During these dramatic weeks of COVID-19 pandemic, my thoughts go to all people and families that are suffering beacause of the disease. Also, I think we should really thank all healthcare professionals that are currently fighting against the virus.
These visualizations are built on the top of the daily COVID-19 data obtained from John Hopkins University, and plotted using the Covid19CountsPlot R library (https://github.com/dami82/Covid19CasesPlot). Briefly, instead of visualizing cumulative data, here I am showing day-by-day numbers. The goal is trying to understand when selected countries (or US States) of interest are reaching an apex or a plateau. Also, I hope to gain insights about time relationships/gaps between number of new cases and COVID-19 related deaths.
Figure 1. xy-plots showing the number of new daily COVID-19 cases (orange points, left y-axis) and number of daily deaths due to COVID-19 (blue points, right y-axis) with respect to time in a set of Countries of interest. Trendlines were computed by LOESS. Data provided by John Hopkins University. Note: U.S. plot y-axis on a different scale.
Top-20 US States by cumulative number of confirmed cases
Figure 3. xy-plots showing the number of new daily COVID-19 cases (orange points, left y-axis) and number of daily deaths due to COVID-19 (blue points, right y-axis) with respect to time in a set of US-states of interest. Trendlines were computed by LOESS. Data provided by John Hopkins University. Note: Plot have y-axes on independent scales.
More info are available at:
Day-by-day COVID-19 Patterns: https://www.data-pulse.com/dev_site/covid19/
GitHub (R lib): https://github.com/dami82/Covid19CasesPlot
JHU COVID-19 data: https://systems.jhu.edu/research/public-health/ncov/