India and COVID

Statue of Buddha with lotos and birds

I love India. Thanks to my scientific collaboration (funded by the UK government and my New Professor Fund at Strathclyde) I have had the pleasure and privilege to visit the country twice and to meet fantastic Indian people. It is a country of huge potential, great challenges, landscape, and food like nowhere else. I was struck by the warmth of welcome and by the generosity of people.

The above picture, taken during my first visit in 2019, shows one aspect of India – the calm, serenity, and beauty of nature. But, I also like another version of that picture, taken in 2020:

Statue of Buddha with a female worker using a mobile phone

showing a connection between the past and the present – the bustle of a building site and ubiquitous mobile phones – the dynamics of the modern world and the advances in the technology of which India is very proud.

I was there last year when the pandemic was starting, flying out of India on 7th March 2020. There were people in masks at the airports and I carried a hand disinfectant. Over the next days, weeks and months, I was very concerned about the spread of the coronavirus in India, particularly with such challenges in living conditions, crowding, low hygiene, and deprivation. I was very unsure how it would be possible to control air- and droplet-borne virus in crowded cities, with streets as seen below:

A crowded street in India.

And yet, India largely managed to control the pandemic throughout the year 2020 and early 2021. Although there have been concerns about under-reporting, the outbreak until very recently was very far from a disaster that I had been fearing.

There have been many possible explanations for this relative success. The early reach of herd immunity was suggested, with serological data pointing to very high levels of antibodies in locations that had experienced early outbreaks, like Delhi. A very strict initial lockdown in March and April (shortly after I had left) seemed to contribute to the reduction in the potential of the virus to spread. Indeed Google mobility data showed a rapid, immense response to the lockdown.

Graph showing daily new confirmed cases per million, with a peak of abouy 70 pm in August 2020 and a rapid growth since March 2021

I have also read very interesting stories of self-help, community kitchens, mental health support, for example in Indian states like Kerala. As a result, India has so far been a COVID-19 success story. Unfortunately, not much longer.

Most likely it was the relaxation of rules in early 2021, seen in the mobility graph above that created a condition for the “second wave” which India is experiencing now. The raw numbers of new cases are now staggering – exceeding 300,000 new reported cases each day; there is almost certainly a huge under-reporting going on, so the true numbers are possibly 10 or more times higher – reaching perhaps millions. The scary part is the rapid, exponential growth, of cases and associated deaths (which also are most likely underestimated, possibly by at least a factor of 5), as seen below

Graph showing daily new confirmed deaths per million, with a peak of abouy 0.8 pm in August 2020 and a rapid growth since March 2021

Even more tragic is a constant stream of news about overcrowded hospitals, intensive care units running out of oxygen, and funeral pyres (cremation is part of traditional funeral rites). At the same time, vaccination progresses slowly (compared with e.g. the UK) despite India being one of the largest vaccine manufacturers.

Graph showing vaccination in India (10%) and the UK (50%)

The prognosis is, unfortunately, grim. There is some hope in the small drop in mobility (the relationship between mobility and infection is quite complex, but the higher the mobility the more chance for the virus to spread), but there appear to be only limited policy actions that could stop the spread.

Modelling of future scenarios is difficult, as there are plenty of unknowns:

(i) levels of pre-existent immunity and immunity acquired during the previous wave;

(ii) effects of non-pharmaceutical actions, like lockdowns and curfews;

(iii) huge heterogeneities in between cities and countryside, and poor and rich;

(iv) even the actual numbers are subject to huge under-reporting and so we do not know exactly the infection and death status.

With these caveats, some modelling results suggest that India is likely to experience a massive wave of infection, with the daily true number of cases reaching 15,000,000 (for comparison, London population is 9,000,000) and the daily number of deaths exceeding 13,000 (the Infection Fatality Rate, IFR, i.e. the ratio between the number of deaths and the number of infected individuals might be lower than in countries like the UK, as the Indian population is younger). The modelling results shown here come from an approach developed by the Institute for Health Metrics and Evaluation (IHME) which is an independent global health research centre at the University of Washington, USA.

