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.
Cumulative ICU (State)
The COVID Tracking Project
COVID-19 Cumulative ICU (State)
The data shows auto correlation, seasonality and a non-normal distribution. The data should be differenced and seasonally adjusted. While the Order Norm transformation, provides the best normality, the Untransformed variable will also perform well.
Data is unable to be distributed by time or geography. The roll up method used is Max.
Data shows auto correlation indicating a need for differencing
The ACF indicates 1 order differencing is appropriate.
Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.20
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.06 p-value = 0.10 indicates that the data is stationary.
The Shapiro-Wilk test returned W = 0.90 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of -0.13 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.08 with a p-value of 0.00 inidcates the data is multimodal
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
As of March 7, 2021 The COVID Tracking Project are no longer collecting new data
The COVID Tracking Project, Data API; https://covidtracking.com/data/api, Retrieved daily.
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