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Techniques and Approaches for Disease Outbreak Prediction: A Survey

Published: 21 March 2016 Publication History

Abstract

As on today, due to rise in pollution and dense population in India, there has been an increase in health related issues with respect to spreading disease. The recent outbreak of diseases like HINI, dengue, malaria etc. have made people to focus on ways to minimize the effect of infectious disease. This can be achieved if early prediction and detection of such diseases are possible. Research is being carried out in building various prediction models to forecast the outbreak of such epidemics. While the traditional techniques were useful in giving the results, inputs from social media enabled us to detect the presence, prevalence and spread of these disease in a much early manner. Social networking Medias and their blogs have also been used for prediction. To handle such variety and size of data, it is required to design a system using big data approach for forecasting. Big Data is one of the need of the hour area of research that is become increasingly popular in prediction. This paper focuses on the survey carried out on available techniques and presents a unified approach to prepare a conceptual forecasting model.

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Fabgge Li, Peixian Luan, 2011, A Model for Predicting the Number of New Outbreaks of Newcastle Disease during the Month, IEEE 2011
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Cited By

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  • (2024)Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random ForestDiagnostics10.3390/diagnostics1404038514:4(385)Online publication date: 9-Feb-2024
  • (2022)Study on the ANN Forecasting of Epidemical DiseasesCOVID-19 Pandemic10.1007/978-981-16-4372-9_8(129-145)Online publication date: 6-Jan-2022
  • (2021)Modeling and Forecasting Cases of RSV Using Artificial Neural NetworksMathematics10.3390/math92229589:22(2958)Online publication date: 19-Nov-2021
  • Show More Cited By

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Published In

cover image ACM Other conferences
WIR '16: Proceedings of the ACM Symposium on Women in Research 2016
March 2016
179 pages
ISBN:9781450342780
DOI:10.1145/2909067
Permission to make digital or hard copies of all or part of this 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 March 2016

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Author Tags

  1. Prediction model
  2. disease outbreak
  3. social media
  4. time series model

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  • Research-article
  • Research
  • Refereed limited

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WIR '16
WIR '16: Women in Research 2016
March 21 - 22, 2016
Indore, India

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WIR '16 Paper Acceptance Rate 35 of 117 submissions, 30%;
Overall Acceptance Rate 35 of 117 submissions, 30%

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Cited By

View all
  • (2024)Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random ForestDiagnostics10.3390/diagnostics1404038514:4(385)Online publication date: 9-Feb-2024
  • (2022)Study on the ANN Forecasting of Epidemical DiseasesCOVID-19 Pandemic10.1007/978-981-16-4372-9_8(129-145)Online publication date: 6-Jan-2022
  • (2021)Modeling and Forecasting Cases of RSV Using Artificial Neural NetworksMathematics10.3390/math92229589:22(2958)Online publication date: 19-Nov-2021
  • (2020)Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from ArgentinaPLOS ONE10.1371/journal.pone.023385515:7(e0233855)Online publication date: 16-Jul-2020
  • (2020)Learning from Tweets: Opportunities and Challenges to Inform Policy Making During Dengue EpidemicProceedings of the ACM on Human-Computer Interaction10.1145/33928754:CSCW1(1-27)Online publication date: 29-May-2020
  • (2020)Social media based surveillance systems for healthcare using machine learning: A systematic reviewJournal of Biomedical Informatics10.1016/j.jbi.2020.103500108(103500)Online publication date: Aug-2020
  • (2018)Dengue Epidemics Prediction: A Survey of the State-of-the-Art Based on Data Science ProcessesIEEE Access10.1109/ACCESS.2018.28712416(53757-53795)Online publication date: 2018

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