The effect of climate change on cholera disease: The road ahead using artificial neural network

被引:53
作者
Asadgol, Zahra [1 ]
Mohammadi, Hamed [2 ]
Kermani, Majid [1 ,3 ]
Badirzadeh, Alireza [4 ]
Gholami, Mitra [1 ,3 ]
机构
[1] Iran Univ Med Sci, Sch Publ Hlth, Dept Environm Hlth Engn, Tehran, Iran
[2] Zanjan Univ Med Sci, Sch Publ Hlth, Dept Environm Hlth Engn, Zanjan, Iran
[3] Iran Univ Med Sci, Res Ctr Environm Hlth Technol, Tehran, Iran
[4] Iran Univ Med Sci, Sch Med, Dept Parasitol & Mycol, Tehran, Iran
基金
美国国家科学基金会;
关键词
INFECTIOUS-DISEASES; RAINFALL; TEMPERATURE; IMPACT;
D O I
10.1371/journal.pone.0224813
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020-2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.
引用
收藏
页数:20
相关论文
共 50 条
[1]   Application of artificial neural network for predicting weld quality in laser transmission welding of thermoplastics [J].
Acherjee, Bappa ;
Mondal, Subrata ;
Tudu, Bipan ;
Misra, Dipten .
APPLIED SOFT COMPUTING, 2011, 11 (02) :2548-2555
[2]   Reinforcing cholera intervention through prediction-aided prevention [J].
Akanda, Ah S. ;
Jutla, Antarpreet S. ;
Gute, David M. ;
Evans, Timothy ;
Islam, Shafiqul .
BULLETIN OF THE WORLD HEALTH ORGANIZATION, 2012, 90 (03) :243-244
[3]  
[Anonymous], 2008, PROT HLTH CLIM CHANG
[4]  
[Anonymous], 1885, HIST CHOLERA INDIA 1
[5]  
ARBOGAST T, 2017, PLOS NEGLECT TROP D, V13, DOI DOI 10.1371/JOURNAL.PNTD.5245
[6]   The impact of rainfall and temperature variation on diarrheal prevalence in Sub-Saharan Africa [J].
Bandyopadhyay, Sushenjit ;
Kanji, Shireen ;
Wang, Limin .
APPLIED GEOGRAPHY, 2012, 33 (01) :63-72
[7]  
Bryden JL, 1871, EPIDEMIC CONNECTION
[8]  
Bryden JL, 1870, REPORT GEN ASPECTS E
[9]   Pandemics, pathogenicity and changing molecular epidemiology of cholera in the era of global warming [J].
Chowdhury, Fazle Rabbi ;
Nur, Zannatun ;
Hassan, Nazia ;
von Seidlein, Lorenz ;
Dunachie, Susanna .
ANNALS OF CLINICAL MICROBIOLOGY AND ANTIMICROBIALS, 2017, 16
[10]   Feature selection for genetic sequence classification [J].
Chuzhanova, NA ;
Jones, AJ ;
Margetts, S .
BIOINFORMATICS, 1998, 14 (02) :139-143