READY SIGNAL CONTROL DATA

%UNWEIGHTED ILI - National Summary

Why Use This Data Source In Your Models?

Percentage of visits for influenza like illness reported by sentinel providers. Information on patient visits to health care providers for influenza-like illness is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). ILINet consists of approximately 3,200 outpatient healthcare providers in all 50 states, Puerto Rico, the District of Columbia and the U.S. Virgin Islands reporting over 36 million patient visits each year. Each week, approximately 2,000 outpatient healthcare providers around the country report data to CDC on the total number of patients seen and the number of those patients with influenza-like illness (ILI) by age group (0-4 years, 5-24 years, 25-49 years, 50-64 years, and ≥ 65 years). For this system, ILI is defined as fever (temperature of 100°F [37.8°C] or greater) and a cough and/or a sore throat in the absence of a known cause other than influenza. Sites with electronic records use an equivalent definition as determined by state public health authorities.

Seasonal Impact

Instantly Download this Data Using Our Automated Feature Engineering Tool.

Automated Data Profiling

Ready Signal automatically profiles each data set and offers up suggested industry standard data science treatments to utilize with these data in your models.

Suggested Treatment:

The data shows seasonality. The data should be adjusted. While the Boxcox transformation, provides the best normality, the Order Norm variable will also perform well.

Grain Transformation:

Data is unable to be distributed by time or geography. The roll up method used is Sum.

Auto Correlation Analysis:

Data does not show strong auto correlation indicating no need for differencing

The ACF indicates 0 order differencing is appropriate.

Further differencing is reccommended

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.04 p-value = 0.10 indicates that the data is stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.79 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 1.84 indicates the data are substantially skewed.

Hartigan's dip test score of 0.01 with a p-value of 0.96 inidcates the data is unimodal

Statistics (Pearson P/ df, lower => more normal)

No transform

20.75

Box-cox

1.18

Log_b(x-a)

1.71

sqrt(x+a)

3.35

exp(x)

48.83

arcsinh(x)

2.64

Yeo-Johnson

1.26

OrderNorm

1.22

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal and Trend Decompostion

Designed For Data Scientists and Analysts

400+ Data Sources

Use our platform to aggregate, normalize, and profile open source and premium control data. Spend less time finding and wrangling data, and more time building efficient and feature-rich machine learning data pipelines.

Data Science Treatments

Instantly apply industry-standard data science treatments and transformations, including (but not limited to) Differencing, Lead/Lag, Box Cox. Easily manipulate data across different time and geographic grains.

Auto Discovery

Our Patent Pending iterative testing engine allows you to upload your target variable, and the platform will test for possible statistical relationships across all available data sources. Saving you time and removing analyst bias.

Data Ingestion

Easily integrate your Ready Signal data to the data science platform of your choice. Connect directly to Ready Signal through our API or using one of our pre-built data connectors or download directly in Excel or CSV format.