| Literature DB >> 29506911 |
Matthew Biggerstaff1, Michael Johansson2, David Alper3, Logan C Brooks4, Prithwish Chakraborty5, David C Farrow6, Sangwon Hyun7, Sasikiran Kandula8, Craig McGowan9, Naren Ramakrishnan5, Roni Rosenfeld10, Jeffrey Shaman8, Rob Tibshirani11, Ryan J Tibshirani12, Alessandro Vespignani13, Wan Yang8, Qian Zhang13, Carrie Reed9.
Abstract
Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from <0.01 to 0.41, peak week ranged from 0.08 to 0.49, and peak intensity ranged from <0.01 to 0.17. The scores for predictions of ILI 1-4 weeks in advance ranged from 0.02-0.38 and was highest 1 week ahead. Forecast skill varied by HHS region. Forecasts can predict epidemic characteristics that inform public health actions. CDC, state and local health officials, and researchers are working together to improve forecasts. Published by Elsevier B.V.Entities:
Keywords: Epidemics; Forecasting; Influenza; Modeling; Prediction
Mesh:
Year: 2018 PMID: 29506911 PMCID: PMC6108951 DOI: 10.1016/j.epidem.2018.02.003
Source DB: PubMed Journal: Epidemics ISSN: 1878-0067 Impact factor: 4.396