Application of geospatial big data and artificial intelligence in driving factor identification and risk prediction for urban dengue fever

LI Zhi-chao, DONG Jin-wei, LIU Qi-yong

doi:10.11853/j.issn.1003.8280.2022.03.001

Chinese Journal of Vector Biology and Control >

2022 , Vol. 33 >Issue 3: 321 - 325

DOI: https://doi.org/10.11853/j.issn.1003.8280.2022.03.001

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Application of geospatial big data and artificial intelligence in driving factor identification and risk prediction for urban dengue fever

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1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
2. State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China

Received date: 2022-05-13

Online published: 2022-06-11

Supported by

Strategic Priority Research Program of the Chinese Academy of Sciences(A) (No. XDA19040301)

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Abstract

Dengue fever is a mosquito-borne viral infectious disease that is widely distributed in urban or peri-urban areas in the tropical, subtropical, and warm temperate zones worldwide and threatens the health of populations in more than 100 countries and regions. Global climate change, urbanization, and urban population growth have created favorable conditions for the spread of dengue fever virus. At present, due to a lack of vaccines applicable for mass vaccination, Aedes vector control is the main measure for the prevention and control of dengue fever, and accurate and timely risk prediction for dengue fever can provide an important basis for precise prevention and control, and decision-making. In recent years, the development of geospatial big data promotes the identification of the driving factors for dengue fever at different spatial and temporal scales, and the advances in artificial intelligence, especially the emergence of various deep learning networks, provide new techniques for the risk prediction of dengue fever. Through a comprehensive analysis of the various types of driving factors for dengue fever and their mechanism of action, geospatial big data, and artificial intelligence techniques, this article elaborates on the application of geospatial big data in identifying the driving factors for dengue fever from the aspects of urban land use, climate and environment conditions, and population movement, as well as the current status of the application of artificial intelligence algorithms in predicting the risk of dengue fever transmission. Based on the current research status of geospatial big data and artificial intelligence, it is proposed that future research should develop spatiotemporal risk predictive models at different spatial and temporal scales and the performance of such models should be evaluated in terms of the difference between predicted and true values, the spatiotemporal aggregation patterns of dengue fever, and the actual needs of dengue fever prevention and control.

Key words： Dengue fever; Driving factor; Risk prediction; Geospatial big data; Artificial intelligence

Cite this article

LI Zhi-chao, DONG Jin-wei, LIU Qi-yong . Application of geospatial big data and artificial intelligence in driving factor identification and risk prediction for urban dengue fever[J]. Chinese Journal of Vector Biology and Control, 2022 , 33(3) : 321 -325 . DOI: 10.11853/j.issn.1003.8280.2022.03.001

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