Climate change suggests a shift of H5N1 risk in migratory birds

Tian, Huaiyu; Zhou, Sen; Dong, Lü; Boeckel, Thomas P. Van; Yao, Panpan; Wu, Qizhong; Yuan, Wenping; Guo, Yan; Huang, Shanqian; Chen, Wenhuan; Lu, Xueliang; Liu, Zhen; Bai, Yuqi; Yue, Tianxiang; Grenfell, Bryan T.; Xu, Bing

doi:10.1016/j.ecolmodel.2014.08.005

Cited by 24 publications

(16 citation statements)

References 61 publications

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“…Maps representing the results from this risk analysis are presented in 36.5% (19/52) of points fell in the medium-high risk category and 25.0% (13/52) of points fell in the high risk category. For exact H7N9 cases, 8.82% (6/68) of points fell in the low risk category, 13.2% (9/68) of points fell in the low-medium risk category, 44.1% (30/68) of points fell in the medium-high risk category and 33.8% (23/68) of points fell in the high risk category.…”

Section: Risk Analysismentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Risk Analysismentioning

confidence: 99%

“…approximately 1km 2 ) resolutions [25,29]. Most SDM studies which built H5N1 models for China [18,[36][37][38][39] used coordinate data from the World Organization for Animal Health (OIE), for which the accuracy of the case locations are not described [40]. Only Martin et al[37] took accuracy of coordinate data into account.…”

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confidence: 99%

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

et al. 2017

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“…Due to the passive surveillance system used, outbreaks might be underreported (Tian, Zhou, Dong, Van Boeckel, Pei, et al, 2015). We included records from both sources and removed any duplicated reports.…”

Section: Data Collectionmentioning

confidence: 99%

Contrasting effects of host species and phylogenetic diversity on the occurrence of HPAI H5N1 in European wild birds

Huang

Langevelde

et al. 2019

Journal of Animal Ecology

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Studies on the highly pathogenic avian influenza (HPAI) H5N1 suggest that wild bird migration may facilitate its long‐distance spread, yet the role of wild bird community composition in its transmission risk remains poorly understood. Furthermore, most studies on the diversity–disease relationship focused on host species diversity without considering hosts’ phylogenetic relationships, which may lead to rejecting a species diversity effect when the community has host species that are only distantly related. Here, we explored the influence of waterbird community composition for determining HPAI H5N1 occurrence in wild birds in a continental‐scale study across Europe. In particular, we tested the diversity–disease relationship using both host species diversity and host phylogenetic diversity. Our results provide the first demonstration that host community composition—compared with previously identified environmental risk factors—can also effectively explain the spatial pattern of H5N1 occurrence in wild birds. We further show that communities with more higher risk host species and more closely related species have a higher risk of H5N1 outbreaks. Thus, both host species diversity and community phylogenetic structure, in addition to environmental factors, jointly influence H5N1 occurrence. Our work not only extends the current theory on the diversity–disease relationship, but also has important implications for future monitoring of H5N1 and other HPAI subtypes.

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“…Studies have shown that environmental factors such as geography, climate, and weather have a significant influence on the geographic distribution of animal viruses. For example, adenovirus (Fagbo et al, 2016), rotavirus (Das et al, 2017), respiratory syncytial virus (Nenna et al, 2017), hantavirus (Prist, Uriarte, Fernandes, & Metzger, 2017;Tian et al, 2017), and avian influenza virus (Tian et al, 2015) are closely related to temperature, precipitation, and humidity, which may vary locally and seasonally (Lujan, Greenberg, Hung, Dimenna, & Hofkin, 2014). Indeed, CPV-2 shows local and seasonal characteristics (Schoeman, Goddard, & Leisewitz, 2013;Zhao et al, 2016).…”

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confidence: 99%

Bioclimatic and altitudinal variables influence the potential distribution of canine parvovirus type 2 worldwide

Jiang

2018

Ecology and Evolution

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Canine parvovirus type 2 (CPV‐2) is extremely contagious and causes high rate of morbidity to many wild carnivores. It has three variants (CPV‐2a, CPV‐2b, and CPV‐2c) that are distributed worldwide with different frequencies and levels of genetic and antigenic variability. The disease poses a threat to the healthy survival and reproduction of wildlife. The research on the relationship between CPV‐2 epidemic and environmental variables is lacking. To fill this research gap, we used maximum entropy (MaxEnt) approach with principal component analysis (PCA) to evaluate the relation between CPV‐2 and environmental variables and to create a world risk map for this disease. According to the PCA results, 18 environmental variables were selected from 68 variables for subsequent analyses. MaxEnt showed that annual mean temperature, isothermality, altitude, November precipitation, maximum temperature of warmest month, and precipitation of warmest quarter were the six most important variables associated with CPV‐2 distribution, with a total of 77.7% percent contribution. The risk of this disease between 18°N and 47°N was high, especially in the east of China and the United States. These results support further prediction of risk factors for this virus to help secure the health and sustainable survival of wild carnivores.

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Climate change suggests a shift of H5N1 risk in migratory birds

Cited by 24 publications

References 61 publications

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

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

Contrasting effects of host species and phylogenetic diversity on the occurrence of HPAI H5N1 in European wild birds

Bioclimatic and altitudinal variables influence the potential distribution of canine parvovirus type 2 worldwide

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