ArticleOpen forum infectious diseases2017
Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model.
Article in Open forum infectious diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Effects of weather-related social distancing on city-scale transmission of respiratory viruses: a retrospective cohort study.BMC infectious diseases · 2021Article
- Predicting regional influenza epidemics with uncertainty estimation using commuting data in Japan.PloS one · 2021Article
- Robust two-stage influenza prediction model considering regular and irregular trends.PloS one · 2020Article
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Authors and funding
11 authors.
Funding
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Abstract
backgroundSeasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging.
methodsWe used the previously described "incidence decay with exponential adjustment" (IDEA) model, a 2-parameter phenomenological model, to evaluate the characteristics of the 2015-2016 influenza season in 4 Canadian jurisdictions: the Provinces of Alberta, Nova Scotia and Ontario, and the City of Ottawa. Model fits were updated weekly with receipt of incident virologically confirmed case counts. Best-fit models were used to project seasonal influenza peaks and epidemic final sizes.
resultsThe 2015-2016 influenza season was mild and late-peaking. Parameter estimates generated through fitting were consistent in the 2 largest jurisdictions (Ontario and Alberta) and with pooled data including Nova Scotia counts (R
conclusionsA simple, 2-parameter influenza model provided reasonably accurate real-time projections of influenza seasonal dynamics in an atypically late, mild influenza season. Challenges are similar to those seen with more complex forecasting methodologies. Future work includes identification of seasonal characteristics associated with variability in model performance.
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