Application of artificial intelligence in study of epidemiological patterns of vector-borne diseases
Issued Date
2026-03-25
Resource Type
ISSN
10077588
Scopus ID
2-s2.0-105037360817
Journal Title
Resources Science
Volume
48
Issue
3
Start Page
584
End Page
594
Rights Holder(s)
SCOPUS
Bibliographic Citation
Resources Science Vol.48 No.3 (2026) , 584-594
Suggested Citation
Ding F., Yang S., Li Z., Ma T., Wang Q., Zheng C., Jiang D. Application of artificial intelligence in study of epidemiological patterns of vector-borne diseases. Resources Science Vol.48 No.3 (2026) , 584-594. 594. doi:10.18402/resci.2026.03.06 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116566
Title
Application of artificial intelligence in study of epidemiological patterns of vector-borne diseases
Author's Affiliation
University of Chinese Academy of Sciences
Yale University
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
Chinese Center for Disease Control and Prevention
Nuffield Department of Medicine
Yale School of the Environment
Mahidol Oxford Tropical Medicine Research Unit
Yale University
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
Chinese Center for Disease Control and Prevention
Nuffield Department of Medicine
Yale School of the Environment
Mahidol Oxford Tropical Medicine Research Unit
Corresponding Author(s)
Other Contributor(s)
Abstract
With the intensification of climate change, emerging and re-emerging vector-borne diseases have become increasingly active worldwide, posing a continuous threat to human health. These diseases, mainly transmitted by arthropod vectors such as mosquitoes, ticks, and mites, have epidemiological processes that are generally influenced by natural environmental conditions, human factors, and biological factors, exhibiting pronounced regional distribution characteristics. In recent years, artificial intelligence (AI) technologies, driven by big data and algorithms, have developed rapidly, providing new opportunities for studying epidemiological patterns of vector-borne diseases. Based on a review of the spatiotemporal epidemiological characteristics of various common vector-borne diseases, this study reviews the development of AI-enabled research from three dimensions: data acquisition, analysis of influencing factors, and epidemic risk early warning, while also summarizing the challenges faced in this field. Finally, this study offers prospects for the application of AI in the study of epidemiological patterns of vector-borne diseases.