[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121922-en":3,"doc-seo-121922-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121922,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning - article summary","Tuberculosis (TB) remains a major cause of mortality worldwide, with Brazil and its Amazon region carrying substantial burdens. Focusing on riverine municipalities, the study tests whether TB incidence forms spatial clusters and trains machine learning to classify municipalities as hotspots or non-hotspots using surveillance indicators. Moran’s I quantifies spatial autocorrelation and Getis-Ord Gi* identifies high- and low-incidence clusters. A Random Forest model predicts hotspots with AUC-ROC 0.81, highlighting recurrent cases, TB deaths, regimen changes, new case shares, and smoking history as key predictors to support evidence-based resource allocation.","[www.scielo.br/rbepid](www.scielo.br/rbepid Revista Brasileira de)[ Revista Brasileira de](www.scielo.br/rbepid Revista Brasileira de) Epidemiologia [https://doi.org/10.1590/1980-549720240024](https://doi.org/10.1590/1980-549720240024)  \nORIGINAL ARTICLE  \nPrediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning  \nPredição de áreas de aglomeração de tuberculose nos municípios ribeirinhos da Amazônia brasileira  \ncom aprendizagem de máquina  \nLuis SilvaI , Luise Gomes da MottaII , Lynn EberlyI   \nI University of Minnesota, Minneapolis – Minneapolis (MN), United States. II Universidade Federal Fluminense – Niterói (RJ), Brazil.  \nABSTRACT  \n\n| Objective: Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the “riverine municipalities” and hypothesizes the presence of TB clusters in the area. We also aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. Methods: Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran’s I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. Results: Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. Conclusion: Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities. Keywords: Tuberculosis. Amazon. Spatial analysis. Machine learning. Epidemiology. Ribeirinhos. |\n| --- |\n| CORRESPONDING AUTHOR: Luis Silva. 717 Delaware St, Suite 508, 55141 Minneapolis (MN), United States. Email: [silva364@umn.edu](silva364@umn.edu) |\n| CONFLICT OF INTERESTS: nothing to declare. |\n| HOW TO CITE THIS ARTICLE: Silva L, Motta LG, Eberly LE. Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning. Rev Bras Epidemiol. 2024; 27: e240024 . [https://doi.org/10.1590/1980-549720240024](https://doi.org/10.1590/1980-549720240024) |\n\nThis is an open article distributed under the CC-BY 4.0 license, which allows copying and redistribution of the material in any format and for any purpose as long asthe original authorship and publication credits are maintained.  \nReceived on: 10/17/2023  \nReviewed on: 02/17/2024  \nAccepted on: 03/06/2024  \nPrediciton of Tuberculosis clusters in the Brazilian Amazon. Rev Bras Epidemiol. 2024; 27: e240024 1  \n[www.scielo.br/rbepid](www.scielo.br/rbepid) [https://doi.org/10.1590/1980-549720240024](https://doi.org/10.1590/1980-549720240024)   \nINTRODUCTION  \nA 2022 report by the World Health Organization places tuberculosis (TB) as the second most deadly infectious disease globally, surpassed only recently by COVID-191. It also shows Brazil as one of the 30 countries with the highest TB burden in the world. Brazilian healthcare authorities have reported 78,057 cases of the disease in 2022. As such, the yearly incidence of TB in the country was 34.9 per 100 thousand2.  \nAm","cbCaiq1drjPbPjsM","https://ap.wps.com/l/cbCaiq1drjPbPjsM","pdf",1728835,1,7,"English","en",105,"# Introduction\n## Study rationale and definition of riverine municipalities\n## Objective: spatial clustering and machine-learning hotspot prediction\n# Methods\n## Data sources (TB incidence 2019–2022)\n## Spatial analysis (Moran’s I, Getis-Ord Gi*)\n## Machine learning approach (Random Forest classification)\n# Results\n## Geographical clustering patterns\n## Model performance and selected predictors\n# Conclusion\n## Implications for targeting TB control resources","[{\"question\":\"What does the study aim to predict and how are hotspots defined?\",\"answer\":\"The study predicts TB hotspots—riverine municipalities with high-incidence clusters—using surveillance variables. Municipalities are classified relative to other non-hot-spot areas identified through spatial methods.\"},{\"question\":\"How are spatial clusters detected in the analysis?\",\"answer\":\"Global spatial autocorrelation is assessed with Moran’s I, and the Getis-Ord Gi* method is used to detect high- and low-incidence clusters across municipalities.\"},{\"question\":\"Which surveillance factors were most predictive of TB hotspots?\",\"answer\":\"Higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, the share of new cases, and cases with a smoking history were the strongest predictors.\"}]","Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning - article summary | PDF",1785807753,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prediction-of-tuberculosis-clusters-in-the-riverine-municipalities-of-the-brazilian-amazon-with-machine-learning-article-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-tuberculosis-clusters-in-the-riverine-municipalities-of-the-brazilian-amazon-with-machine-learning-article-summary/121922/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the study aim to predict and how are hotspots defined?","Question",{"text":75,"@type":76},"The study predicts TB hotspots—riverine municipalities with high-incidence clusters—using surveillance variables. Municipalities are classified relative to other non-hot-spot areas identified through spatial methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are spatial clusters detected in the analysis?",{"text":80,"@type":76},"Global spatial autocorrelation is assessed with Moran’s I, and the Getis-Ord Gi* method is used to detect high- and low-incidence clusters across municipalities.",{"name":82,"@type":73,"acceptedAnswer":83},"Which surveillance factors were most predictive of TB hotspots?",{"text":84,"@type":76},"Higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, the share of new cases, and cases with a smoking history were the strongest predictors.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]