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ILINet Flu Surveillance Data Interactive Visualization

✓ Published1🌍 Public
Yyuzhang21
Last edited Oct 17, 2019
Created on Oct 16, 2019

This interactive visualization displays ILINet flu surveillance data, showing aggregated weekly influenza-like illness percentages across US states from 2015 onward. The interface combines four coordinated views: a line chart colored by year, a scatter plot encoding patient counts through circle size, a horizontal bar chart of state averages, and a choropleth map. Users select which data attribute to visualize through a dropdown menu. Built with React and D3 v5, the visualization uses d3.scaleOrdinal for year-based coloring, d3.scaleLinear for axes, d3.line with curveNatural for smoothed trajectories, and d3.geoAlbers with topojson for the US map rendering.

AI-generated description

This visual use D3.js and React to display a scatter plot using multiple channels and add a menu to choose different fields in the dataset for visualization. The Data used here is the ILINet Flu Surveillance Dataset.

In this ILINet Flu Surveillance dataset, we have ILITOTAL to indicate the number of patients recorded by the network. The %UNWEIGHTED ILI is actually the ratio to total number of patients served. Clearly this value could be large if the base numbers are small. In the single plot, we show all data by the week of the year since 2015. Each data point represent a state on the same week, different color indicates different years. We also assigned the ILITOTAL number to the size channel, which maps the bigger size to larger number of patients. From the plot we can see that the big size mostly happens at the end of the year till at the beginning of the year, which is the flu season. Although the %ILI data indicates similar results, the size gives more sharper results. Also the points at the bottom of the weeks are all smaller, which allows us to visualize the bigger patients’ number more easily on the plot, it happens on both end of each week’s line because the data with smaller number of patient are generally ignorable.

This plot is comparable with original visualization that uses the vega-lite-api.

The original data comes from the webpage: CDC FluView Interactive.

MIT Licensed

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