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Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets. / Gulyaeva, Marina; Huettmann, Falk; Shestopalov, Alexander et al.

In: Scientific Reports, Vol. 10, No. 1, 16817, 01.12.2020.

Research output: Contribution to journal › Article › peer-review

Harvard

Gulyaeva, M, Huettmann, F, Shestopalov, A, Okamatsu, M, Matsuno, K, Chu, DH, Sakoda, Y, Glushchenko, A, Milton, E & Bortz, E 2020, 'Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets', Scientific Reports, vol. 10, no. 1, 16817. https://doi.org/10.1038/s41598-020-73664-2

APA

Gulyaeva, M., Huettmann, F., Shestopalov, A., Okamatsu, M., Matsuno, K., Chu, D. H., Sakoda, Y., Glushchenko, A., Milton, E., & Bortz, E. (2020). Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets. Scientific Reports, 10(1), [16817]. https://doi.org/10.1038/s41598-020-73664-2

Vancouver

Gulyaeva M, Huettmann F, Shestopalov A, Okamatsu M, Matsuno K, Chu DH et al. Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets. Scientific Reports. 2020 Dec 1;10(1):16817. doi: 10.1038/s41598-020-73664-2

Author

Gulyaeva, Marina ; Huettmann, Falk ; Shestopalov, Alexander et al. / Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets. In: Scientific Reports. 2020 ; Vol. 10, No. 1.

BibTeX

@article{47bc3652cbac4cb2a68bf61c2b1e145a,
title = "Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets",
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.",
keywords = "WILD BIRDS, A VIRUSES, SURVEILLANCE, H5",
author = "Marina Gulyaeva and Falk Huettmann and Alexander Shestopalov and Masatoshi Okamatsu and Keita Matsuno and Chu, {Duc Huy} and Yoshihiro Sakoda and Alexandra Glushchenko and Elaina Milton and Eric Bortz",
note = "Publisher Copyright: {\textcopyright} 2020, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2020",
month = dec,
day = "1",
doi = "10.1038/s41598-020-73664-2",
language = "English",
volume = "10",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets

AU - Gulyaeva, Marina

AU - Huettmann, Falk

AU - Shestopalov, Alexander

AU - Okamatsu, Masatoshi

AU - Matsuno, Keita

AU - Chu, Duc Huy

AU - Sakoda, Yoshihiro

AU - Glushchenko, Alexandra

AU - Milton, Elaina

AU - Bortz, Eric

N1 - Publisher Copyright: © 2020, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/12/1

Y1 - 2020/12/1

N2 - 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.

AB - 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.

KW - WILD BIRDS

KW - A VIRUSES

KW - SURVEILLANCE

KW - H5

UR - http://www.scopus.com/inward/record.url?scp=85092314253&partnerID=8YFLogxK

U2 - 10.1038/s41598-020-73664-2

DO - 10.1038/s41598-020-73664-2

M3 - Article

C2 - 33033298

AN - SCOPUS:85092314253

VL - 10

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 16817

ER -

ID: 25686526