Numbers Matter | Two years, three waves, four lessons

Mar 03, 2022 02:46 PM IST

Here we take a look at the lessons that two years of the pandemic have taught us, and how they may serve us in possible future waves.

Earlier this week, India’s ongoing battle with the global pandemic touched a milestone – the country’s first case of domestic transmission of Covid-19 was detected on March 2, 2020, when two foreign returnees (one in Delhi and one in Telangana) tested positive. In the two years since, the country has recorded nearly 43 million infections and at least 514,000 deaths, with the actual numbers of both figures likely to be far higher.

But the second anniversary comes at one of the better times of the pandemic. (Sanchit Khanna/HT Photo) PREMIUM
But the second anniversary comes at one of the better times of the pandemic. (Sanchit Khanna/HT Photo)

But the second anniversary comes at one of the better times of the pandemic. The third wave of the pandemic, driven by the relatively milder Omicron variant of Sars-CoV-2, has been ebbing for the past few weeks with daily infections dropping consistently, and fast. This Monday, for instance, saw 5,804 new cases of Covid-19 – the lowest in 70 days, and the second-lowest in nearly 650 days.

With an improving pandemic situation, it gives us the chance to look back and learn from the things the country got right in the past two years, and the things that we did not. Here we take a look at the lessons that two years of the pandemic have taught us, and how they may serve us in possible future waves.

Resource management

If we look at the case curve in these three waves in terms of duration, each wave has followed a different trajectory. The first wave, which started March 3, 2020, peaked on September 16, 2020 – taking exactly 198 days from start to peak – making it the longest. But in terms of volume, it was the lowest of the three – the seven-day average of daily cases in this wave touched a high of 93,617. This long-and-flat nature of the first wave is easily explained by the hard lockdown imposed in the country starting March 24, 2020. 

In contrast, the second wave (caused by the Delta variant) started on February 12, 2021. It saw a much more abrupt rise in cases as there were very few curbs in place by the start of 2021. By Day 87, the case curve soared to a whopping 391,819 (the highest of the three waves). The third wave grew at a rate never seen before due to the highly transmissible nature of the Omicron variant. By Day 30, the case curve peaked at 312,180 infections a day. But in a little over two months since its start, it has already dropped to roughly the same level where the first wave ended.

The most difficult phase of the pandemic for India was the second wave when the seven-day average of daily deaths soared to a whopping 4,191. In contrast, the seven-day average of deaths soared to 1,169 (or 28% of the second peak) in the first wave. But it’s the third wave that presented the most comforting numbers – daily deaths saw the lowest peak with 1,133 (or 27% of the second wave).

The spike in deaths in the second wave was simply a crisis borne out of resource management. When cases spiked in the country, it led to a sudden and simultaneous demand for medicines, and resources (like medical oxygen) needed to battle the disease.

More than any of the other waves, India’s experience through April-May 2021 should serve as an example of what not to do in terms of management of medical resources – lessons that were largely applied in the third wave with several states (such as Delhi and Maharashtra) quickly rushing to boost O2 beds availability as soon as it became apparent that a new surge of infections was starting in early 2022. And while resources were never pushed to the wall again in the third wave due to the relatively mild Omicron variant, the safety net was a crucial part of how the third wave never sparked fears as the second wave did in the country.

Data sharing

Data scientists across the world have tried to analyse the pandemic on the basis of numbers. This endeavour was driven by both government data, as well as public data communities such as stepping up by creating data sets out of disparate pieces of statistics around the pandemic. But despite such endeavours, there remains a massive gulf in what independent data scientists have had access to in terms of on-ground-statistics.

For starters, district-wise testing numbers have never been released to the general public. The reason this statistic is important is that it enables the calculation of the positivity rate at a more localised level – a figure that enables people to know which regions have a higher prevalence of the virus.

Another key statistic that remains a challenge is calculating the true cost of the pandemic in terms of human lives. While on paper a little over half a million people have died from the disease, most experts believe this number is a gross underestimation. In statistical terms, data scientists use what is called the all-cause excess mortality – a number that refers to the total deaths occurring during a crisis that is above and beyond what would have been expected under regular conditions. 

Examples of this would include people dying due to heart attacks due to ambulances not being available due to Covid, or instances of infant or mother mortality that would otherwise have been prevented if medical resources wouldn’t have been so scarcely distributed. Exercises such as these have tried to gauge the real impact of the pandemic in a region – this has ranged from Delhi seeing twice as many deaths due to Covid, to somewhere like Bihar, where we’ve calculated this number to be as much as 48 times the reported death toll.

Lockdowns and how

A vast majority of the first wave, as mentioned above, was spent under a hard lockdown in the country. This bought India much-needed time to arrange resources for a prolonged fight against the disease. The lockdown also resulted in a long-and-slow rise and fall of the first wave. But the time the second wave took grips in the country, most of the country was thrown open, resulting in the massive surge visible.

However, practising caution against Covid may not necessarily mean that people need to be locked inside homes, and businesses shut down. This is because while hospitalisations and deaths remain a small possibility, the proportion of cases that needed it dropped significantly in the third wave. Locking down indiscriminately in any can possibly hurt not only the economy at large, but also households and small businesses at a personal level.

With two years of experience behind us, scientists and governments know what kind of situations can pose a risk to virus spread. We know now that masking and ventilation remain the best bets against fighting this virus. As long as businesses, schools, colleges, houses and offices stick to the basics, full-scale lockdowns can be avoided.

Vaccinations, vaccinations, vaccinations 

Last, and perhaps most important, is the impact of vaccination. In each subsequent wave, the proportion of people vaccinated in the country has been increasing. At the peak of the first wave, no vaccine had been developed for the disease, so not a single person had been vaccinated. At the peak of the second wave, only 12% of the country’s adults had received a shot of the vaccine. This proportion soared to 95% (of those eligible) at the peak of the third wave – a factor that explains how the country saw off the third wave a lot better than it did the second (or even the first wave).

This places India in a position to prepare for possible future waves. With most people already covered with two shots of the vaccine, now would be a good time to offer third shots to more people so that future waves can be controlled with minimal need for a halt on social and economic life.

If the pandemic has taught us anything, it is the value of data. To help understand the battle against the pandemic, Jamie Mullick, HT’s Covid data whiz, writes Numbers Matter

The views expressed are personal

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    Jamie Mullick works as a chief content producer at Hindustan Times. He uses data and graphics to tell his stories.

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