Fine-scale dengue transmission risk prediction based on multi-source geographic data in Guangzhou,China

ZHANG Meng-zhen, REN Zhou-peng, FAN Jun-fu, XIAO Jian-peng, ZHANG Ying-tao

doi:10.11853/j.issn.1003.8280.2023.05.013

Chinese Journal of Vector Biology and Control >

2023 , Vol. 34 >Issue 5: 654 - 663

DOI: https://doi.org/10.11853/j.issn.1003.8280.2023.05.013

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Fine-scale dengue transmission risk prediction based on multi-source geographic data in Guangzhou,China

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1. School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo, Shandong 255000, China;
2. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China;
3. Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong 511430, China;
4. Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong 511430, China

Received date: 2023-04-06

Online published: 2023-10-27

Supported by

National Natural Science Foundation of China (No. 42071377,42171413)

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Abstract

Objective This study predicted the risk of dengue fever transmission on a fine scale, aiming to meet the practical needs of meticulous prevention and control and to provide a reference for relevant departments to formulate more precise response plans against dengue fever.Methods A Random Forest model was constructed to predict the risk of dengue fever transmission at a fine resolution of 1 km×1 km based on the data on dengue fever cases as well as natural and socio-economic factors including precipitation, land surface temperature, population density, road density, the normalized difference vegetation index, hospital accessibility, bus stop density,and the Shannon evenness index of land use in Guangzhou, China from 2017 to 2019.Results Compared with the models based on undersampling or combined sampling, the oversampling-based prediction model had better precision, with the area under the curve (AUC) being 0.999, accuracy being 0.978, precision being 0.999, recall being 0.959, and F1 value being 0.979. The analysis of the importance of single factors in dengue fever prediction revealed that the importance of population density was much higher than those of the other variables, with an average increase in mean squared error of 63.76; hospital accessibility was the second important feature;the average land surface temperature had the lowest importance among the selected variables, with an average increase in mean squared error of 35.42. The distribution of dengue fever transmission risk was consistent with the distribution of populated areas in Guangzhou. The high-risk areas accounted for 6.18% of the total area, and the at-risk populations in the high-risk areas accounted for 39.13% of the total population. More than 80.00% of the population in Yuexiu, Liwan, Haizhu, and Tianhe districts were in the high-risk areas.Conclusions The risk of dengue transmission in Guangzhou was mainly distributed in the central urban areas of Guangzhou, with Yuexiu, Liwan, and Haizhu districts as the center, extending northward to central Baiyun District, southward to the junction of Panyu and Nansha districts, and eastward to eastern Huangpu District. The predicted risk areas were highly consistent with case distributions, indicating that the method proposed in this study can more accurately depict the geographical distribution of dengue transmission risk.

Key words： Dengue fever; Transmission risk prediction; Random forest; Resampling; Guangzhou

Cite this article

ZHANG Meng-zhen, REN Zhou-peng, FAN Jun-fu, XIAO Jian-peng, ZHANG Ying-tao . Fine-scale dengue transmission risk prediction based on multi-source geographic data in Guangzhou,China[J]. Chinese Journal of Vector Biology and Control, 2023 , 34(5) : 654 -663 . DOI: 10.11853/j.issn.1003.8280.2023.05.013

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