A systematic review of studies on forecasting the dynamics of influenza outbreaks
Version of Record online: 23 DEC 2013
© 2013 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Influenza and Other Respiratory Viruses
Volume 8, Issue 3, pages 309–316, May 2014
How to Cite
2014) A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza and Other Respiratory Viruses 8(3), 309–316.et al. (
- Issue online: 10 APR 2014
- Version of Record online: 23 DEC 2013
- Manuscript Accepted: 24 NOV 2013
- Intelligence Advanced Research Projects Activity (IARPA)
- Department of Interior National Business Center (DoI/NBC). Grant Number: D12PC000337
- Compartmental models;
- individual-based models;
- infectious diseases;
- influenza forecasting;
- time series models
Forecasting the dynamics of influenza outbreaks could be useful for decision-making regarding the allocation of public health resources. Reliable forecasts could also aid in the selection and implementation of interventions to reduce morbidity and mortality due to influenza illness. This paper reviews methods for influenza forecasting proposed during previous influenza outbreaks and those evaluated in hindsight. We discuss the various approaches, in addition to the variability in measures of accuracy and precision of predicted measures. PubMed and Google Scholar searches for articles on influenza forecasting retrieved sixteen studies that matched the study criteria. We focused on studies that aimed at forecasting influenza outbreaks at the local, regional, national, or global level. The selected studies spanned a wide range of regions including USA, Sweden, Hong Kong, Japan, Singapore, United Kingdom, Canada, France, and Cuba. The methods were also applied to forecast a single measure or multiple measures. Typical measures predicted included peak timing, peak height, daily/weekly case counts, and outbreak magnitude. Due to differences in measures used to assess accuracy, a single estimate of predictive error for each of the measures was difficult to obtain. However, collectively, the results suggest that these diverse approaches to influenza forecasting are capable of capturing specific outbreak measures with some degree of accuracy given reliable data and correct disease assumptions. Nonetheless, several of these approaches need to be evaluated and their performance quantified in real-time predictions.