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Tuesday, April 20, 2021

Explained: A year on, making sense of India’s Covid-19 data

Coronavirus in India: It is not yet clear why the numbers declined for five months and then rose again. An analysis of data leads to more confounding questions, and some contradictory findings; we have to rely on many conjectures.

Written by Dr Tushar Gore | New Delhi |
Updated: April 8, 2021 9:44:53 am
coronavirus, coronavirus in india, covid 19 in india, coronavirus cases, coronavirus cases in india, coronavirus deaths in india, coronavirus vaccine, covid 19 vaccine, coronavirus india dataA sample being collected during a serosurvey in Delhi in August. A serosurvey predicts the actual prevalence of disease in a population. (Express photo by Abhinav Saha/Archive)

The coronavirus infection number has started to rise again. Previously, it had peaked in mid-September and declined steadily until February this year, until the current resurgence in a few states. The daily count of cases is back in the 70,000 range, while about 400 deaths in a day are being reported now. In a country that counts roughly 27,000 deaths a day as a baseline, the coronavirus death numbers do not seem very alarming.

The reasons for the five-month decline starting in September, and the current resurgence, are not very well understood. In fact, an analysis of the data leads us to more confounding questions, and some contradictory findings.

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India’s coronavirus numbers: Cases & deaths

The common metrics to assess the spread of Covid-19 are the numbers of cases and deaths. Although India has over 12 million cases now, the cases as a proportion of population are at 9.02 per thousand. India is outperforming many countries on this metric.

Coronavirus deaths in India are low no matter which parameter is assessed. The best parameter is Infection Fatality Rate (IFR). It measures deaths as a proportion of total infections (and not just confirmed infections). Total infections is estimated through serosurveys. The IFR in India is 0.08% according to initial government surveys. The US estimate is about 0.6%, about 8 times that of India. Almost half the difference can be attributed to India’s relative younger population. The reasons for the remaining difference are unknown.

Actual disease prevalence

To understand whether the low case numbers are real or due to lower testing, metrics such as Test Positivity Rate (TPR) are relevant. TPR is the ratio of positive results and total number of tests. A TPR below 5% is considered the benchmark for appropriate level of testing. The chart shows a 7-day rolling average TPR for US and India since April 1, 2020. India’s performance on this metric seems adequate barring the months leading up to the September peak.

An overlooked indicator is the actual prevalence of the disease in the population. This number is much more than the total cases confirmed by test results. It is determined through serosurveys.

The latest data indicates about 21% of the population was affected by Covid by December end. Comparisons with other countries are difficult because not all countries run countrywide surveys that match the Indian survey time-frame. An estimate for the US based on numbers in different states indicates a range of 15-20%. By this metric, India appears to have one of the highest spreads in the world.

This number can also be used to assess testing adequacy. A 21% spread equals about 280 million infected people. By December 31, the total number of cases identified were 10.3 million leading to ratio of 27 for total infected to total detected. In the US, this same ratio is around 2.5 (using 15% as prevalence for December-end). Therefore, in India for every detected case, 26 were missed. This assessment indicates testing has been very inadequate. It also raises the confounding question —why was the TPR low if so many people were infected? Also, if testing wasn’t the control mechanism for disease spread, then what led to the decline?

coronavirus, coronavirus in india, covid 19 in india, coronavirus cases, coronavirus cases in india, coronavirus deaths in india, coronavirus vaccine, covid 19 vaccine, coronavirus india data Test positivity ratio: 7-day rolling average

This also indicates that the decline in cases in India from mid-September was a true decline and not due to reduced testing.

A year into the pandemic, cases and deaths are low in India. The spread of the disease, however, is one of the highest. Testing appears to have missed many infections. It appears that the disease is widespread but is a milder form, one that escapes testing and does not cause serious illness.

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Possible explanations

The daily number of infections (not just positive results, but total infections) is dependent on three factors:

* The number of infectious individuals who can transmit the disease

* The rate of transmission. This in turn depends on several parameters, including virus strain, susceptibility of population and exposure time.

* The number of susceptible individuals in the population. More people available for infection would increase new infections.


Different strategies are deployed to control one or more of these three numbers in order to bring down the daily count of infections

STRATEGY 1: Reduce the number of infectious people in the population

This can be done through lockdown, which reduces the circulation of infected individuals. It can also be done through testing and quarantining, which means identification of infected individuals and their isolation.

STRATEGY 2: Reduce rate of transmission

Lockdown plays a role here as well, by reducing contact between infectious and susceptible individuals. Masks and physical distancing also help in this objective. So does adequate ventilation, because this dilutes the virus in the air surrounding a susceptible individual.

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STRATEGY 3: Reduce the number of susceptible individuals

Here, vaccinations play an important role.

The three factors act together. In a situation wherein the infectious and susceptible population are high, reduction of cases is possible by controlling the rate of transmission (through distancing measures, for example). As the infectious population goes down due to better control, or the number of susceptible individuals goes down due to increased immunity, the contact between individuals, and consequently the rate of transmission, can be allowed to increase (relaxation on assembly, for example) without an upward effect on the new cases.

In India, serosurveys indicate that testing missed almost 24 out of possible 25 exposures. Consequently, testing and quarantining lever has not been the most effective, and is ruled out as a possible reason for the decline.

Explained, unexplained

LOCKDOWN: It doesn’t explain the decline in the number of cases since strict lockdown ended way back in May. In the initial months, however, it did help the health system to prepare itself to deal with increasing cases.

MASKING, PHYSICAL DISTANCING: After the most recent round of relaxation in restrictions in public transport and gatherings, cases have increased in some states, indicating that these restrictions could have been a factor in controlling the spread earlier. However, many of these measures were being relaxed gradually from June onwards. It doesn’t explain why the cases peaked in September, and did not continue to increase further. Nor does it explain why the recent increase in cases is still concentrated in a few states.

‘HERD IMMUNITY’ IN SOME CITIES: A serosurvey in August in Mumbai indicated 45% prevalence in slums and 18% in other areas. While these numbers are not near the threshold that is generally considered as herd immunity level, these could have potentially explained the decline when coupled with a low rate of transmission. But then, the current rise is maximum in these very cities, like Mumbai and Pune, which had reported high levels of immunity. Also, rural areas, which had lower prevalence rates earlier, are still showing a lack of any major rise.

PRE-EXISTING IMMUNITY: This is often referred to as the hygiene hypothesis, proposing that the Indian population has better immunity against coronavirus because it has been exposed to a lot of other infectious diseases earlier. This is still an untested hypothesis.


The lack of spread in rural areas in the absence of herd immunity could be explained by lower population density and higher natural ventilation. It is possible this rate is low enough to compensate for the higher susceptible population.

A decline in cities could be explained by a decrease in the susceptible population due to high disease spread, and the resulting acquired immunity. The recent surge then becomes difficult to explain unless re-infection rates are high. Re-infection is still poorly understood and it is possible that a “milder” initial exposure still leaves the individual susceptible to recurring exposure. A helpful analogy is the common cold, a mild reaction that does not really provide immunity from recurring infections. Another possibility is that a new strain with a higher transmission rate is the driver of the latest surge. At this point, however, it is a theory without any detailed evidence in support.

The above discussion indicates that we have to rely on many conjectures beyond the basic tactics/levers to explain the timing of decline, lack of spread in rural areas, and resurgence in some areas. With lack of certainty on factors contributing to the decline and the low death rates, it will be premature for individuals to breathe a sigh of relief and resume activities at a pre-Covid scale.

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