I do really hope the scenario shown above will not materialise and somehow the epidemic will stop spreading through India. I am thinking of all my friends and colleagues in India, and all the people I met and seen, and I am praying that they are spared the infection and complications.

But I am also seeing the current developments in India as a warning to all of us. The scenario of a relatively small “first wave”, followed by relaxation of regulations too early and (relatively) open borders, combined with the rise of new strains (like B.1.617), is something that has been happening across the world and leading to a huge “second” or “third” wave. Many countries in Eastern Europe (like my native Poland), the UK, Chile, and others have shown a similar pattern, although not at such scale and rapidity.

The current events in India also seem to contradict many theories that have been used by lockdown sceptics and those who advocate the “light-touch” approach to COVID-19 – pre-existing immunity was supposed to protect the Indian population from the virus or herd immunity was supposed to have been reached a long time ago. Examples from Brazil, Chile and now India show exactly what might have happened in the UK, Poland, and across the world if the virus was allowed to spread uncontrolled. We might criticise our governments and scientists for many things, but not for imposing lockdown controls and developing and successfully administering the vaccine.

My thoughts are now with India. There is not much I can do to help with fighting the outbreak. But, I do hope that my work with colleagues in India would help long term in restoring the country’s food security post-pandemic. I look forward to future visits and tasting again the fabulous Indian food.

Has England reached herd immunity level – part 2

An update on the previous post on the UCL model and herd immunity levels. I just thought of adding a couple of further thoughts on this topic.

Firstly, there appears to be growing evidence of a potential for new strains (Brazil, South African, Indian) to overcome the immunity created by the previous infections.

The rapid growth of cases in Brazil and now in India suggests that either our estimates of Herd Immunity Threshold (HIT) are too low, or people who have SARS-CoV-2 antibodies can still be infected.

Secondly, there is an interesting interplay of heterogeneity and HIT. Heterogeneity, be it in the infectivity or susceptibility, is normally thought to lower the HIT. This basically is caused by the infection “burning” through the most susceptible parts of the population first.

As a result, we end up with a population where those who are not yet immune are protected by their lower susceptibility. This results in a lower value of HIT than predicted by the homogenous theory.

However, there is another trend that counteracts this process, and the one which seems more dominant at this moment. We now have a very uneven distribution of immunity due to vaccination.

Those who are potentially most likely to become infected (those in the BAME population, in deprived areas, or those who are vaccine-hesitant or anti-vaccine) also have the lowest vaccination coverage.

Thus, even if on average we can reach the HIT, these pockets of low-immunity, high-susceptibility/high-infectiousness, will keep the infection going and possibly creating the conditions where new strains can arise.

Even more arguments for the #ZeroCovid strategy… Let’s just keep going for a while longer, paying particular attention to those who are most at risk, to reduce the overall levels of infection until we can bring the vaccination higher.

Is England reaching herd immunity level today?

Much in the news has recently been made of a statement by a group of modellers led by the University College London that England will be reaching the herd immunity threshold (HIT) on April 9th, 2021:

A herd immunity threshold (of 73.4%) will be reached this week on 9 April 2021.

https://www.fil.ion.ucl.ac.uk/spm/covid-19/forecasting/

The popular view is that once we reach the HIT, the reproductive number will always be smaller than 1 regardless of what we do, the number of cases will always decline and hence the pandemic is essentially over. Unfortunately, none of this is in fact correct – but the problem is less with the UCL model and more with our – scientists – ability to communicate uncertainty, and our – the “Joe public” – ability to understand it.

Let’s start with the model. There are currently several models of the coronavirus pandemic in the UK, including the three “official” ones, by Warwick, LSHTM and Imperial groups. In addition, there are many other models by different groups, with different level of complication and addressing different questions. All of these models attempt to capture the key elements of the transmission – contact structure, probability of infection, age-dependent susceptibility and fatality rate, loss of immunity.

