India’s Unique Coronavirus Challenges
June 19, 2020, CoronavirusClick Here for the Latest on COVID-19 from Michigan Public Health Experts
Though it’s landmass is only one-third the size of the United States, India is home to nearly 17% of the world’s population — approximately 1.34 billion people. But as it grapples with the coronavirus pandemic, it’s population size is just one of the unique challenges facing the country. Michigan Public Health Professor Bhramar Mukherjee explains the factors behind India’s still-rising infection numbers, and how she’s helping the Indian government use data to slow the virus’ spread.
Learn more about Prof. Mukherjee’s COVID-19 modeling project: http://myumi.ch/Axm7W
Be sure to follow us at @umichsph on Twitter, Instagram, and Facebook, so you can share your perspectives on the issues we discussed, learn more from Michigan Public Health experts, and share episodes of the podcast with your friends on social media.
Mukherjee: India is a very interesting place to study and I think in the next couple of month’s the world’s global attention is going to shift to India. And I think even after we go through the first races of the pandemic, we are going to study what worked and what did not work in India for a long, long time because of its very special context of slum-dwelling population and religious gatherings, social beliefs, the low resource setting the economy, everything put together, how India fairs is going to be a very dynamic and intriguing story.
Right now India is sixth, in terms of the world lists, but the number of cases are increasing every day, so I would not be surprised if India ended up with the largest number of cases in the world, but its population is also quite large, it's only less next to China, but China was able to contain it by keeping it localized in one province and it did not spread as badly as in India.
I wish I had better more positive messages, but from our modeling team, by July 1, there are approximately 600,000 to 2-million cases in India.
Speaker 1: Though its land mass is only one-third the size of the United States, India is home to nearly 17% of the world’s population, approximately 1.34 billion people. But as it grapples with the coronavirus pandemic, it's population size is just one of unique challenges facing the country. As of June 2020, there are 280,000 cases of COVID-19 and infection trends are still rising.
Hello and welcome to Population Healthy, a podcast from the University of Michigan School of Public Health. This episode is part of a series of special editions of our podcast focusing on the ongoing coronavirus pandemic. University of Michigan School Public Health Biostatistics Chair and Professor Bhramar Mukherjee has been working with university colleagues and the Indian government to model the spread. Their goal is to provide current and clear data to inform decision-making.
Mukherjee: India took very early and very strong public health prevention methods. So on March 25, when the initial lockdown started India had about 536 cases and about 11 deaths. Very early in the course of the pandemic, but then the locktown in other countries has shown a pattern that within three to four weeks maximum, we have seen that the number of active new cases have gone down. But unfortunately in India, the national curve and the statewide curves appear to indicate that it has not turned the corner yet. The number of people on average that one infected person can spread the infection to, that looks like below one would be good, or around one would be good, but in India that’s still around 1.3 and the number of cases are still growing and they’re growing at a slower rate. That's the effect that the lockdown has had on the coronavirus spread and this is very puzzling because we have not seen that with a complete national lockdown for two months in any country around the world.
Speaker 1: India's population density and the unique structures of its families and workforce may continue to challenge the public health interventions that have helped slow the spread in other countries.
Mukherjee: India has been under four and now in the fifth phase of lockdown and the last two, three phases have been more relaxed and modulated compared to the first two phases. The lockdown is not a permanent strategy and lockdown is never meant to be a permanent strategy. It was only to buy us time so that we slow down the number of cases and the spread of the virus, so that by the time the lockdown is over, then we have the number of cases that we have, they are in a manageable number, and we have a strategy. So for example, in South Korea they never went into a lockdown with a really high intensity contact tracing and tracking of the cases and testing. That was their strategy. For India, different states have had different strategies and different degrees of successes in terms of implementing these non-pharmaceutical interventions, and the effect of lockdown. For example, the two states Punjab and Kerala have done exceptionally well but they have much less of a challenge than Mumbai or Delhi, where these are big metros and people move around and there is a very congested population. The challenge for India is that lockdown is not going to be feasible because our the goal of any society is to minimize the total human suffering and loss. And so the economic devastation that lockdown has brought about can lead to more people dying of hunger than from coronavirus, and we do not want that situation.
Mukherjee: India is very unique, of course, with 1.34 billion people and many very large, densely populated areas, so it's a very hard place to roll out social distancing interventions in many places of the country. For example, the two hot spots, Mumbai and Delhi — Mumbai has very large slum areas where the housing conditions are very congested, multiple people live in very small rooms, so the concept of social distancing is very, very difficult. In India, what you see is that there's a lot of variation across the states, but the unique challenge with India’s national lockdown was that there's not enough preparedness to let the migrant workers return to their home.
