It is a general trend seen that patients who belong to an elderly age group has higher chances of getting into a COVID-19 infection than that of the younger people. ABC News

Welp… let’s see how rusty I’ve gotten with those good ol’ graphs.

With a weak correlation we observe the following trends in the matrix:

  1. With the rise in temperature, the confirmed cases tend to slow down (negative correlation). However substantial proof needs to be added here. I need to circle back to this and analyze the trends for all the days vs temperature.
  2. Median age tends to affect the cases. So for a higher median age of the country cases tends to increase.
  3. Life expectancy also seems to affect the COVID-19 confirmed cases with a weak correlation. The effect is seen more prominent in males than in females.
Which demographic factors play a role in transmission across populations?

Does living in colder weather environment are more prone to covid-19 that those in warmer regions?

Daily Confirmed Cases vs. Temperature (C) – Global Figures – Jan 22-March
Circular Weight Hierarchy Plot of Cities With Highest Covid-19 Cases

Clearly from the above graphs, I’m able to pretty confidently say that countries with the most number of cases have a cooler temperature. I can analyze and confirm the same trends with bar charts.

The temperature Range (-4 C to 17 C) has the highest number of confirmed case counts. Almost majority of the confirmed cases are in this range. Although the spread of Covid-19 is across the whole temperature spectrum, but within this range the spread is observed to be highest.

And as observed in the boxplot plotted over the scatter plot, within three quartiles of temperature (Q1-Q3) majority and highest number of confirmed cases are seen.

There’s a high chance that this information and publicly available datasets in general don’t count for asymptomatic Covid-19 Confirmed cases, the official number of Covid-19 infections can be much higher.

US Health/Demographic Figures

Does population/health demographics of the US affect the spread of Covid-19 or is the spread completely random? Let’s see…

Sources used: US Cov19 Dataset MyrnaMFL, UC-19 USA Facts Dataset, CovCSD : Covid-19 Countries Statistical Dataset

Choropleth Map of Confirmed Cases

The spread of Covid-19 lately has been around the eastern coastal side of US. New York is the major epicenter for US and counties nearby New York have higher concentration of cases than those away from it. Areas around Chicago also have higher case density than other parts of US.

Running Chart Analysis of Cases in US Counties

New York City, Nassau, Suffolk, Westchester have the highest reported cases of COVID-19. Sometime soon-ish I’m going to go over this again with the demographic distribution of these regions to analyze the trends on much better scale.

Is there any spread relation with health indices?

Welp….

Daily Confirmed Cases vs. Traffic Volume – US Jan 22nd to April 14th

None of the figures like Smokers percentage in population, obesity, diabetics tend to affect the spread of COVID-19 Infections in US in general. A certain correaltion is observed with the number of confirmed cases in a county and the traffic congestion present for that county (as of 2020). The correlation for the variables is 0.613053. This might be significant as the quarantine and total isolation of people disallowing personal movement across US Counties was super late in comparison to countries like India/Korea/China/Japan. Hence asymptomatic cases that were carrying the virus might have had spread the same, because the movement wasn’t restricted and the congestion of traffic for each particular county was high.

Wrap Up

Median age tends to affect the cases. So for a city that has a higher median age, the cases tend to increase.

Life expectancy also seems to affect the COVID-19 confirmed cases with a weak correlation. The effect is seen more prominent in males than in females. This weak correlation might highlight a factor related to the medical facilities for the country. Further research as to which I still need to do but I’m super lazy.

With the rise in temperature, the confirmed cases tend to slow down (negative correlation). The maximum number of cases has occurred between a range on temperatures (from -4 to 17 C). The transmissions are substantially more prominent in colder regions and with the increase in temperature, transmissions tend to decrease. Hence, people in colder environments and cooler climates are much more prone to the transmission of Covid19.

None of the figures like smokers percentage in population, obesity, diabetics tend to affect the spread of COVID-19 infections in US in general.

A certain correaltion is observed with the number of confirmed cases in a county and the traffic congestion present for that county (as of 2020). The correlation for the varibles is 0.613053. This might be significant as the quarantine and total isolation of people disallowing free movement across US counties was very late in comparison to countries like India/Korea/China/Japan. Hence asymptomatic cases that were carrying the virus might have spread identically, as the moment wasn’t restricted and the congestion of traffic for those counties tend to be high.

… to be continued.

Leave a Reply

Your email address will not be published. Required fields are marked *