They also attempt to factor in the uncertainties associated with our lack of knowledge of some pretty important elements. Primarily, this is related to the actual number of those who are immune to the infection. The models are essentially used to “reconstruct” this number, and different assumptions can lead to different numbers.

https://www.fil.ion.ucl.ac.uk/spm/covid-19/forecasting/

Why is it important? Our best understanding of how coronavirus spreads suggests that once the proportion of those who are immune reaches a certain threshold, the infection numbers will stop increasing. The epidemic will not be over, there will still be people becoming ill and dying, but the overall trend will be downwards.

The “Holy Grail” of epidemic modelling is the knowledge of what this level is and countless arguments have been presented for different numbers. To some extent, however, this is a bit of a “red herring”. This number depends on how fast the disease is spreading, but this, in turn, depends on how we behave and what we do to control the spread.

There is, however, an upper limit to this threshold value, corresponding to the situation with no lockdown, mask use, hand washing, or staying at home (international travel is a slightly different thing). Good evidence suggests a value of 60-80%. It could be somewhat higher – as the “new variants” have higher potential for spread, or it could be lower – if there are substantial levels of variability in how susceptible people are or how they make contact. Somewhere in the middle, there is 75%, so we might stick to this number.

So, the models predict that once the proportion of immune people – which is unknown but estimated by the model – reaches 75%, the trend in the disease levels can only be down. But, what are these model estimates? How far are we from the “magic” threshold?

In principle, trying to estimate this value is a hopeless case, because of a combination of asymptomatic spread and low testing coverage and efficiency. Thus, at the beginning of the epidemic, we only tested – and reported – those with symptoms or even just those who came to a hospital. Anybody who did not have severe symptoms never made it as a number.

It is a bit like in the parable of the blind sages and the elephant.

By Ohara Donshu, Japanese, died 1857 – Online Collection of Brooklyn Museum; Photo: Brooklyn Museum, 1993.57_IMLS_SL2.jpg, Public Domain, https://commons.wikimedia.org/w/index.php?curid=10967373

It is a story of a group of blind men who try to guess what the elephant is like by touching it and only experiencing a small part of it. In the same way, we are trying to guess how many people have already been infected and hence might be immune, based on the case notifications.

Different models might make different assumptions and so get different estimates. The graph below shows such estimates from four models, compared with the actual data which represent the recorded cases. As we can see, the elephant we managed to recreate has some “elephant-like” features (the first peak, followed by a summer break, and the double winter peak), but the estimated number of “true infections” and hence the number of those who might be immune by now differs massively.

The situation is actually not as hopeless as it sounds. We can actually test people for the presence of antibodies and then verify that our models are correct – and reject or modify those that are not. By the way, antibodies are molecules that a body produces in response to infection (or vaccination) and their presence suggests some levels of immunity.

A month ago, in a survey, 54% of people in England tested positive suggesting that as many as that had contact with the virus. With relentless vaccination since then, I would not be surprised we might soon reach 75% testing positive or vaccinated with one dose. The numbers of those with two doses (myself included) are also going up.

But this almost certainly does not mean 75% are fully immune. The vaccine is not perfect (and the 85.2% efficacy assumed by UCL might be an overestimate). The presence of antibodies does not mean the person is completely safe. We still do not know how much vaccination stops transmission; if it does not fully, the HIT will be higher than the 75% quoted above.

As even small changes in these percentages can cause large changes in the way the virus spreads, there is a huge uncertainty associated with the size of the “third” wave. In the light of this, I think the UCL group are certainly unwise to make statements about reaching the HIT on a particular day.

Finally, does this mean the pandemic is over in the UK after today and we can relax all social distancing measures and stop vaccinating? Unfortunately, no. We do not know how well prior exposure to the virus guarantees immunity to the new strains. We know the new strains lower the efficacy of the vaccine. We know the immunity wanes in time. Our models are probably good enough, but not perfect to predict that the exact value would be reached on a particular day.

But even more importantly, the HIT only means that the daily count of infections will be going down once we reach that level. But there will still be many new infections and many deaths. More, models tell us that if we relax all regulations now, the speed with which the virus will decline will be much slower – which means many more sufferings and deaths. As throughout the last year, the more intensively we fight the virus, the quicker it would go away. Let’s keep doing this.


It has been pointed out to me that my second vaccination dose is not counting towards the English herd immunity levels, but the Scottish ones.