So what happened was that the lockdown typically makes you stay put where you are, but what happened in India was that as soon as the lockdown was enforced, then migrant workers started to go back to their own homes. So this new mobility actually led to a lot of chaos because these people do not have adequate transportation, there's a lot of loss of lives for them to try to go back home on foot, and then also the transmission around the country. So we see that the infection moved from the western part to the eastern parts, and now it's in many, multiple states in the country. So that was very unique.
And also in the eastern part, there was a cyclone of Category-4 in West Bengal and Odisha, so that changed how do you social distancing during the time of a national disaster. And on top of that, I think India is very unique in terms of its age structure and the contact network, because most of the elderly actually live with families. And so it's very hard to isolate them from the rest of the family. India has a youthful population, it's a younger population if you compare it to Italy or if you compare it to the US so the fatality rates have been lower.
The interesting challenge for policy makers is that you are sort of in an uncharted territory where you are expected to make decisions very quickly and with the rapidly evolving situation, so I think it's very important to have the data that you really need at hand, in a digestible format, so that you can make the decisions and also you can escalate some of the the interventions and some of the strategies which seem to be working and shut down things that not working in a very quick and nimble manner. So this agility in terms of decision making I think is very, very important.
I think that there has been a lot of effort to do syndromic surveillance so that they can report symptoms that we have seen in the US. In India, health is jointly managed by the states and the national government, so the states are very proactive, and each state has a different kind of performance in terms of managing the pandemic. But they really need to employ grassroots-level community health workers, because there is policy, but then there is a translation and implementation aspect of it. So policies can be released but then the whole workforce at a grassroots level needs to be mobilized in order to properly implement those, and also communicate to people in areas across different education spectrums, economic spectrums, what they need to do.
And one thing is that you know people in these communities are actually quite disciplined, and so I think in particular that communication and engagement of community health workers in under-represented communities and impoverished communities are going to be very, very important right now.
Speaker 1: In India, like many countries across the globe, empowering communities starts by emphasizing the role of the public health workforce, including expanding it and transmitting information to people.
Mukherjee: For India particularly, I think there's a real lack of a public workforce and public health awareness. Prior to the pandemic I think that there were very few trained epidemiologists and biostatisticians. India has very few schools of public health, so I definitely think that that's an area where the government needs to invest because you never predict the future.
We have seen the spread of misinformation almost as exponentially as the virus itself. I think that people are relying on social media and anecdotal evidence, so I think it's tremendously important to really release information which people can understand, not only the educated, not only the intellectuals and people with college degrees, but people who are illiterate, people who are working in various different sectors. There are tons and tons of data lying there, but there are not able hands and able minds which can analyze them as quickly as they are getting in order to inform the government of right policies. I know that many people have been visiting our website because our models get updated every day for projection, but in terms of policy, you really need a prediction for the actual number of cases and the actual number of deaths, because those are the people who are going to need care. A fraction of them are going to show up in the hospital, and we need to know those numbers for planning purposes, so data can be used in the next phase to optimally deploy resources. For example, if I predict that there are going to be a lot of cases now in Haryana, and Punjab is doing very well right now, why don't we move some resources and personnel and health professionals from Punjab to Haryana?
So these types of studies, if you think about Wuhan, about 30,000 healthcare workers from all over China went to Wuhan to help out, and that was a major success in taking care of those patients in that tight timeline. Things like that have to be improvised and you nee good prediction models and good statisticians working with the government in order to inform those strategies and influence policy.
Speaker 1: In the early days of the pandemic, Professor Mukherjee and colleagues were motivated to use their expertise to help India confront the virus. Creating projections using data and adding in different variables is called modeling. She and her team created multiple models that played a role in decisions coming from the Indian government.
Mukherjee: The last three months seemed to be the longest or the shortest three months of my life — longest in terms of more hours. I have been working 16 every day. So it's a lot, but also in terms of also knowledge. I think that what I have learned in three months probably, I would not have learned in three years, which has been really inspiring and very enriching as well. And shortest, it has been a really short time to learn so much. The way it all started, this project was, I exactly remember the date because it was March 16, the day of first quarantine in Michigan.
