Predicting influenza with dynamical methods

BMC Medical Informatics and Decision Making

Research Article

Jean-Paul Chretien¹  Anna L. Buczak²  Ben Baugher²  Linda Moniz²  Erhan Guven² 

[1] Armed Forces Health Surveillance Branch, Defense Health Agency, Silver Spring, MD, USA;Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD, USA;

关键词: Influenza;    Prediction;    Analogues;   

DOI  :  10.1186/s12911-016-0371-7

 received in 2015-10-10, accepted in 2016-10-05,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundPrediction of influenza weeks in advance can be a useful tool in the management of cases and in the early recognition of pandemic influenza seasons.MethodsThis study explores the prediction of influenza-like-illness incidence using both epidemiological and climate data. It uses Lorenz’s well-known Method of Analogues, but with two novel improvements. Firstly, it determines internal parameters using the implicit near-neighbor distances in the data, and secondly, it employs climate data (mean dew point) to screen analogue near-neighbors and capture the hidden dynamics of disease spread.ResultsThese improvements result in the ability to forecast, four weeks in advance, the total number of cases and the incidence at the peak with increased accuracy. In most locations the total number of cases per year and the incidence at the peak are forecast with less than 15 % root-mean-square (RMS) Error, and in some locations with less than 10 % RMS Error.ConclusionsThe use of additional variables that contribute to the dynamics of influenza spread can greatly improve prediction accuracy.

【 授权许可】

CC BY   
© The Author(s). 2016