Viziflu: Initial Release

andrea b
Published in
4 min readDec 12, 2018


Viziflu is a visualization tool that displays multiple predictions about the timing of “Peak Week,” the week with the highest predicted number of flu cases. By displaying the outputs of multiple influenza models and allowing users to compare the uncertainty across those models, Viziflu can help make influenza forecasts more actionable for decision-makers.

How to read Viziflu output

This article was co-authored by Andrea B., Dylan G., and George S.

IQT Labs is pleased to release Viziflu, an open-source data visualization tool for exploring the output of multiple influenza forecasting models (e.g. models developed by a variety of private parties to predict the timing of onset, peak number of cases, outbreak severity, and duration of seasonal influenza outbreaks). Viziflu runs in your browser and shows the forecasted temporal progression of seasonal influenza in the United States. The display highlights predictions about the timing of “Peak Week,” the week during which the highest number of flu cases is expected to occur.

Infectious disease forecasting is becoming an important analytic tool for managing outbreaks, such as seasonal influenza outbreaks. As described in the FluSight documentation (FluSight is an annual forecasting challenge, hosted by Centers for Disease Control and Prevention (CDC)):

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.

To the untrained eye, the output of forecasting models can be challenging to interpret, leaving key messages unclear. Therefore, improved approaches to visualization are essential for advancing the utility of forecasting for operational decision making.

Viziflu is an open source tool for visualizing influenza forecasts. This screenshot shows forecast data from the 2017–2018 flu season.

Viziflu allows the user to see several important aspects of seasonal influenza forecasts in one graphical depiction:

  • Timeline: the duration of the flu season is represented as a horizontal timeline from October through May.
  • Multiple models: Multiple forecasting models are listed alphabetically on the left side of the display. The set of models currently shown were submitted by teams participating in the CDC FluSight Challenge.
  • Predicted probabilities of each model: A color gradient indicates each models’ predicted probability that flu will peak in each week of the flu season.
Grey indicates a (predicted) 0% chance of Peak Week; light yellow indicates less than a 5% chance; and dark red indicates a greater than 50% chance.
  • Previous forecasting results: As the flu season progresses, the models are updated week-by-week. Along the bottom of the Viziflu display, a series of numbered tabs allows you to toggle through these revised versions, which allows you to see how they evolve over the course of the flu season. A vertical black line indicates when in the season the forecasts were submitted to CDC.

Viziflu was developed following a B.Next workshop and paper on Technology to advance infectious disease forecasting for outbreak management (paper available soon) that identified the need for improved communication about public health data, including new visualization techniques to help convey risks and uncertainties to decision-makers and the public. Viziflu is written in JavaScript and built using a number of open source libraries and resources, including D3.js, Colorbrewer 2.0, and visavail.js. The CDC’s Influenza Division provided domain expertise and guidance on the visualization design.

If you would like to explore Viziflu peak week visualizations, you can click here, to access a fully working demo of the 2017–2018 season or here, to see early season forecasts for the current (2018–2019) season.

Although Viziflu was designed for seasonal influenza forecasts, we imagine the tool could be adapted for other applications where users would benefit from the ability to compare forecasted probabilities of occurrence over time, as predicted by multiple models. If you are interested in using or adapting Viziflu, please check out this code repo and readme, which are available to the public for reuse/modification under the Apache 2.0 License.

Please note the forecasting models presented in ViziFlu are not official CDC forecasts and are not endorsed by either CDC or IQT Labs.

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andrea b

Andrea is a designer, technologist & recovering architect, who is interested in how we interact with machines. For more info, check out: