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. 2019 Dec 10;19(1):1659.
doi: 10.1186/s12889-019-7966-8.

Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples

Chelsea S Lutz  1   2   3 , Mimi P Huynh  4 , Monica Schroeder  4 , Sophia Anyatonwu  5 , F Scott Dahlgren  6 , Gregory Danyluk  7 , Danielle Fernandez  8 , Sharon K Greene  9 , Nodar Kipshidze  10 , Leann Liu  11 , Osaro Mgbere  12 , Lisa A McHugh  13 , Jennifer F Myers  14 , Alan Siniscalchi  15 , Amy D Sullivan  16 , Nicole West  17 , Michael A Johansson  18 , Matthew Biggerstaff  6
Affiliations

Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples

Chelsea S Lutz et al. BMC Public Health. .

Abstract

Background: Infectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts.

Main body: For forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013-14 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication.

Conclusions: These efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.

Keywords: Decision making; Disease outbreaks; Emergency preparedness; Forecast; Infectious disease; Influenza; Pandemic.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The use of trade names is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention and/or the Council for State and Territorial Epidemiologists
Fig. 2
Fig. 2
The Morbidity and Mortality Weekly Report (MMWR) week is the week of the epidemiologic year for which the National Notifiable Diseases Surveillance System (NNDSS) disease report is assigned by the reporting local or state health department for the purposes of disease incidence reporting and publishing [36]. Values range from 1 to 53, although most years consist of 52 weeks. The weeks shown in the figure above are for example only, as MMWR weeks and corresponding calendar date may shift year to year
Fig. 3
Fig. 3
Predictions for national ILI percentage published for Week 52 through Week 3 (1-, 2-, 3-, and 4-weeks ahead, respectively) and associated 80% prediction interval
See this image and copyright information in PMC

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