Seroprevalence Study in Mumbai: In Conversation with TIFR Scientists -- Part 1



Part 1: Introduction and About the Study


Mumbai, a city in Maharashtra (India) has been the center of the epidemic since the outbreak of COVID-19. The prevailing pandemic has led to the spread of COVID-19, caused by the SARS-CoV-2 virus, both symptomatically and asymptomatically. One of the ways to understand the dynamics of transmission of disease is through  community based serological studies to test for infection. It is important as depending on the level of prevalence of infection, appropriate public health interventions can be planned and implemented for prevention and control of the disease.

A COVID-19 seroprevalence survey study was recently conducted in a joint effort by the National Institution for Transforming India, (NITI-Aayog), the Municipal Corporation of Greater Mumbai (MCGM) and the Tata Institute of Fundamental Research (TIFR), Mumbai; along with other partners, Kasturba Hospital for Infectious Diseases, Mumbai; Translational Health Science and Technology Institute (THSTI), Faridabad; and NGOs.

The results of this study are sending shock waves across the world! To get up close and personal insights into the seroprevalence study, we at CovidGyan are in conversation with Ullas Kolthur and Sandeep Juneja, both scientists at the TIFR, who lead the study.

Meena Kharatmal: Welcome Dr. Ullas and Dr. Sandeep in this "Conversation on COVID" series. Congratulations on the study. First of all, we will be interested to know how you both as scientists at TIFR got involved with the Government's COVID-19 seroprevalence study? How has your research background helped you contribute to this study?
Sandeep Juneja: The opportunity came through Ullas. Our computer science department has been involved in building a city simulator, simulation model for the city of Mumbai, Delhi, which basically can project how the epidemic is going to spread. In this context, having this serosurvey data would be enormously important as an input. When the opportunity came up it seemed very natural for me to be part of it. I have been following this line of work on modeling  for quite sometime now, and certainly these data would be useful to us. In terms of my own skills as a probabilist, I have a fair idea of how the statistics is done. It was a good match and happy to be involved in this project.
Ullas Kolthur: I have been part of multiple efforts at TIFR, from finding some molecular solutions, all the way to testing and training.  Because I was in touch with the Government, local Maharashtra Government and other agencies, a strong need was felt to actually know what was the prevalence of infection in the city. The molecular diagnostic tests are usually performed on people who report to the medical centers because of symptoms, which does not necessarily capture the actual infection spread. Although we both, Sandeep and myself, do not belong to this classical field of epidemiology or disease biology, we thought that this is a useful thing to contribute. The only connection that I found was to explore what is COVID-19 impact on people who are suffering with co-morbidities and the elderly. My area of research is somewhat connected, because we study on diabetes, obesity and aging.
Meena Kharatmal: What is COVID-19 seroprevalence study? How does this study attempt to understand the COVID-19 disease?
Ullas Kolthur: Many of us during our everyday life are possibly infected by multiple pathogens. Most often than not these pathogens are automatically cleared by our bodies because we have a very robust immune system. In rare cases, this leads to symptoms and in even rarer cases this leads to several outcomes in terms of diseases, and of course in unfortunate cases it could cost lives as well. So to know whether somebody is infected or not, just testing people who report symptoms is one way of doing the study. But that is not the only way because, like I said, many of us could have been infected and we may not have shown symptoms, or we might have just recovered from the infection. So it does not tell us how many people are infected. Seroprevalence allows us to assess what percentage of population is infected.
In our body, usually there is something called innate immune system and adaptive immune system. Antibodies come in this particular arm called adaptive immune system and antibodies are nothing but small protein molecules that are circulating in our blood and they are also secreted in the mucus and other secretions. These are raised when there is a pathogenic onslaught as a normal defense mechanism. One way to know if someone is infected is by measuring the antibodies that are raised against the pathogen. So in this case, we set out to ask whether there are antibodies that are raised in individuals against the SARS-CoV-2 virus. Therefore, seroprevalence captures the percentage of people who might be infected, who may or may not have had symptoms and also may not have had reported to the healthcare centers. Hence,  it’s a powerful method and gives a perspective of population level spread.
Meena Kharatmal: Prof. Sandeep, as your research is in the area of simulations and modeling, we will be interested to know how you came up with the method of mapping Mumbai city and the selection of these three particular Wards, considering geographical and population wise diversity.
