Deaths (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 Deaths (State)
Units:
Persons
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 Arcsin 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 = 0.26 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 0.00 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.04 with a p-value of 0.22 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, Key Metrics by State; https://covidtracking.com/data/charts/all-metrics-per-state, Retrieved daily.