Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets.
Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets.
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DOI：
10.1038/s41598-020-73664-2
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发表时间：
2020-10-08
影响因子：
4.6
通讯作者：
Bortz E
中科院分区：
文献类型：
--
作者：
Gulyaeva M;Huettmann F;Shestopalov A;Okamatsu M;Matsuno K;Chu DH;Sakoda Y;Glushchenko A;Milton E;Bortz E
禽流感(AI)是一种复杂但仍鲜为人知的疾病；特别是在涉及到水库、联合感染、连通性和更广泛的景观前景时。与高致病性禽流感病毒(HPAIV)相比，由毒力较低的禽流感病毒株(AIV)引起的鸡的低致病性(低路径LP)禽流感甚至还没有得到很好的描述，也不知道它们如何导致更广泛的禽流感和免疫系统问题。LPAIV和HPAIV的共循环表明它们在生态方面存在相互作用。在这里，我们为环太平洋地区展示了一种国际方法，如何通过机器学习和开放访问数据集和地理信息系统(GIS)在5公里像素大小上对LP AI及其生态位进行数据挖掘和建模预测，以获得最佳可能的推断。这是基于关于这一问题的最佳可用数据(~ 40,827条来自日本、俄罗斯、越南、蒙古、阿拉斯加和流感研究数据库和美国农业部数据库的实验室分析现场数据，以及19个地理信息系统数据层)。我们抽样了157个宿主和110个低路径AIV，其中32个物种为驱动因素。低途径AIV亚型的流行主要是番鸭、绿头鸭、呼啸天鹅和海鸥，还强调了人类主导的野生动物接触区的工业影响。这项调查为研究水库、大数据挖掘、高致病性禽流感和其他流行病的预测和随后的爆发开创了良好的先例。
Avian Influenza (AI) is a complex but still poorly understood disease; specifically when it comes to reservoirs, co-infections, connectedness and wider landscape perspectives. Low pathogenic (Low-path LP) AI in chickens caused by less virulent strains of AI viruses (AIVs)—when compared with highly pathogenic AIVs (HPAIVs)—are not even well-described yet or known how they contribute to wider AI and immune system issues. Co-circulation of LPAIVs with HPAIVs suggests their interactions in their ecological aspects. Here we show for the Pacific Rim an international approach how to data mine and model-predict LP AI and its ecological niche with machine learning and open access data sets and geographic information systems (GIS) on a 5 km pixel size for best-possible inference. This is based on the best-available data on the issue (~ 40,827 records of lab-analyzed field data from Japan, Russia, Vietnam, Mongolia, Alaska and Influenza Research Database (IRD) and U.S. Department of Agriculture (USDA) database sets, as well as 19 GIS data layers). We sampled 157 hosts and 110 low-path AIVs with 32 species as drivers. The prevalence across low-path AIV subtypes is dominated by Muscovy ducks, Mallards, Whistling Swans and gulls also emphasizing industrial impacts for the human-dominated wildlife contact zone. This investigation sets a good precedent for the study of reservoirs, big data mining, predictions and subsequent outbreaks of HPAI and other pandemics.
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从西伯利亚西部的麝香鼠中分离出的H2N2流感病毒的遗传表征。
DOI：
10.1292/jvms.17-0048
发表时间：
2017-08-18
期刊：
The Journal of veterinary medical science
影响因子：
--
作者：
Gulyaeva M;Sharshov K;Suzuki M;Sobolev I;Sakoda Y;Alekseev A;Sivay M;Shestopalova L;Shchelkanov M;Shestopalov A
通讯作者：
Shestopalov A
影响因子：
4.8
作者：
Elith, J.;Leathwick, J. R.;Hastie, T.
通讯作者：
Hastie, T.
影响因子：
5.7
作者：
Breiman, L
通讯作者：
Breiman, L
影响因子：
4.5
作者：
Friedman, JH
通讯作者：
Friedman, JH
影响因子：
3.9
作者：
Jiao, Shengwu;Huettmann, Falk;Ouyang, Yanlan
通讯作者：
Ouyang, Yanlan