Literature DB >> 18593605

Bayesian prediction of an epidemic curve.

Xia Jiang1, Garrick Wallstrom, Gregory F Cooper, Michael M Wagner.   

Abstract

An epidemic curve is a graph in which the number of new cases of an outbreak disease is plotted against time. Epidemic curves are ordinarily constructed after the disease outbreak is over. However, a good estimate of the epidemic curve early in an outbreak would be invaluable to health care officials. Currently, techniques for predicting the severity of an outbreak are very limited. As far as predicting the number of future cases, ordinarily epidemiologists simply make an educated guess as to how many people might become affected. We develop a model for estimating an epidemic curve early in an outbreak, and we show results of experiments testing its accuracy.

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Year:  2008        PMID: 18593605     DOI: 10.1016/j.jbi.2008.05.013

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  16 in total

1.  Temporal and spatial monitoring and prediction of epidemic outbreaks.

Authors:  Amin Zamiri; Hadi Sadoghi Yazdi; Sepideh Afkhami Goli
Journal:  IEEE J Biomed Health Inform       Date:  2014-08-06       Impact factor: 5.772

2.  Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

Authors:  Di Zhao; Chunhua Weng
Journal:  J Biomed Inform       Date:  2011-05-27       Impact factor: 6.317

3.  Prediction of an Epidemic Curve: A Supervised Classification Approach.

Authors:  Elaine O Nsoesie; Richard Beckman; Madhav Marathe; Bryan Lewis
Journal:  Stat Commun Infect Dis       Date:  2011-10-04

4.  Influenza detection and prediction algorithms: comparative accuracy trial in Östergötland county, Sweden, 2008-2012.

Authors:  A Spreco; O Eriksson; Ö Dahlström; T Timpka
Journal:  Epidemiol Infect       Date:  2017-05-17       Impact factor: 4.434

5.  Optimizing provider recruitment for influenza surveillance networks.

Authors:  Samuel V Scarpino; Nedialko B Dimitrov; Lauren Ancel Meyers
Journal:  PLoS Comput Biol       Date:  2012-04-12       Impact factor: 4.475

Review 6.  Influenza forecasting in human populations: a scoping review.

Authors:  Jean-Paul Chretien; Dylan George; Jeffrey Shaman; Rohit A Chitale; F Ellis McKenzie
Journal:  PLoS One       Date:  2014-04-08       Impact factor: 3.240

7.  Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations.

Authors:  A Spreco; T Timpka
Journal:  BMJ Open       Date:  2016-05-06       Impact factor: 2.692

8.  Discovering causal interactions using Bayesian network scoring and information gain.

Authors:  Zexian Zeng; Xia Jiang; Richard Neapolitan
Journal:  BMC Bioinformatics       Date:  2016-05-26       Impact factor: 3.169

9.  Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

Authors:  Xia Jiang; Jeremy Jao; Richard Neapolitan
Journal:  PLoS One       Date:  2015-12-01       Impact factor: 3.240

Review 10.  A systematic review of studies on forecasting the dynamics of influenza outbreaks.

Authors:  Elaine O Nsoesie; John S Brownstein; Naren Ramakrishnan; Madhav V Marathe
Journal:  Influenza Other Respir Viruses       Date:  2013-12-23       Impact factor: 4.380

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