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

doi:10.11853/j.issn.1003.8280.2022.03.001

Abstract

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

摘要： 登革热是蚊媒病毒性传染病，广泛分布于全球热带、亚热带、甚至暖温带的城市化和半城市化区域，对全球100多个国家造成人群健康威胁。全球气候变化、城镇化和人口增长为登革病毒的扩散创造了有利条件。目前，由于缺乏可广泛接种的疫苗，媒介伊蚊控制是预防控制登革热的主要措施，而准确、及时的登革热风险预测可为登革热精准防控和决策制定提供重要依据。近年来，地理空间大数据的发展促进不同时空尺度下登革热驱动因素的识别。人工智能算法的进步，尤其是多种深度学习网络的出现，为登革热的风险预测提供了新技术。该文综合考虑登革热多种类型的驱动因素及其作用机制、地理空间大数据与人工智能技术，阐述如何应用地理空间大数据识别登革热的城市土地利用、气候环境和人口流动3方面的驱动因素，阐述人工智能算法在登革热传播风险预测中的应用现状。并基于现状提出未来研究应该加强在不同时空尺度上构建时空一体的风险预测模型，提议从预测值与真实值的差异、疫情时空聚集格局和防疫实际需求等方面评估模型性能。

关键词: 登革热, 驱动因素, 风险预测, 地理空间大数据, 人工智能

CLC Number:

R373.3⁺3

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.

李之超, 董金玮, 刘起勇. 地理空间大数据与人工智能在城市登革热驱动因素识别与风险预测中的应用[J]. 中国媒介生物学及控制杂志, 2022, 33(3): 321-325.

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