SARS-COV-2 / COVID-19
Leveraging COVID-19 data aggregated by Johns Hopkins University, Lyteworx produced several charts that are automatically generated on a daily bases that reflect the most current data. The format of the data is manipulated to easily take advantage of the charting capabilities found in the popular Python package Plotly. The charts are generated with fresh data daily using the automation functionality native to Github called Workflows.
Clinical Age Histogram - This chart is designed to show if there is an age that is more common for those who test positive for COVID-19. This dataset is very limited to a small group in California early on. It shows that COVID-19 is spread pretty equally among all ages.
Clinical Temperature Histogram - This chart is designed to show if temperature is a metric for a positive COVID-19 test. This chart shows that many people had high temperature and were negative for COVID-19 and that there were many that had normal temperatures and tested positive.
Histogram
Symptom Correlation
Covid-19 Negative Symptom Correlation Matrix - This chart shows the correlation for symptoms among those who tested negative for COVID-19. It is practically identical to the next chart for those who tested positive.
Covid-19 Positive Symptom Correlation Matrix -This chart shows the correlation for symptoms among those who tested positive for COVID-19. It is practically identical to the next chart for those who tested negative.
Covid-19 Symptom Correlation Matrix -This chart shows symptom correlations for those who tested positive and those who tested negative on a single page.
United States
United States Bubble Chart - This chart shows confirmed cases versus deaths where the size of the bubble is the morality rate and the bubbles are colored by state.United States Cases Per TestThe purpose of these charts is to know that the rise in cases is not due to an increase in tests given. It answers the question, “Is the virus spreading or have we just been testing more?”These are four charts on one page. The top shows the number of cases, the number of tests administered, and the rate of cases per test given. In other words, how many cases are confirmed for each test given. The bottom chart combines these three plots in one chart.
United States Confirmed Cases Growth Rate Map - A choropleth showing the day to day growth rate by state. The rate is shifted by one so that zero is a steady state (not positive or negative).United States Confirmed Cases MapA country choropleth showing the total cases by state. The scale is max at the 95th percentile to give more color to countries with fewer cases. NY, CA, FL, and TX have many more cases than the rest of the country.
United States Confirmed Cases Per Million Map - A case map normalized for population. The color scale is maxed at the 85th percentile. Even though CA has more cases than FL it is less on this map because the population is higher.
United States Confirmed Cases Rolling 14-Day Average Growth Rate Map - A map showing a 14-day rolling growth rate of cases. The scale has been shifted by one so that it is more intuitive with 0 meaning no growth. 14-day rolling average is used as a metric to allow for the two week virus incubation time, if asymptomatic spread is a concern. This method should allow for report time irregularities (report lag time) because it is a rolling average. Since mid-July the country as a whole has been at a decline or near a steady state; aka not wide-spread
Global Confirmed
Global Confirmed Cases Bubble Chart - This animation shows the growth of cases of COVID-19 versus number of deaths from January 22, 2020 to the current time. The colors represent the continent of the country and the size of the circle shows the mortality rate.
Global Confirmed Cases Bubble Chart Per Continent - This animation shows the growth of cases of COVID-19 versus number of deaths from January 22, 2020 to the current time. The circles are colored by country and each facet is a continent.
Global Confirmed Cases Map - A global choropleth showing the total cases by country. The scale is max at the 99th percentile to give more color to countries with fewer cases, but since the US, India, and Brazil have so many more cases than the rest of the world the map is not very interesting.
Global Confirmed Cases Rolling 14-Day Average Growth Rate Map - A global choropleth showing a 14-day rolling growth rate of cases. The scale has been shifted by one so that it is more intuitive with 0 meaning no growth. 14-day rolling average is used as a metric to allow for the two week virus incubation time, if asymptomatic spread is a concern. This method should allow for report time irregularities (report lag time) because it is a rolling average.