How Predictable Death by COVID-19 Incidence?

I downloaded from the European Centre for Disease Prevention and Control website the daily database constituting the number of new cases reported and death worldwide due to COVID-19. The database had 13,623 daily records from December 31, 2019 to April 28, 2020. I summed records by country and filtered the number of incidences avoiding outliers using a statistical formula.  The filtered record shows a list of 136 countries that had an incidence in 314,099 persons, of which 7,866 persons died. It shows that 2.5 percent of infected people had already died and there is a possibility of other deaths too, which the time will tell. Israel had a maximum of 15,598 incidences, and the British Virgin Islands had a minimum of 6 incidences. Algeria had a maximum of 437 deaths and 13 countries each had one death.

Then, I calculated the Pearson correlation coefficient between the number of incidence and deaths for the filtered records.  The purpose of removing outliers was because the Pearson correlation coefficient works when the datasets are close to normal distribution. The analysis shows a correlation coefficient of 0.677 which was significantly different from zero (p=0.01). This suggests that there is a moderate chance of death if one is infected with COVID-19. A regression analysis was also conducted to predict death based on incidence, which gives an R square value of 0.459 indicating that nearly half the variation in the number of deaths is explained by the model. A significant coefficient indicates that the number of incidences significantly contributes to the model. For every 1000 incidences 18 deaths are likely.

Graphics Visualizing Target Population

Graphics is an important form to visualize one data from others. This technique is useful to visualize the target population for any purpose. For example, I referred to Nepal’s population census 2011 data to get population values to visualize the number of male household heads from urban areas of Nepal belonging to the age group of 50 years and above. I used four criteria to filter and get target population data – male, age group, administrative setting (urban), and position or role in a household (household head).

An outermost blue circle shows the total population of Nepal, which is 26,494,504. It is followed by a red circle showing the male population which is 49 percent of the total population. An inner third yellow circle shows the male population aged 50 years and above, which is 15 percent of the total male population. An inner dark ash-colored circle shows the urban male population aged 50 years and above, which is 15 percent of the total males aged 50 years and above. Lastly, an innermost green circle shows the number of urban male household heads aged 50 years and above, which is 81 percent of total urban males aged 50 years and above and 1.9 percent of the total male population in Nepal.

These data are visualized in both stacked Venn and horizontal hierarchical diagrams in Figures 1 and 2 respectively.

Figure 1: Target population of urban male household heads aged 50 years and above shown in a stacked Venn diagram

Figure 2: Target population of urban male household heads aged 50 years and above shown in a horizontally hierarchical diagram

In a nutshell, visualizing filtered data in different diagrammatic forms is a cool way.