By Rai Chowdhary

Last update: May 11, 2020

The nonstop drumbeat about numbers and how people’s lives are getting affected is deafening to say the least. The whole environment is full with discussion points, and an endless parade of commentators who bring on guests and push one angle or another.

How is one to peel the onion, and make sense out of so much noise and chaos?

Well it begins with good questions; those that enable us get the right answers. Unfortunately, there is paucity of this when it comes to much of media, and the leaders of several countries.

The key to gaining accurate knowledge is asking the right questions, then probing the answers…

Here are ten key questions I would be asking (picked from a longer list)…

1. Of the population in each area, how many were tested and the basis for selecting these folks for testing?
2. What % were exposed to the corona virus?
   (These may lead you to think we need to test the whole population – which will be very hard to do. However, a statistically sound estimate can be arrived at using a good sampling approach)
3. Testing methods and their error rate (both – false positives and false negatives – see link below)
4. What does each testing method tell us – that the person was exposed? That they were exposed and recovered? Something else?
5. Of those exposed – how many (%) develop symptoms, and what is their personal and work lifestyle like? Are they a health services worker? Smoker?
6. What are the age groups, and the existing conditions for those who exhibit symptoms?
7. Of those who show symptoms – how many (%) require hospitalization, and how does that compare with hospitalization rates for other diseases we know of?
8. What % of those who are hospitalized recovered and what % did not? How does that compare with other types of flu?
9. Of those who supposedly succumbed to the virus, how many were really examined to establish that as the primary cause of death vs. the contributing condition (see link below for more information on inconsistencies in determining the cause of death)
10. Comparison of the types of antibodies found in those that survived vs. those that succumbed

I present these to help you expand your critical thinking and realize that we need to examine data carefully rather than accepting it at face value. You may have another perspective and questions you would like to ask – feel free to share these to enrich our collective knowledge.

Data with high integrity and fidelity can represent the truth; however multiple true statements can be created from such data. A simple example is a glass is half full; well – it is half empty at the same time.

Testing methods and their error rates

Inconsistencies in determining cause of death

And lest we forget – following the herd does not mean herd immunity, and that all models are wrong – some more than others. 

May truth prevail.

Rai Chowdhary