Positive test (State)
Why Use This Data Source In Your Models?
The COVID‑19 pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). From the business focused data scientist there are three main measures to inform the economic impact- cases diagnosed, available hospital beds and recovered cases.
Seasonal Impact
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Automated Data Profiling
Suggested Treatment:
Grain Transformation:
Source:
The COVID Tracking Project
Release:
COVID-19 Positive Test (State)
Units:
Tests
Frequency:
Daily
Available Through:
03/07/2021
Suggested Treatment:
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Order Norm transformation, provides the best normality, the Boxcox variable will also perform well.
Grain Transformation:
Data is unable to be distributed by time or geography. The roll up method used is Max.
Auto Correlation Analysis:
Data shows auto correlation indicating a need for differencing
The ACF indicates 2 order differencing is appropriate.
Further differencing is reccommended
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.37 p-value = 0.01 indicates that the data is not stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.92 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of -0.15 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.03 with a p-value of 0.65 inidcates the data is unimodal
Statistics (Pearson P/ df, lower => more normal)
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
Data Notes:
As of March 7, 2021 The COVID Tracking Project are no longer collecting new data
Citation:
The COVID Tracking Project, Data API; https://covidtracking.com/data/api, Retrieved daily.