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On May 4, 2020, a day before India recorded the highest single-day jump in COVID-19 cases and deaths with 197 new deaths and 3,900 infections, Lav Agarwal, Joint Secretary, Ministry of Health and Welfare Family member of the Union stated during a press conference that the curve “(COVID-19) is relatively flat as of now.” Then he added that “if we work collectively in that direction, then the peak will never come.”
This huge discrepancy between the Ministry of Health and Family Welfare assessment of the situation and the reality of the ongoing pandemic in terms of lives lost to the disease leads one to wonder: Is India really flattening the curve? Or is this a classic case of cherry picking data to fit a particular narrative?
The answer to this question lies in the definition of the term: “Coupling the curve”.
Definition of flatten the curve
In epidemiology, the curve (also called the epidemic curve or epi) refers to the projected number of new cases over a period of time.
Originally shared by Drew A. Harris, MD, Population Health Analyst, Thomas Jefferson University, Philadelphia. In the form of two epi curves, flattening the curve is a public health strategy to stagger the number of new cases over a longer period. The flattening gives people better access to care, to hospitals and vaccine manufacturers to have more time to prepare, and to health workers and the police to take a breather.
In the graph above, both curves show the number of new cases over time, one without any protective measure (red) and one with it (blue).
A steep curve (shown in red) indicates that the virus is spreading rapidly and that more people are seeking treatment at any given time. A steep drop often follows curves with a steep rise after the virus runs out of new hosts to infect. The catastrophic consequences of misinterpreting a false positive result are all too evident in Italy, and in fact represent a risk not worth taking. Failure to follow a containment measure could result in increased deaths along with the risk of further loss of life caused by a health care system that is overloaded beyond its capacity.
On the other hand, a smoother curve (shown in blue) lower than the dotted line indicates that fewer people are infected with the virus, preventing a sudden surge that will overwhelm the health system.
Simply put, going from the Red to the Blue curve is flatten the curve. But to achieve that, we need to draw these two curves while maintaining the capacity of our local health system (the important dotted line) in mind.
So is India “flattening the curve”?
The short answer is no.
While the stringent containment measures implemented by the central government have certainly slowed the growth of COVID-19 during the shutdown, is that growth rate manageable by our healthcare systems? The answer is no.
Let’s dive in to understand why.
First, let’s look at the rate of increase in COVID-19 cases in India, week by week. Over the past two weeks (April 22-May 5), the growth rate of COVID-19 cases in India is constant at 40 percent (compared to 250 percent the week before the government of India that ordered the closure on March 25).
The decline in the active case rate alone does not indicate that India is flattening the curve, although it definitely suggests that growth has slowed. To identify whether we have flattened the curve or not, we need to draw the two epi curves.
Let’s first draw the blue curve – an epi curve with protective measures like a lock, since we have the data for this.
Drawing the red curve is more complicated, since one needs to identify a projection model, in a hypothetical scenario if there were no block or containment measure. Most COVID-19 projections are based on the “SEIR” model, which tracks the flow of individuals through four stages: susceptible (S), exposed (E), infectious (I), and recovered (R).
Different models use different assumptions and techniques, which means that they give us different points of view. However, since COVID-19 remains an unclear infectious disease, which means we can only get an accurate prediction THEN The outbreak ends. Consequently, the multiplicity of unknown variables indicates that most of the projections currently used at this time are, at best, a pure assumption.
Despite this, for the sake of simplicity, suppose that the cases increase at the same rate as it grew before the crash, which means that it doubles every three days. Based on this assumption, a direct statistical model, also used by Shamika Ravi, former member of the Prime Minister’s Economic Advisory Council, can be applied to create a projection curve.
In that case, this is what the graph looks like, with a projected number of around 38.83,316 cases on May 5:
As of today, COVID-19 cases in India double every 12 days (compared to 3 days before closing). But we still can’t say we have flattened the curve for three big reasons: 1. Low evidence 2. Lack of an efficient healthcare system 3. Unreliable data to determine how COVID-19 will form after the crash Low evidence This is not a hidden fact that India is testing far less than most countries in the world: 1 test per 1,000 people (compared to Italy, which performs around 32 tests per 1,000 people). But the most jarring fact is that India has also lowered testing rates as the number of cases in the country increases. The testing rate decreased from 150% (from March 28 to April 3) to around 50 percent this week. So this could mean that the curve is not flattening and that we have hidden information on COVID-19 cases in India.
Lack of an efficient health system.
In addition to the two epi curves, another critical component of the graph is the dotted line representing the capacity of our health system.
According to an estimate by the Center for Disease Dynamics, Economics and Policy, India has approximately 1.9 million hospital beds, 95,000 ICU beds and 48,000 ventilators.
Even if we keep India locked up forever, with the current rate of reproduction (R0 or R nothing), defined as the average number of people becoming infected by an already infected person of 1.36, about two million people will become infected in about 9 months when the peak arrives (analysis applied to one billion Indians under the age of 50).
We also don’t know how the rate will fare when India removes the block because the virus has an incubation period of about two weeks.
So without any information on how many people we can treat, how many doctors we are testing, and how many of them are affected, we cannot determine the burden on our healthcare system, and therefore cannot conclusively say that we are flattening the curve. .
If you’re still not convinced, here is an example from Kerala that shows exactly what flatten the curve it seems that when the number of active cases becomes equal to or less than the number of recovered patients.
At this time, the Ministry of Health and Family Welfare should stop claiming every day that they have flattened the curve and focus on raising the capacity line of the health system and increasing testing. This is because, even as we flatten the curve, there is still a shortage of test kits, beds, ventilators, PPE kits – in other words, the essentials needed to treat patients.
Now is not the time for rhetoric, it is a time to save lives.
The author is a data scientist and a Reuters researcher.
Update date: 07 May 2020 19:42:42 IST
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