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Reading Covid-19 data properly
Read more on - Polity | Economy | Schemes | S&T | Environment
- The story: India's second surge in the Covid pandemic was shocking. The number of positive cases rose across urban and rural areas, since the middle of March 2021. A good thing was that the numbers started declining in the three metros that first faced the surge, Delhi, Mumbai and Pune, and possibly in some states too. Questions arose if the decline was real, or just a statistical jugglery?
- The data game: The pandemic of 2020-21 has become the most data-rich global health crises in human history. For every district and for all major cities in India, information about three quantities was available: (i) daily numbers of tests, (ii) identified positives, and (iii) deaths. With these, two meaningful quantities can be created - the ratio between the identified positives and tests, which is called “test positivity”; and the ratio between deaths and identified positives, called Case Fatality Rate (CFR). The deaths on any day are from positives identified over a fortnight or so before that day, which have to be considered in obtaining the CFR.
- How many to test: In a standard Test-Trace-Treat strategy, the approach is - Let one individual, let’s say Mr A, test positive (Test stage). Next, health workers will speak to Mr A and find out everyone he has been in contact with (Trace stage). Suppose Mr A has come in contact with 20 people. He spent a lot of time with 5 of them (high risk) and briefly met the others (low risk). All these 20 contacts should be tested, but constraints of time, money, people and equipment come in the way. If only high-risk contacts are tested, many are likely to be positive and therefore the test positivity is likely to be very high. If more and more contacts are tested, the likelihood that many contacts are positive decreases (since low-risk contacts may not get infected). Since identified positive persons cannot infect others because they are isolated, a low test positivity indicates that the epidemic is likely to be contained effectively.
- Test positivity indicator: Test positivity is a good benchmark at the earliest stages of the epidemic. Once the infection has spread sufficiently through the population, the chain of infection is no longer clear. Then if Mr A went to a seminar and caught the infection from someone or passed it to someone, the test positivity may not reduce even when the number of tests is increased. This is a bad situation since it is indicative of widespread infection, with many asymptomatic cases.
- Decline measurement: Test positivity can be also used as an indicator to mark the decline of the epidemic. As the epidemic ends, the number of tests being done will decline and the test positivity is likely to stay constant or decline as well. However, as the number of tests decline, if an increase of test positivity is observed, then the reduction in the number of tests should be taken as a matter of concern. It is then necessary to increase the number of tests.
- Making improvements: Test positivity by itself does not give enough information to understand the epidemic's progress at all stages. There can be many infected people who are asymptomatic and who remain undetected. Such people can infect others, and it is necessary to estimate their number. Here the Case Fatality Rate (CFR) us useful.
- The Infection Fatality Rate (IFR) is needed now. While CFR tells how many of the identified positive persons have died, but there will be many unidentified positive persons as well.
- The IFR is the ratio of deaths to the total number of infected persons, identified or unidentified. IFR is usually much lower than CFR — because of the large number of unidentified positive persons — and can be considered a constant for a disease for a given age group. There are ways to estimate the total number of positive persons in a locality (Pune’s serological survey did this in certain electoral wards). If the IFR for Covid-19 is 0.3%, it means out of every 1,000 infected people, 3 are likely to die. If the CFR is 1%, out of 300 identified infections, 3 people have died. So around 700 Covid-19 positive people have not been identified. If CFR were 2%, 6 people would have died, which would require an underlying population of 1,400 infected but undetected people. The higher the CFR, the greater the number of unidentified infections.
- Summary: High test positivity and high CFR would mean testing needs to be ramped up and door-to-door surveys may be necessary to identify infections before the disease turns rampant. High test positivity and low CFR would mean the infection has spread through the population, but the health system is responding well, keeping deaths low. The number of unidentified positives is not high. But this could also imply under-reporting of deaths, which needs to be investigated. Low test positivity and low CFR would mean the epidemic is likely under control. Low test positivity and high CFR would mean contradictory information, and may point to attempts at keeping the test positivity artificially low.
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