No reason to believe a state will escape the Omicron wave: Cambridge professor who worked on the India Covid-19 tracker


Echoing the concerns of the Department of Health, the latest report from the India Covid-19 tracker developed by the University of Cambridge and the UK’s National Institute for Economic and Social Research, shows the growth rate of new cases is a major concern in 15 states. As of December 29, the number of reproductions for Covid-19 has exceeded 1.2 in these states – Bihar, Chandigarh, Chhattisgarh, Delhi, Goa, Gujarat, Haryana, Jharkhand, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Tripura, Uttar Pradesh and West Bengal. In addition, the filtered growth rate of daily cases exceeded 2.5% in two other states, Telangana and Uttarakhand.

In an email interview with, Professor Paul Kattuman, an economist and applied econometer at Judge Business School who worked on the development of the India Covid-19 tracker, tells us that trends in the data suggest that cases are now increasing super exponentially. and most of the current wave powered by Omicron, which is more transmissible and has better immune escape mechanisms, will fall on the elderly and the immunocompromised. He claims, however, that once the Omicron wave sweeps the world, Covid-19 may well become endemic and operate at a level that more or less standard public health measures can cope with.

Edited excerpts from the interview:

Tell us a bit about the work that went into building the India Covid-19 tracker? What is your association with the project?

My research focuses on the application of statistical methods to analyze data in order to draw valid inferences.

In early 2020, when the UK government prepared to deal with the threat of the pandemic, Public Health England (now known as UK Health Security Agency) in the East of England region contacted the University. to help him analyze big data about the pandemic. This generated a number of collaborative efforts.

A small team assembled at Judge Business School by my colleague Stefan Scholtes, professor of health management, began work on predicting the trajectories of a variety of pandemic-related variables – new cases, hospital admissions , ventilator demand and death. The objective was to give a reliable alert so that the health system can be operationally prepared for the immediate future. The urgency and importance of this assignment led me to focus on new, more reliable time series forecasting models.

Ongoing engagement with Public Health England and the National Health Service has helped us to develop and refine these methods and adapt them for operational purposes. We worked with the National Institute of Economic and Social Research in London to launch a UK-wide tracker in February 2021.

In August 2020, I had started to apply these methods to provide weekly forecast for Kerala, working with Rajeev Sadanandan, CEO of Health Systems Transformation Platform, who had been appointed advisor on Covid-19 to the Chief Minister of Kerala. .

The usefulness of state-level forecasts led us to extend our work, in May 2021, to all Indian states and Union territories. The proven value of the national tracker has led to requests for more detailed weekly forecasts by Punjab and Tamil Nadu. At the end of October 2021, the national tracker and state-level forecast changed to an occasional series.

What kind of data do you use for your modeling and the methodology that helps you make the predictions?

The data used to generate the forecasts are all publicly available. For both national monitoring and district level forecasting for Kerala, Punjab and Tamil Nadu, I used data from the Covid-19-India API to This excellent volunteer collective closed its operations after 19 months of intense effort in October 2021. Since then, I have relied on the Coronavirus Resource Center at Johns Hopkins University of Medicine for data on India. .

In terms of method, we use a new time series forecasting model developed by my colleague Pr Andrew Harvey and myself. The model is based on classical statistical methods and extracts the time varying trend of the pandemic variable of interest, eliminating the effects of the day of the week and the purely random variation.

The model belongs to the family of structural time series models. He is able to note the variations in trajectory over time, due for example to mitigation measures and changes in social behavior. The model makes minimal assumptions by using the time series data to estimate its parameters. The forecasts produced are more precise over shorter horizons. Work on a number of useful extensions is in progress.

Our model contrasts with SEIR-type models which are commonly used in epidemiology and are more useful for determining the impacts of alternative policies using simulations. The two types of models complement each other.

In a recent interview with Bloomberg, you said: “It is likely that India will experience a period of explosive growth in everyday cases and that the phase of intense growth will be relatively short. Could you please explain how you made this prediction?

