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A Systematic Review of Influenza Forecasting Studies
Author(s) -
Jean-Paul Chrétien,
Dylan B. George,
Ellis McKenzie
Publication year - 2014
Publication title -
online journal of public health informatics
Language(s) - English
Resource type - Journals
ISSN - 1947-2579
DOI - 10.5210/ojphi.v6i1.5016
Subject(s) - clinical practice , sensitivity (control systems) , population , computer science , covid-19 , statistics , medicine , data mining , machine learning , mathematics , family medicine , environmental health , disease , electronic engineering , infectious disease (medical specialty) , engineering
We assessed human influenza forecasting studies to spur translation of these novel methods to practice.  Searching 3 databases for papers in English, year 2000-, that validated against independent data, we included 36.  They were population-based, hospital-based, and forecast pandemic spread (N=28, 4, 4, respectively); and used curve-prediction and diffusion models (N=19, 17, respectively).  Four and 5 used internet search and meteorological data, respectively, besides clinical data.  Eight reported sensitivity analyses; 1 compared agent-based and compartmental models.  Several showed favorable 4-week-ahead skill, but lack of sensitivity analysis and model comparisons, and implementation challenges for complex models, may hinder translation to practice.

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