FluSight 2018–2019

Influenza (flu) is a respiratory virus that can result in illness ranging from mild to severe. Each year, millions of people get sick with influenza, hundreds of thousands are hospitalized and thousands of people die from flu. Tracking flu activity to inform prevention measures is an important public health function that is currently performed by CDC’s flu surveillance system, which can lag behind real-time flu activity. But what if it were possible to predict flu activity accurately weeks or months in advance for multiple locations? While this is not currently possible, the goal of flu forecasting is to provide a more-timely and forward-looking tool that health officials can use to target medical interventions, inform earlier public health actions, and allocate resources for communications, disease prevention and control. The potential benefits of flu forecasting are significant.

Since 2013, the Influenza Division at the Centers for Disease Control and Prevention has worked with external researchers to improve the science and usability of influenza forecasts by coordinating seasonal influenza prediction challenges for the United States as a whole and for the 10 Health and Human Services Regions. This work includes defining prediction targets, facilitating data access, establishing evaluation metrics to assess accuracy, and developing forecast visualizations.

Multiple outside research teams have developed different flu forecasting models that will provide flu activity forecasts to CDC for the 2018–2019 influenza season. This beta website houses the weekly influenza activity forecasts provided by the various research teams. It’s important to note that these are not CDC forecasts and that the forecasts on this website are not endorsed by CDC. These forecasts are based on different models, can vary significantly, and may be inaccurate.

National and Regional Influenza Forecasting will begin October 29. Please check back for updates.

Interested in participating in the challenge? Please email flucontest@cdc.gov for more information.

**National and Regional Influenza Forecasting will begin October 29. **Please check back for updates.

Forecast Targets

For each week during the season, participants will be asked to provide national and regional probabilistic forecasts for the entire influenza season (seasonal targets) and for the next four weeks (four-week ahead targets). The seasonal targets are the onset week, the peak week, and the peak intensity of the 2018-19 influenza season. The four-week ahead targets are the percent of outpatient visits experiencing influenza-like illness (ILI) one week, two weeks, three weeks, and four weeks ahead from date of the forecast.

Onset Week

Definition The onset of the season is defined as the MMWR surveillance week when the percentage of visits for influenza-like illness (ILI) reported through ILINet reaches or exceeds the baseline value for three consecutive weeks (updated 2018-19 ILINet baseline values for the US and each HHS region will be available the week of October 8, 2018). Forecasted "onset" week values should be for the first week of that three week period.

Motivation Accurate and timely forecasts for the start of the season can be useful in planning for influenza prevention and control activities. For the general public, the start of the season offers an important opportunity to take preventive measures, such as getting vaccinated, before flu becomes widespread. For clinicians and public health authorities, the start of the season indicates that influenza should be high on their list of possible diagnoses for patients with respiratory illness. This is particularly important for the management of hospitalized patients and high-risk patients with suspected influenza when early treatment with influenza antivirals can be critical.

Seasonal Peak Week

Definition The peak week will be defined as the MMWR surveillance week that the weighted ILINet percentage, rounded to one decimal place, is the highest for the 2018-19 influenza season.

Motivation Accurate and timely forecasts for the peak week can be useful for planning and promoting activities to increase influenza vaccination prior to the bulk of influenza illness. For healthcare, pharmacy, and public health authorities, a forecast for the peak week can guide efficient staff and resource allocation.

Seasonal Peak Intensity

Definition The intensity will be defined as the highest numeric value, rounded to one decimal place, that the weighted ILINet percentage reaches during the 2018-19 influenza season.

Motivation Accurate and timely forecasts for the peak week and intensity of the influenza season can be useful for influenza prevention and control, including the planning and promotion of activities to increase influenza vaccination prior to the bulk of influenza illness. For healthcare, pharmacy, and public health authorities, a forecast for the peak week and intensity can help with appropriate staff and resource allocation since a surge of patients with influenza illness can be expected to seek care and receive treatment in the weeks surrounding the peak.

Short Term Forecasts

Definition One- to four-week ahead forecasts will be defined as the weighted ILINet percentage for the target week, rounded to one decimal place.

Motivation Forecasts capable of providing reliable estimates of influenza activity over the next month are critical because they allow healthcare and public health officials to prepare for and respond to near-term changes in influenza activity and bridge the gap between reported incidence data and long-term seasonal forecasts.

ILINet Data

Data on the weekly proportion of people seeing their health-care provider for influenza-like illness (ILI) is reported through the ILINet System for the United States as a whole and for each HHS health region. These data can be accessed directly from CDC. Alternatively, the R package cdcfluview (available from CRAN or GitHub) can be used to access the data as shown in the following example

# Option 1: Install from CRAN
install.packages("cdcfluview")

# Option 2: Install from GitHub (most up-to-date version)
devtools::install_github("hrbrmstr/cdcfluview")

library(cdcfluview)

# National ILINet data for 1997/98 - 2017/18 seasons
usflu <- ilinet(region = "national", years = 1997:2017)

# HHS Regional ILINet data for 1997/98 - 2017/18 seasons
regionflu <- ilinet(region = "HHS", years = 1997:2017)

Please note that while cdcfluview accesses publically available CDC data, it is not produced, maintained, or endorsed by the CDC.

Additional Data

Teams are welcome to use data sources for model development beyond ILINet - possible additional data sources include but are not limited to:

FluSight Package

The FluSight R package contains functions to help create and format forecasts, read and verify forecast CSVs, and score forecasts. These are the functions that will be used at CDC to verify and score submitted forecasts. Teams are welcome to use these tools to ensure their forecasts fit the required template and score their forecasts prior to receiving official scores from CDC

The package can be downloaded from GitHub.

# Install and load package
devtools::install_github("jarad/FluSight")

library(FluSight)

# Read in entry CSV
entry <- read_entry("your_csv.csv")

# Verify entry
verify_entry(entry)
verify_entry_file("your_csv.csv")

# Create file of observed truth from CDC surveillance data
truth <- create_truth(fluview = T, year = 2018)

# Expand observed truth to take into account additional bins - 1 bin for weeks, 5 bins for percentage
exp_truth <- expand_truth(truth, week_expand = 1, percent_expand = 5)

# Score a weekly entry against the observed truth
exact_scores <- score_entry(entry, truth)
expand_scores <- score_entry(entry, exp_truth)
Guidance documents

The preliminary guidelines for the 2018-19 influenza forecasting challenge can be found here

The preliminary submission document for the 2018-19 influenza forecasting challenge can be found here

Please submit one copy of this form for each model you submit. Please send an updated form if you update your model.