Covid-19 data
Source: By Joy Merwin Monteiro: The Indian Express
The second surge in the pandemic has been rather alarming. The number of positive cases has been rising in much of the country, in urban as well as rural areas, since the middle of March. A silver lining is that the numbers are declining in the three metros that first faced the surge, Delhi, Mumbai and Pune, and possibly in some states too. Is this decline real, or is it simply an artefact of fewer tests being performed, or incorrect or missing numbers? The answer is very important of course, since the easing of restrictions and the resumption of normal activities critically depends on it. And the best answer would be the one most driven by data, rather than by anecdotal evidence and wishful thinking.
Do we have enough data?
The current pandemic is one of the most data-rich global health crises in history, and this is true for India as well. As concerned citizens, we would like to use this data to answer questions such as:
Do declining case numbers actually mean a decline in the epidemic?
What are the best indicators for assessing a decline or flare-up?
How effective is the test-trace-treat strategy in our city?
For nearly every district and for all major cities in India, we have information all through the pandemic about three quantities: daily numbers of tests, identified positives, and deaths. With these numbers, we can create two meaningful quantities: 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 should be tested?
In a standard Test-Trace-Treat strategy, this can be answered as follows: Let one individual, let’s call him Mr X, test positive (Test stage). Next, health workers will speak to Mr X and find out everyone he has been in contact with (Trace stage). Suppose Mr X 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).
In an ideal world, all these 20 contacts should be tested, but constraints of time, money, people and equipment usually do not allow that. 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, low test positivity indicates that the epidemic is likely to be contained effectively.
Is test positivity always a good 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 X went to the market and caught the infection from who-knows-where or passed on the infection to God-knows-who, the test positivity may not reduce even when the number of tests is increased. This is a bad situation of course, since it is indicative of widespread infection, with many asymptomatic cases.
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.
Can we improve on test positivity as an indicator?
As we saw, test positivity by itself does not give enough information to understand the progress of the epidemic at all stages. Moreover, there can be many infected people who are asymptomatic and who therefore remain undetected. Such asymptomatic people can infect others, and it is necessary to estimate their number to understand the progression of the epidemic. That is where the Case Fatality Rate (CFR) comes into play.
To understand the use of CFR, we need to introduce another indicator called the Infection Fatality Rate (IFR). CFR tells us how many of the identified positive persons have died. But there will be many unidentified positive persons as well. 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).
Let us say the IFR for Covid-19 is 0.3%. This 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. The implication is that 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. This is a hypothetical example, but it illustrates the fact that the higher the CFR, the greater the number of unidentified infections.
So, what have we learned?
A simple analysis of more or less readily available pandemic data tells us the following:
High test positivity and high CFR: 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: 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: The epidemic is likely under control.
Low test positivity and high CFR: Provides contradictory information, and may point to attempts at keeping the test positivity artificially low.
