Influenza A H5N1 and H7N9 in China: A spatial risk analysis

Abstract

BackgroundZoonotic avian influenza poses a major risk to China, and other parts of the world. H5N1 has remained endemic in China and globally for nearly two decades, and in 2013, a novel zoonotic influenza A subtype H7N9 emerged in China. This study aimed to improve upon our current understanding of the spreading mechanisms of H7N9 and H5N1 by generating spatial risk profiles for each of the two virus subtypes across mainland China.Methods and findingsIn this study, we (i) developed a refined data set of H5N1 and H7N9 locations with consideration of animal/animal environment case data, as well as spatial accuracy and precision; (ii) used this data set along with environmental variables to build species distribution models (SDMs) for each virus subtype in high resolution spatial units of 1km2 cells using Maxent; (iii) developed a risk modelling framework which integrated the results from the SDMs with human and chicken population variables, which was done to quantify the risk of zoonotic transmission; and (iv) identified areas at high risk of H5N1 and H7N9 transmission. We produced high performing SDMs (6 of 8 models with AUC > 0.9) for both H5N1 and H7N9. In all our SDMs, H7N9 consistently showed higher AUC results compared to H5N1, suggesting H7N9 suitability could be better explained by environmental variables. For both subtypes, high risk areas were primarily located in south-eastern China, with H5N1 distributions found to be more diffuse and extending more inland compared to H7N9.ConclusionsWe provide projections of our risk models to public health policy makers so that specific high risk areas can be targeted for control measures. We recommend comparing H5N1 and H7N9 prevalence rates and survivability in the natural environment to better understand the role of animal and environmental transmission in human infections.

Risk factors associated with highly pathogenic avian influenza subtype H5N8 outbreaks on broiler duck farms in South Korea

“…To construct strategies to control diseases, the identification of timely risk factors in outbreaks is important (Lewallen & Courtright, 1998). Previous avian influenza epidemiologic studies have identified several risk factors from a variety of settings and countries (Abbas, Wilking, Horeth-Bontgen, & Conraths, 2012;Arriola et al, 2015;Beaudoin et al, 2014;Biswas et al, 2011;Boender et al, 2007;Bui, Gardner, MacIntyre, & Sarkar, 2017;Cao et al, 2010;Chaudhry, Rashid, Thrusfield, Welburn, & Bronsvoort, 2015;Desvaux et al, 2011;Dinh et al, 2006;Fang et al, 2008;Fasina, Rivas, Bisschop, Stegeman, & Hernandez, 2011;Gale et al, 2014;Garber et al, 2016;Gilbert et al, 2006;Henning et al, 2009;Huang, Zeng, & Wang, 2016;Iglesias, Jesus Munoz, Martinez, & de la Torre, 2010;Kung et al, 2007;Liu et al, 2015;Lohiniva et al, 2013;Loth, Gilbert, Osmani, Kalam, & Xiao, 2010;Mannelli, Ferre, & Marangon, 2006;Martin et al, 2011;McQuiston et al, 2005;Metras et al, 2013;Mounts et al, 1999;Musa, Abdu, Sackey, & Oladele, 2013;Nishiguchi et al, 2007;Osmani et al, 2014;Paul et al, 2011;Tenzin, Wangdi, & Rai, 2017;Thomas et al, 2005;Thompson, Sinclair, & Ganzevoort, 2008;…”

The temporal distribution of new H7N9 avian influenza infections based on laboratory-confirmed cases in Mainland China, 2013–2017

“… 3 , and Bui et al . 18 provided detailed descriptions on the temporal, spatial, and population distributions of the epidemics. These studies laid the foundation for investigating the epidemiological characteristics of the H7N9 epidemics.…”

Spatial and Temporal Epidemiologic features analysis of pulmonary tuberculosis in Nanjing, China

“…Instead of predicting whether it exists or not, Maxent estimates the relative environmental adaptability of species existence [14].…”