{"title":"Fine-Scale Spatial Prediction on the Risk of <i>Plasmodium vivax</i> Infection in the Republic of Korea.","authors":"Kyung-Duk Min, Yae Jee Baek, Kyungwon Hwang, Na-Ri Shin, So-Dam Lee, Hyesu Kan, Joon-Sup Yeom","doi":"10.3346/jkms.2024.39.e176","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Malaria elimination strategies in the Republic of Korea (ROK) have decreased malaria incidence but face challenges due to delayed case detection and response. To improve this, machine learning models for predicting malaria, focusing on high-risk areas, have been developed.</p><p><strong>Methods: </strong>The study targeted the northern region of ROK, near the demilitarized zone, using a 1-km grid to identify areas for prediction. Grid cells without residential buildings were excluded, leaving 8,425 cells. The prediction was based on whether at least one malaria case was reported in each grid cell per month, using spatial data of patient locations. Four algorithms were used: gradient boosted (GBM), generalized linear (GLM), extreme gradient boosted (XGB), and ensemble models, incorporating environmental, sociodemographic, and meteorological data as predictors. The models were trained with data from May to October (2019-2021) and tested with data from May to October 2022. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).</p><p><strong>Results: </strong>The AUROC of the prediction models performed excellently (GBM = 0.9243, GLM = 0.9060, XGB = 0.9180, and ensemble model = 0.9301). Previous malaria risk, population size, and meteorological factors influenced the model most in GBM and XGB.</p><p><strong>Conclusion: </strong>Machine-learning models with properly preprocessed malaria case data can provide reliable predictions. Additional predictors, such as mosquito density, should be included in future studies to improve the performance of models.</p>","PeriodicalId":16249,"journal":{"name":"Journal of Korean Medical Science","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11164649/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Korean Medical Science","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3346/jkms.2024.39.e176","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Malaria elimination strategies in the Republic of Korea (ROK) have decreased malaria incidence but face challenges due to delayed case detection and response. To improve this, machine learning models for predicting malaria, focusing on high-risk areas, have been developed.
Methods: The study targeted the northern region of ROK, near the demilitarized zone, using a 1-km grid to identify areas for prediction. Grid cells without residential buildings were excluded, leaving 8,425 cells. The prediction was based on whether at least one malaria case was reported in each grid cell per month, using spatial data of patient locations. Four algorithms were used: gradient boosted (GBM), generalized linear (GLM), extreme gradient boosted (XGB), and ensemble models, incorporating environmental, sociodemographic, and meteorological data as predictors. The models were trained with data from May to October (2019-2021) and tested with data from May to October 2022. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).
Results: The AUROC of the prediction models performed excellently (GBM = 0.9243, GLM = 0.9060, XGB = 0.9180, and ensemble model = 0.9301). Previous malaria risk, population size, and meteorological factors influenced the model most in GBM and XGB.
Conclusion: Machine-learning models with properly preprocessed malaria case data can provide reliable predictions. Additional predictors, such as mosquito density, should be included in future studies to improve the performance of models.
期刊介绍:
The Journal of Korean Medical Science (JKMS) is an international, peer-reviewed Open Access journal of medicine published weekly in English. The Journal’s publisher is the Korean Academy of Medical Sciences (KAMS), Korean Medical Association (KMA). JKMS aims to publish evidence-based, scientific research articles from various disciplines of the medical sciences. The Journal welcomes articles of general interest to medical researchers especially when they contain original information. Articles on the clinical evaluation of drugs and other therapies, epidemiologic studies of the general population, studies on pathogenic organisms and toxic materials, and the toxicities and adverse effects of therapeutics are welcome.