The RED line for India is proof that the deaths in India due to COVID-19 are at par with global trends for other countries. Taking the day-to-day data for cumulative Deaths per 1000 InFections (measured real data), India is showing a trend that is higher than that for Germany, South Korea, Australia, Canada, China and the US. It is lower compared to France, UK, Spain and Italy. So, it would be wrong to conclude that India has lower deaths than other countries due to inbuilt immunity, BCG vaccine or geographic temperature.
I have brilliant friends around the world who have been thinking about the impact of COVID-19 on India. Initially, we debated on the low number of inFections in India. Eventually we all agreed that the published data on inFections in India is incorrect, since the number of tests/million is very low. No dispute here.
However, questions started pouring in that if the detected inFections are low in India, the data should at least show up in the death statistics. The logic was that there is bound to be lot more undetected inFections in the country and some of these folks would get really sick and would have to eventually reach a hospital for care. Sadly, some of them would succumb and this should show up in the mortality data.
As on 11 Apr 2020, the published data from JHUM (John Hopkins University and Medicine) for India indicated 288 deaths. This is a rather low number for the second most populous country in the world.
We examined many theories floating around. It was speculated that the Indian people had fantastic immunity. Some speculated that Indian’s had resistance because of the BCG vaccine, given for tuberculosis (TB), since it is part of the childhood immunization program. Others felt that it was because of the higher temperature in India, especially due to the onset of summer.
One needs to examine data carefully before drawing conclusions.
As on date (11 Apr 2020) it is tempting to compare the 288 deaths in India (1366 million people) with that in other countries. For example, Italy (61 million people) has 19,648 deaths. But, keep in mind that the exponential growth of inFections in India stared on 22 Mar 2020 (Day-1 for India), while in Italy it started on 26 Feb 2020 (Day-1 for Italy). We are off by a month, if we just look at the data based on the calendar. I have found that when inFections reach about 400 in any country, the exponential growth phase begins. So, taking that point as Day-1, we can transform all the country data to a common starting origin. We can then begin to compare for an equal time lapse.
In addition, we have to remove errors in sampling. An effective way to examine the data is to calculate the day-to-day metric of “measured deaths per 1000 inFections”. By doing this we can remove some of the errors in sampling. For example, if we claim that the number of people tested is not sufficient, then there is a sampling error in the inFection data. But, if we take the ratio of such inFection data and match it with measured deaths, the errors would mathematically cancel out (at least to a large extent). Implicit in this argument is the fact that measured deaths will arise from measured inFections.
Therefore, the graph at the very top, is quite stunning in its result. It visually shows that India is following the death trend for other countries. It is somewhere in the median position. Its trend is actually higher than that for Germany, South Korea, Australia, Canada, China and the US. It is lower compared to France, UK, Spain and Italy. Variations in the relative positions in the graph can be attributed to the accessibility and quality of health care in a given country.
The graph also reveals another startling fact. It is telling us that the death/inFection ratio can skyrocket all of a sudden. For example, France and India have similar datum values until Day-15. Then France suddenly shoots up. Why is this? I attribute it to the inability of the medical care to cope up with the critical cases.
India has data for 21 days during its exponential growth (22 Mar 2020 to 11 Apr 2020). I have computed the average for the first 21 days for all the countries that I have analyzed. India has an average ratio of 26.37 compared to 22.87, which is the average for all the countries. The graph below shows this visually.