The evolution of the filtered growth rate of the daily cases (with removal of the effects of the day of the week and the purely random variation) is revealing. On December 25, the filtered value of the daily growth rate of new cases across India was slightly negative at -0.4%. This rate rose to 0.6% on December 26, to 2.4% on December 27 and to 5% on December 29. The increasing growth rate indicates super-exponential growth in daily cases.

This pattern of a short period of explosive growth in new cases is a common experience with Omicron, internationally. In the UK, for example, the first Omicron case was identified in the third week of November. The filtered trend in the daily growth rate of new cases in England remained close to zero in early December. After an initially gradual increase, the daily growth rate of infections increased sharply in just five days, from around 1.5% on December 13 to 7.5% on December 18.

The growth rate has since declined considerably, having passed through most of the susceptible population (which had been reduced by an intense booster vaccination program). However, the growth rate remains positive to this day. Daily cases are expected to continue to peak in the first or second week of January 2022.

The Cambridge India tracker correctly predicted the peak of the second wave in India in May. What are your predictions for a third wave? Also, how long is the third wave likely to last in India and when can it peak?

At this point, when the cases are increasing super exponentially, the time series model cannot reliably indicate when exactly the peak will occur or how big the number will be at the peak. The environment in which the disease evolves over time, not only due to mitigation measures, but also changes in social behavior. We will be monitoring the trajectory closely and hope to be able to indicate the approaching peak as soon as the signal is clear.

There has been a lot of talk and debate about the intensity of the third wave. Many scientists said it could be milder than the devastating second wave that wreaked havoc across the country. According to your model, roughly how many daily cases, hospitalizations and deaths could we consider in Wave 3?

One of the dismal limitations to be done with the Covid-19 data for India is the lack of reliable data sets on hospitalizations and deaths, as far as I know. Thus, model-based predictions of hospitalizations and deaths cannot be generated.

While Omicron is now known to cause serious illness much milder than Delta, it is also known to be much more transmissible and more able to evade the immune system. Experience in the UK suggests that these opposing forces occur, over time, to generate noticeable increases in hospital admissions among older people. While young people – by far the vast majority in India – should be able to recover easily, those who are immunocompromised are likely to be more severely affected.

Based on emerging data patterns, which states do you think are at risk of seeing an immediate increase? Is it possible that a third wave involves localized surges and impacts some states more than others? Or will we see a more or less widespread impact across the country as was the second in the second wave?

Looking at the experience of the Indian states, as of December 24, only six states recorded daily growth rates above 5%. By December 26, that number had increased to 11 states and the 29 to 14 states.

At this time, there is no reason to believe that a state will escape the Omicron wave entirely, unless it is completely isolated. But then again, these extremely high growth rates cannot persist for very long. Of course, the daily cases will continue to increase until the growth rate itself drops to zero.

Is Omicron likely to replace Delta as the dominant variant? And will it be fair to say that Omicron will power the third wave?

Reliable and complete data on the variants does not appear to be available. From the British experience, Omicron will play by far the decisive role in the current wave.

The big question everyone seems to be asking now is whether Covid-19 could enter an endemic phase later in 2022. Or does it depend on the role of vaccine coverage, how long the jabs offer protection and their effectiveness, and the emergence of new variants?

As I mentioned earlier, cases are now multiplying super exponentially and the model cannot reliably indicate when exactly the peak will occur. We hope to keep a close eye on the trajectory and hope to point to the approaching peak as soon as a clear signal emerges.

Epidemiologists and virologists are carefully studying the hypothesis (based on a now well-known South African study in a small sample size) that infection with Omicron enhances immunity against the more virulent Delta variant.

If so, given the imponderable of other variants, once the Omicron wave sweeps the world, Covid-19 may well become endemic and operate at a level that standard public health measures should be able to do. face. But the weight of an Omicron wave will fall heavily on those of us who are older or immunocompromised.


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