[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117951-en":3,"doc-seo-117951-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117951,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Predicting Infectious Disease Outbreaks with Machine Learning and Epidemiological Data","International public health has increasingly relied on machine learning combined with epidemiological data to forecast and control infectious-disease outbreaks. This study examines how modern modeling paradigms change disease-outbreak prediction, focusing on deep learning and ensemble approaches for extracting patterns and correlations from large, diverse datasets. It integrates epidemiological evidence such as case reports, genetic sequencing, and population dynamics, aiming to improve predictive accuracy and understanding of disease dynamics. It also highlights using human health data alongside environmental, socioeconomic, and mobility indicators to strengthen forecast robustness and completeness.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-4 Year 2023 Page 110:121  \nPredicting Infectious Disease Outbreaks with Machine Learning and  \nEpidemiological Data  \nSyed Ziaur Rahman1*, R Senthil2, Venkadeshan Ramalingam3, R. Gopal 4  \n1Assistant Professor, Department of Information Technology, Majan University College, Sultanate of Oman.  \n[syed.rahman@majancollege.edu.om](syed.rahman@majancollege.edu.om)  \n2Assistant Professor, Department of Information Technology, Majan University College, Sultanate of Oman,  \n[senthil.ramadoss@majancollege.edu.om](senthil.ramadoss@majancollege.edu.om)  \n3Faculty – Information Technology Department, Department of Information Technology, University of Technology and Applied Sciences – Shinas,Sultanate of Oman, [Venkadeshan.ramalingam@shct.edu.om](Venkadeshan.ramalingam@shct.edu.om)[ ](Venkadeshan.ramalingam@shct.edu.om)4Assistant Professor, Information and communication Engineering, College of Engineering, University of  \nBuraimi, Sultanate of Oman, [Gopal.r@uob.edu.om](Gopal.r@uob.edu.om)  \n*Corresponding author’[s E-mail: syed.rahman@majancollege.edu.om](s E-mail: syed.rahman@majancollege.edu.om)  \n\n| Article History\u003Cbr>Received: 06 June 2023\u003Cbr>Revised: 05 Sept 2023\u003Cbr>Accepted: 25 Nov 2023\u003Cbr>CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Over the past several years, there has been a notable shift in the international public health arena, mostly driven by the use of machine learning methodologies and epidemiological data for the purpose of forecasting and controlling outbreaks of infectious diseases. This study explores the changing paradigm of disease outbreak prediction by examining current advancementsand emerging patterns in the field of machine learning and epidemiology. In this paper, we explore the complex procedure of forecasting infectious disease outbreaks, a task of significant significance for global public health authorities. This paper examines the crucial role of machine learning algorithms in this undertaking, elucidating their capacity to analyze extensive and heterogeneous datasets in order to produce significant insights and predictions Our inquiry spans multiple facets of this complex topic. This study examines the transformative impact of machine learning models, namely deep learning and ensemble approaches, on the field. The individuals in question have exhibited remarkable proficiency in recognizing patterns, establishing correlations, and formulating predictions by utilizing past data. Consequently, this has greatly contributed to the prompt identification and readiness for potential outbreaks. Moreover, our study involves the incorporation of epidemiological data, including case reports, genetic sequencing, and population dynamics, into the machine learning architecture. This study investigates the enhanced predictive accuracy and improved comprehension of disease dynamics resulting from the integration of data-driven models and expert knowledge from the field of epidemiology. The integration of different approaches is of utmost importance when it comes to effectively tackling the distinct characteristics and problems presented by diverse infectious illnesses. Additionally, the research emphasizes the significance of incorporating a wide range of data sources, including not only data related to human health, but also environmental factors, socioeconomic metrics, and patterns of human mobility. Non-conventional data sources provide essential contextual information for comprehending the dynamics of disease transmission, hence enhancing the robustness and comprehensiveness of forecasts.\u003Cbr>Keywords: Convolutional Neural Network (CNN) |\n| --- | --- |\n\n1. Introduction  \nInfectious diseases present an ongoing and significant risk to the overall well-being of the public, frequently exhibiting rapid and unpredictable transmission patterns, and carrying the potential for substantial societal and economic ramifications. The increasing occurrence of novel pat","cbCaip71XQiF6Get","https://ap.wps.com/l/cbCaip71XQiF6Get","pdf",447707,1,12,"English","en",105,"# Abstract\n# Article History\n# Introduction\n# Methods and Data Integration\n# Machine Learning Models for Outbreak Forecasting\n# Predictive Accuracy and Disease Dynamics\n# Data Sources and Contextual Features","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To improve understanding and forecasting of infectious disease outbreaks by combining machine learning with epidemiological data and exploring recent advances in the field.\"},{\"question\":\"Which machine learning approaches does the study emphasize?\",\"answer\":\"It highlights deep learning models and ensemble approaches for analyzing heterogeneous, large-scale datasets and producing predictions.\"},{\"question\":\"What types of epidemiological data are incorporated?\",\"answer\":\"The study discusses using case reports, genetic sequencing, and population dynamics 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