Uma Ramakrishnan: Thanks Meena for bringing that up.  What do you think were predictors that you were looking for when you were designing the study, that may have caused differences in seroprevalence? That is something people who are outside of science do not think about. It would be very nice to hear your thoughts on the potential reasons for differences in seroprevalence when you were designing the study and how that  translated into the sampling design.
Sandeep Juneja: It was clear to us that population density has to play a role because infection spreads from people crowding and coming together. Highly densely populated slums and relatively less dense non-slums are a unique phenomena in Mumbai and it has to be captured properly. So in the very beginning, we were very clear that we need to do the study separately for slums and non-slums. Now when we started the study, we did consultation with BMC (Brihanmumbai Municipal Corporation). We could not do the study for the entire Mumbai, as it would require lot more samples, and it is also logistically far more difficult.  We planned for representative areas or Wards in Mumbai that has 24 Wards, and we signed up for three Wards.  This is how we decided the Wards. One was in the city and had seen lot of infection, that is the Matunga Ward. Second was at the intermediate level on the Eastern suburb, that is Chembur West Ward. The third was chosen because the infection was just beginning to rise, all the way in the North, that is Dahisar Ward.  Our own simulation model gave us an idea for how much area of prevalence we expect to see by and large. So with our model we could have a fair idea of how many  samples to look for in each of these three Wards in the slums as well as non-slums. That is how the design came about.
In our methodology, we tried to ensure certain issues. When we collected these samples, there was a worry that we get very selected population, in the sense that people who have reason to be tested come forward etc. So we wanted to preclude these biases.  We worked hard to ensure that we have a fairly random coverage of these regions, so that the samples that we get are far more representative of the population.
Ullas Kolthur: There is one more thing that I will add about the design. We wanted to do a cross-sectional study, which means the ability to capture potential correlates that could have driven prevalence. This is important because just capturing a number tells you what the percentage of people infected is. But going back and asking about other factors that could contribute is important because it raises new hypotheses. For example, whether there are any differences in age, gender, population density, number of people in a household, etc.
Now there was a third aspect that we also considered in designing the study. In addition to assessing seropositive people, we wanted to check for antibodies that could neutralize the virus. So, we wanted to do a test to actually assess what percentage of this population can actually make antibodies which can neutralize the virus. This is one of the many correlates to assess whether there is immunity or not. So the design of the study was both statistically robust, tried to negate biases and at the same time capture factors. In fact, we also had another important part of conducting information survey about the number of people in the households, whether they have access to toilets, whether they have co-morbidities. In this way, design of the study included biological aspects, logistics aspects and statistical aspects. This was an unique opportunity where we merged all the three.
Sandeep Juneja: I will just add one more point. We also planned it in two phases. So the second round of survey is going on right now and the main intent is also to see the direction of the trend of increase of prevalence. That gives us a better sense of how close we are to herd immunity etc., how fast the infection is spreading, and where it is spreading.
Key insights from the study, continued in Part 2.

About the people:

Dr. Ullas Kolthur is a Professor at the Department of Biological Sciences of the Tata Institute of Fundamental Research, Mumbai. His research interest is in the area of cellular metabolism and energetics.
Dr. Sandeep Juneja is a Professor and Dean at the School of Technology and Computer Science, Tata Institute of Fundamental Research, Mumbai. His research interests lie in applied probability including in sequential learning, mathematical finance, Monte Carlo methods, and game theoretic analysis of queues.
Dr. Uma Ramakrishnan is a Professor at the National Centre for Biological Sciences (TIFR), Bangalore. Her research investigates population genetics and evolutionary history of mammals in the Indian subcontinent, including work to save India’s tigers.
Ms. Meena Kharatmal is a Scientific Officer at the Homi Bhabha Center for Science Education (TIFR), Mumbai. Currently she is contributing articles, resources for the CovidGyan. She is also trying to complete her PhD in the area of Biology Education.
This interview was recorded on 19th August 2020. Since then, the preprint on the findings of the Mumbai seroprevalance study is available as a report published on TIFR website.