U.S. under-counts coronavirus cases

By Kent R. Kroeger (March 23, 2020)

A Moscow woman in a medical mask during the coronavirus epidemic (Photo by https://www.vperemen.com; Licensed under the Creative Commons Attribution-Share Alike 4.0 International license)

The Results

With each new day that the WHO reports confirmed coronavirus cases, my original model (Model 1) has increased its ability to explain country-level variation in coronavirus cases. The original March 14th model explained 73 percent of the variance in country-level case numbers and found the following variables to be significant predictors: National income per capita, total population, number of international tourism arrivals per year, percent of annual deaths due to communicable diseases, and an indicator variable for China.

How to read the chart: Countries above the line had predicted values higher than actual values; countries below the line had predicted values lower than the actual values. Countries in bold are significant outliers.
How to read the chart: Countries above the line had predicted values higher than actual values; countries below the line had predicted values lower than the actual values. Countries in bold are significant outliers.
Data Sources: WHO, ourworldindata.org (Analysis by Kent R. Kroeger)

APPENDIX:

Variables

Dependent Variable (Source: WHO): Number of confirmed COVID-19 cases as of 21 March 2020 (log transformed)

Linear regression models

Model 1 (Does not include a control for the number of coronavirus tests; 167 cases)

Regression analysis run in JASP by Kent R. Kroeger
Regression analysis run in JASP by Kent R. Kroeger

Residual plots

Model 1 (Does not include a control for the number of coronavirus tests; 167 cases)

I am a survey and statistical consultant with over 30 -years experience measuring and analyzing public opinion (You can contact me at: kroeger98@yahoo.com)

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