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Scientific reports2022; 12(1); 3910; doi: 10.1038/s41598-022-07512-w

Predicting the possibility of African horse sickness (AHS) introduction into China using spatial risk analysis and habitat connectivity of Culicoides.

Abstract: African horse sickness (AHS) is a devastating equine infectious disease. On 17 March 2020, it first appeared in Thailand and threatened all the South-East Asia equine industry security. Therefore, it is imperative to carry out risk warnings of the AHS in China. The maximum entropy algorithm was used to model AHS and Culicoides separately by using climate and non-climate variables. The least cost path (LCP) method was used to analyze the habitat connectivity of Culicoides with the reclassified land cover and altitude as cost factors. The models showed the mean area under the curve as 0.918 and 0.964 for AHS and Culicoides. The prediction result map shows that there is a high risk area in the southern part of China while the habitats of the Culicoides are connected to each other. Therefore, the risk of introducing AHS into China is high and control of the border area should be strengthened immediately.
© 2022. The Author(s).
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Publication Date: 2022-03-10 PubMed ID: 35273211PubMed Central: PMC8913660DOI: 10.1038/s41598-022-07512-wGoogle Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research forecasted the potential risk of the introduction of African Horse Sickness (AHS) into China through spatial risk analysis and examined the habitat connectivity of Culicoides, the insect that serves as a vector for the disease. Predictions indicate a high risk of AHS entry, particularly in southern China.

Methodology and Findings

  • The study utilized climate and non-climate variables to model AHS and Culicoides independently using the maximum entropy algorithm, an approach that provides a sophisticated likelihood measure of the distribution of a given event.
  • Meanwhile, the researchers employed the least-cost path (LCP) method to assess the habitat connectivity of Culicoides. This method considered the reclassified land cover and altitude as cost factors influencing their distribution.
  • The models demonstrated high accuracy, with the area under the curve—an indicator of model performance—being 0.918 for AHS and 0.964 for Culicoides. These high values imply that the models were successful in predicting the risks and distribution patterns of both AHS and Culicoides.
  • The prediction result map indicated a high-risk area in southern China, where the habitats of Culicoides are interconnected. This suggests easy transmission pathways for the disease within this region.

Implications and Recommendations

  • These findings reveal a considerable potential for AHS to enter China, primarily through its southern region. This result is a significant cause for concern for China’s equine industry and necessitates immediate preventative measures.
  • The study emphasizes that controlling the border areas, reducing the habitat connectivity of Culicoides—the carrier insect—and implementing strict environmental, climate, and parasitic controls will be vital in preventing the introduction and potential epidemic of AHS. Furthermore, targeted vaccinations in the high-risk areas should be considered as a primary element of control strategies.
  • This investigation also underscores the utility of spatial risk analysis and disease modeling in detecting disease introduction risks. The techniques used here can be applied to other species and diseases for early warning and preventative strategies.

Cite This Article

APA
Gao S, Zeng Z, Wang H, Chen F, Huang L, Wang X. (2022). Predicting the possibility of African horse sickness (AHS) introduction into China using spatial risk analysis and habitat connectivity of Culicoides. Sci Rep, 12(1), 3910. https://doi.org/10.1038/s41598-022-07512-w

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 12
Issue: 1
Pages: 3910

Researcher Affiliations

Gao, Shan
  • College of Wildlife & Protected Area, Ministry of Education, Northeast Forestry University, 26 Hexing Road, Xiangfang District, Harbin, 150040, Heilongjiang Province, People's Republic of China.
  • Key Laboratory of Wildlife Diseases and Biosecurity Management of Heilongjiang Province, 26 Hexing Road, Xiangfang District, Harbin, 150040, Heilongjiang Province, People's Republic of China.
Zeng, Zan
  • Department of Vascular Surgery, Changhai Hospital, Second Military Medical University, Shanghai, 200433, People's Republic of China.
Wang, HaoNing
  • School of Geography and Tourism, Harbin University, Harbin, 150086, Heilongjiang Province, People's Republic of China.
Chen, FangYuan
  • The Second Geomatics Cartography Institute, Ministry of Natural Resource, Harbin, 150086, Heilongjiang Province, People's Republic of China.
Huang, LiYa
  • Changbai Mountain Academy of Sciences, Antu, 133613, Jilin Province, People's Republic of China.
Wang, XiaoLong
  • College of Wildlife & Protected Area, Ministry of Education, Northeast Forestry University, 26 Hexing Road, Xiangfang District, Harbin, 150040, Heilongjiang Province, People's Republic of China. wxlhrb123@outlook.com.
  • Key Laboratory of Wildlife Diseases and Biosecurity Management of Heilongjiang Province, 26 Hexing Road, Xiangfang District, Harbin, 150040, Heilongjiang Province, People's Republic of China. wxlhrb123@outlook.com.

MeSH Terms

  • African Horse Sickness / epidemiology
  • African Horse Sickness Virus
  • Animals
  • Ceratopogonidae
  • China / epidemiology
  • Ecosystem
  • Horses
  • Insect Vectors
  • Risk Assessment

Conflict of Interest Statement

The authors declare no competing interests.

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This article has been cited 12 times.
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