more at risk than others? And where are the areas where this disease could potentially spread elsewhere in Asia?
This is what Marius Gilbert, Research Associate of the FNRS - Laboratory of Biological Control and Spatial Ecology (LUBIES), Interfaculty School of Bioengineering (EIB , Université libre de Bruxelles) and colleagues from the University of Oxford, the International Livestock Research Institute and the China Center for Disease Control have been trying to understand and predict. They publish this week in Nature Communications an Asia wide risk map of the areas that would be suitable for markets infection by the H7N9 influenza virus.
Based on a retrospective study of the spatial distribution of markets infected by H7N9 in 2013 and 2014, they developed a spatial model that can accurately predict the risk of market H7N9 infection in China, and can be extrapolated to Asia. Local live-poultry market density is found to be the most important predictor variable of H7N9 infection risk at the market level. Other predictor variables of H7N9 infection risk include the population densities of chickens reared in extensive and intensive systems, water bodies, accessibility to major cities, human and domestic duck population density and rice land cover. The areas predicted to be most suitable for new H7N9 market infection include specific urban areas of China where the disease has not yet occurred, an extensive area in Bengal, the river deltas of Vietnam, and parts of Indonesia and Philippines. These maps aim to facilitate evidence-based prioritization of surveillance in the region. They should help the early detection of new incursions, early response and active containment, minimizing impacts to agricultural livelihoods and reducing risk to human health.