Schistosomiasis transmission in Zimbabwe:Modelling based on machine learning  

作　　者：Hong-Mei Li Jin-Xin Zheng Nicholas Midzi Masceline Jenipher Mutsaka-Makuvaza Shan Lv Shang Xia Ying-jun Qian Ning Xiao Robert Berguist Xiao-Nong Zhou 

机构地区：[1]National Institute of Parasitic Diseases,Chinese Center for Disease Control and Prevention(Chinese Center for Tropical Diseases Research),NHC Key Laboratory of Parasite and Vector Biology,WHO Collaborating Centre for Tropical Diseases,National Center for International Research on Tropical Diseases,Shanghai,200025,China [2]National Institute of Health Research,Ministry of Health and Child Care,Harare,Zimbabwe [3]University of Rwanda,College of Medicine and Health Sciences,School of Medicine and Pharmacy,Department of Microbiology and Parasitology,Rwanda [4]Ingerod 407,SE-45494,Brastad,Sweden

出　　处：《Infectious Disease Modelling》2024年第4期1081-1094,共14页传染病建模（英文）

基　　金：supported by the program of the Chinese Center for Tropical Diseases Research(No.131031104000160004);the China-Africa Cooperation Project on Schistosomiasis Control and Elimination(2020-C4-0001-2).

摘　　要：Zimbabwe,located in Southern Africa,faces a significant public health challenge due to schistosomiasis.We investigated this issue with emphasis on risk prediction of schistosomiasis for the entire population.To this end,we reviewed available data on schistosomiasis in Zimbabwe from a literature search covering the 1980-2022 period considering the potential impact of 26 environmental and socioeconomic variables obtained from public sources.We studied the population requiring praziquantel with regard to whether or not mass drug administration(MDA)had been regularly applied.Three machinelearning algorithms were tested for their ability to predict the prevalence of schistosomiasis in Zimbabwe based on the mean absolute error(MAE),the root mean squared error(RMSE)and the coefficient of determination(R2).The findings revealed different roles of the 26 factors with respect to transmission and there were particular variations between Schistosoma haematobium and S.mansoni infections.We found that the top-five correlation factors,such as the past(rather than current)time,unsettled MDA implementation,constrained economy,high rainfall during the warmest season,and high annual precipitation were closely associated with higher S.haematobium prevalence,while lower elevation,high rainfall during the warmest season,steeper slope,past(rather than current)time,and higher minimum temperature in the coldest month were rather related to higher S.mansoni prevalence.The random forest(RF)algorithm was considered as the formal best model construction method,with MAE=0.108;RMSE=0.143;and R^(2)=0.517 for S.haematobium,and with the corresponding figures for S.mansoni being 0.053;0.082;and 0.458.Based on this optimal model,the current total schistosomiasis prevalence in Zimbabwe under MDA implementation was 19.8%,with that of S.haematobium at 13.8% and that of S.mansoni at 7.1%,requiring annual MDA based on a population of 3,003,928.Without MDA,the current total schistosomiasis prevalence would be 23.2%,that of S.haematobium 17.1% and that of

关 键 词：MACHINE-LEARNING Transmission risk model SCHISTOSOMIASIS Zimbabwe 

分 类 号：R532.2[医药卫生—内科学] TP181[医药卫生—临床医学]