Abstract
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.
Highlights
Influenza A virus infections are a significant problem affecting the health of wild and domestic animals and public health[1]
The rapid and unpredictable evolution of Avian Influenza (AI) viruses leads to the emergence of new influenza virus strains and subtype combinations, which potentially point towards a global p andemic[3,4,8]
One of the fundamental unknowns in the field of influenza biology is a panoramic understanding of the role wild birds play in the global maintenance and spread of influenza A viruses
Summary
Influenza A virus infections are a significant problem affecting the health of wild and domestic animals and public health[1]. The majority of them is classified as lower pathogeny (low-path LP); those are still underestimated, insufficiently studied and little surveyed even It has been suggested, but poorly studied, that those AI strains co-occur and interact. To get closer to such type of questions, here we focus on the northern Pacific Rim, a region between North America and Asia, namely Alaska, Russia, Japan and Vietnam (Fig. 1; see[2,9] for an application) This region is known to be connected through various animal migration patterns ( birds2 and[10], marine mammals, mammals, fish and sea turtles), as well as climate regimes. Using the ‘best available’ scientific information on AI for those nations, we try to obtain alternatively validated AI samples to draw generalizable inferences explicit in space and time
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