表紙画像

Leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east Peninsular Malaysia

Leptospirosis is a zoonotic tropical disease caused by pathogenic Leptospira sp. whose transmission has been linked to extreme hydrometeorological phenomena. Hydrometeorological variability in the form of averages and extremes indices have been used before as drivers in statistical prediction of...

詳細記述

保存先:
書誌詳細
第一著者: Jayaramu, Veianthan
フォーマット: 学位論文
言語:English
出版事項: 2022
主題:
Hydrometeorology
Leptospirosis
オンライン･アクセス:http://psasir.upm.edu.my/id/eprint/115737/1/115737.pdf
http://psasir.upm.edu.my/id/eprint/115737/
http://ethesis.upm.edu.my/id/eprint/18246
タグ: タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
id my.upm.eprints.115737
record_format eprints
spelling my.upm.eprints.1157372025-03-13T07:47:47Z http://psasir.upm.edu.my/id/eprint/115737/ Leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east Peninsular Malaysia Jayaramu, Veianthan Leptospirosis is a zoonotic tropical disease caused by pathogenic Leptospira sp. whose transmission has been linked to extreme hydrometeorological phenomena. Hydrometeorological variability in the form of averages and extremes indices have been used before as drivers in statistical prediction of disease occurrence; however, their importance and predictive capacity are still little known. Random forest classification models of leptospirosis occurrence were developed to identify the important hydrometeorological indices and models’ prediction accuracy, sensitivity, and specificity based on the sets of indices used, using case data from three districts in Kelantan, Malaysia. This region experiences annual monsoonal rainfall and flooding, and that record high leptospirosis incidence rates. First, hydrometeorological data including rainfall, streamflow, water level, relative humidity and temperature were derived into 164 weekly average and extreme indices in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). Then, the weekly number of cases were classified into binary classes ‘high’ and ‘low’ based on an average threshold. 17 models based on ‘average’, ‘extreme’ and ‘mixed’ sets of indices – based on the type of indices used as input – were trained by optimizing the feature subsets using the embedded approach that utilized the mean decrease Gini (MDG) scores. The variable importance was assessed through cross correlation analysis and the MDG scores. The results showed that the average and extreme models showed similar prediction accuracy ranges while the mixed models showed some improvement. An extreme model was the most sensitive while and average model was the most specific. The time lag associated with the driving indices agreed with the seasonality of the monsoon. The variable importance analysis based on the MDG scores indicated that overall, the rainfall (extreme) factor dominated, suggesting its strong influence on Leptospirosis incidence while the streamflow variable was the least important to the model development despite showing higher cross correlations with leptospirosis. 2022-06 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/115737/1/115737.pdf Jayaramu, Veianthan (2022) Leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east Peninsular Malaysia. Masters thesis, Universiti Putra Malaysia. http://ethesis.upm.edu.my/id/eprint/18246 Hydrometeorology Leptospirosis
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
topic Hydrometeorology
Leptospirosis
spellingShingle Hydrometeorology
Leptospirosis
Jayaramu, Veianthan
Leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east Peninsular Malaysia
description Leptospirosis is a zoonotic tropical disease caused by pathogenic Leptospira sp. whose transmission has been linked to extreme hydrometeorological phenomena. Hydrometeorological variability in the form of averages and extremes indices have been used before as drivers in statistical prediction of disease occurrence; however, their importance and predictive capacity are still little known. Random forest classification models of leptospirosis occurrence were developed to identify the important hydrometeorological indices and models’ prediction accuracy, sensitivity, and specificity based on the sets of indices used, using case data from three districts in Kelantan, Malaysia. This region experiences annual monsoonal rainfall and flooding, and that record high leptospirosis incidence rates. First, hydrometeorological data including rainfall, streamflow, water level, relative humidity and temperature were derived into 164 weekly average and extreme indices in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). Then, the weekly number of cases were classified into binary classes ‘high’ and ‘low’ based on an average threshold. 17 models based on ‘average’, ‘extreme’ and ‘mixed’ sets of indices – based on the type of indices used as input – were trained by optimizing the feature subsets using the embedded approach that utilized the mean decrease Gini (MDG) scores. The variable importance was assessed through cross correlation analysis and the MDG scores. The results showed that the average and extreme models showed similar prediction accuracy ranges while the mixed models showed some improvement. An extreme model was the most sensitive while and average model was the most specific. The time lag associated with the driving indices agreed with the seasonality of the monsoon. The variable importance analysis based on the MDG scores indicated that overall, the rainfall (extreme) factor dominated, suggesting its strong influence on Leptospirosis incidence while the streamflow variable was the least important to the model development despite showing higher cross correlations with leptospirosis.
format Thesis
author Jayaramu, Veianthan
author_facet Jayaramu, Veianthan
author_sort Jayaramu, Veianthan
title Leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east Peninsular Malaysia
title_short Leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east Peninsular Malaysia
title_full Leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east Peninsular Malaysia
title_fullStr Leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east Peninsular Malaysia
title_full_unstemmed Leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east Peninsular Malaysia
title_sort leptospirosis modelling using hydrometeorological indices and random forest machine learning for humid tropical north-east peninsular malaysia
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/115737/1/115737.pdf
http://psasir.upm.edu.my/id/eprint/115737/
http://ethesis.upm.edu.my/id/eprint/18246
_version_ 1827442620921544704
score 13.250246

類似資料