[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120524-en":3,"doc-seo-120524-105":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":4,"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},120524,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",6,"Technology","EpiLearn - A Python Library for Machine Learning in Epidemic Modeling","EpiLearn is a Python toolkit created to model, simulate, and analyze epidemic data, addressing limitations of existing epidemic packages that rely mainly on mechanistic equations or conventional statistical tooling. By integrating machine-learning evaluation with comprehensive epidemic-data analysis utilities—simulation, visualization, and transformations—it supports researchers in bridging modern ML methods with epidemiological modeling. A unified framework enables training and evaluation for forecasting and source detection tasks. EpiLearn also includes an interactive web application for real-world or simulated data and is designed with modular flexibility for rapid new-model development.","EpiLearn: A Python Library for Machine Learning in  \nEpidemic Modeling  \nZewen Liu  \nDepartment of Computer Science Emory University [zewen.liu@emory.edu](zewen.liu@emory.edu)  \nYunxiao Li∗ Department of Computer Science Emory University [yunxiao.li2@emory.edu](yunxiao.li2@emory.edu)  \nMingyang Wei∗ Department of Computer Science Emory University [mingyang.wei@emory.edu](mingyang.wei@emory.edu)  \narXiv :2406 .060 16v2 [ cs .LG] 9 Sep 2024  \nGuancheng Wan  \nDepartment of Computer Science Emory University [gwan4@emory.edu](gwan4@emory.edu)  \nMax S.Y. Lau  \nDepartment of Biostatistics and Bioinformatics Emory University [msy.lau@emory.edu](msy.lau@emory.edu)  \nWei Jin  \nDepartment of Computer Science Emory University [wei.jin@emory.edu](wei.jin@emory.edu)  \nABSTRACT  \nEpiLearn is a Python toolkit developed for modeling, simulating, and analyzing epidemic data. Although there exist several packages that also deal with epidemic modeling, they are often restricted to mechanistic models or traditional statistical tools. As machine learning continues to shape the world, the gap between these packages and the latest models has become larger. To bridge the gap and inspire innovative research in epidemic modeling, EpiLearn not only provides support for evaluating epidemic models based on machine learning, but also incorporates comprehensive tools for analyzing epidemic data, such as simulation, visualization, transformations, etc. For the convenience of both epidemiologists and data scientists, we provide a unified framework for training and evaluation of epidemic models on two tasks: Forecasting and Source Detection. To facilitate the development of new models, EpiLearn follows a modular design, making it flexible and easy to use. In addition, an interactive web application is also developed to visualize the real-world or simulated epidemic data. Our package is available at [https://github.com/Emory-Melody/EpiLearn](https://github.com/Emory-Melody/EpiLearn).  \nKEYWORDS  \nEpidemiology, Epidemic models, Machine learning, Neural networks, Python  \nACM Reference Format:  \nZewen Liu, Yunxiao Li, Mingyang Wei, Guancheng Wan, Max S.Y. Lau, and Wei Jin. 2024. EpiLearn: A Python Library for Machine Learning in Epidemic Modeling. In Proceedings of the 7th epiDAMIKACM SIGKDD International Workshop on Epidemiology meets Data Mining and Knowledge Discovery, August 26, 2024, Barcelona, Spain. ACM, New York, NY, USA, 5 pages.  \n∗ Equal Contribution  \nPermission to make digital or hard copies of part or all ofthis work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s) .  \nepiDAMIK ’24, August 26, 2024, Barcelona, Spain © 2024 Copyright held by the owner/author(s) .  \n1 INTRODUCTION  \nData mining in epidemiology is a crucial subject in the healthcare domain, garnering increasing attention in recent years due to the COVID-19 outbreak [1, 2]. A key focus is the development ofcomputational methods in epidemic modeling, which incorporate disease transmission mechanisms to provide insights into changing demographic health states. The diversity of data involved in epidemic modeling necessitates a broad range of tasks, including epidemic forecasting [3], simulation [4], source detection [5], intervention strategies [6], and vaccination [7] .  \nTraditionally, knowledge-driven approaches like mechanistic models (e.g., SIR, SIS, and SEIR [8]) have been employed for epidemic modeling. These methods utilize differential equations to explicitly model relationships among population groups in different states, e.g. Suspected, Infected, and Recovered, and have demonstrated promising performance. However, over the past decade, the rapid progress in machine learning and deep learning [9, 10, 11, 12,","cbCaily3xfEEfdTe","https://ap.wps.com/l/cbCaily3xfEEfdTe","pdf",4190489,1,5,"English","en",105,"# Introduction\n## Forecasting and Source Detection\n## Mechanistic vs Machine Learning Models\n## Unified Library and Modular Design\n## Interactive Visualization","[{\"question\":\"What problem does EpiLearn address in epidemic modeling?\",\"answer\":\"EpiLearn targets the gap between traditional epidemic-modeling packages and newer machine-learning-based approaches by providing ML-supported evaluation and analysis tools for epidemic data.\"},{\"question\":\"Which core tasks does EpiLearn support for model training and evaluation?\",\"answer\":\"EpiLearn provides a unified framework for two tasks: epidemic forecasting and source detection.\"},{\"question\":\"How does EpiLearn enable researchers to build and extend new models?\",\"answer\":\"Its modular design makes the library flexible and easy to use, supporting the development of new epidemic models.\"}]","EpiLearn - 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