The Indian students and faculty as well of Indian origin, with the travel ban they were very worried about India, what's happening in the US, we could not go back home, many of us had summer plans to visit our parents and loved ones in India. So I want to somehow repurpose that anguish and that anxiety and that energy into something meaningful. So I convened a Zoom call and I said, “what can we do as quarantined public health data scientists that's going to be meaningful for us?” It was identified that we wanted to work on prediction models for India. And the good thing was that one of my colleagues, Peter Song and his team, had already built a pipeline for analyzing data and creating prediction models for Wuhan and for China, so we borrowed some of those mathematical modeling tools.
But one thing which is very important to me and which is very distinct to me from other approaches is that I realized that this is a long game, and we need to build the platform, not to strike one single paper and be done with it, because the reality on the ground changes every day. That's why we build a website, COVID19.org, where we have the power to update our prediction models. It takes a long time to run those models under the current different scenarios across different 20 states and across the nation, and the models run every night, because things are moving in the order of days now, not months. That was probably the only good vision that I had not to just have packages and our codes for statisticians, but to have an interface where people and media and journalists and epidemiologists could visit and see what is happening in India today, as opposed to referring to a paper that got done a month ago.
The story is that on March 16th we convened on that Zoom call, and then we built this report in a period of 72 hours, a huge team from here, Johns Hopkins University, University of Connecticut, Delhi School of Economics. We put all of our efforts together into creating this product, which I think all of us are quite proud of, and we wrote this Medium article. And the next morning when I woke up, my social media was just flooded with notifications, and I initially thought that maybe I had done something really, really bad. So then I opened the social media and everywhere the projections, and my email was, was swamped by media queries from India and all over the world actually, about these projections. And we had projected that by May 15, India will have about 97,000 cases, and actually India was at about 100,000 cases on May 15. We daily update our predictions, so people come to this website for information. So it has been really spectacular, but the best part of this is probably for me personally, on a very different note, that for the first time probably my parents understand what I do because I have been on national television trying to communicate complex models in simple language. And I have tried to write differently in terms of public health engagement so that people can understand without getting lost into the intricacies of equations. This has been a very big leap, but extremely gratifying that we could contribute to this process as data scientists.
Speaker 1: For Professor Mukherjee, this work is deeply meaningful, both in terms of her relationship with her native country and her relationships with her students.
The irony is that right now I cannot go to India, there are no flights and India’s borders our closed, but I feel closer to India in the sense that I feel connected to the scientific community in India. I feel I am actually really connecting with the pulse of the country by reading, researching, working with various governments and people reach out to us, our team for projections for their own states and to shape their policies. This has been really, really a very inspiring time, I think very challenging, very draining, but at the same time, I feel like alternative waves or tiredness, fatigue, fear and inspiration have hit all of us. It has been a very special time.
My students have been really amazing and I always see that our faculty is very good, but our students are just unparalleled. And you know, the students have really seen data in action, and many times for biostatistics, particularly, it's very hard to see that immediate translation into making a difference. So I think that has been very useful for our students as an example. But I also have to say that they are so articulate, so thoughtful. I'm so impressed by this new generation of students who really want to make an impact and not just enrich their CV, but have something which is more autistic.
I have many memories during this time that I'm going to really treasure. One of them is that when one of our students was quoted in a newspaper, then his mom actually put it on the refrigerator and he sent me a picture of that, and I felt so happy for the parents who are seeing their kids making a difference through their education at University of Michigan.
Speaker 1: This has been a special edition of Population Healthy, a podcast from the University of Michigan School of Public Health. During the ongoing coronavirus pandemic, we’ll work to bring you analysis from our community of experts to help you understand what this public health crisis means for you. To stay up-to-date in between special edition episodes, be sure to check out our website publichealth.umich.edu, subscribe to our Population Healthy newsletter at publichealth.umich.edu/news/newsletter and follow us on Twitter, Instagram, and Facebook @umichsph.
In This Episode
John D. Kalbfleisch Collegiate Professor of Biostatistics; Chair of Biostatistics, University of Michigan School of Public Health
Bhramar Mukherjee is John D. Kalbfleisch Collegiate Professor and Chair of the Department of Biostatistics at the University of Michigan School of Public Health. Her research interests include statistical methods for analysis of electronic health records, studies of gene-environment interaction, Bayesian methods, shrinkage estimation, analysis of multiple pollutants. Collaborative areas are mainly in cancer, cardiovascular diseases, reproductive health, exposure science and environmental epidemiology. She has co-authored more than 200 publications in statistics, biostatistics, medicine and public health and is serving as PI on NSF and NIH funded methodology grants. Learn more.