By: Hannah Lin (CC '23)
Could you talk about your new book, Understanding Coronavirus?
This book is a summary for the public of some of the main points of what we know about how the virus appeared, how it’s spreading, how it’s changing, how it compares to other infectious diseases, including SARS or flu, and medications.
When did you start writing the book?
I planned to be on sabbatical starting in January, and my plan was just to travel and work with colleagues around the world. I was going to go to Hong Kong, Korea, Europe, and do research and talk to my collaborators about my work. Then, the news about the virus came from China first and quickly followed from the rest of the world. Then all these trips were impossible, so I stayed here in New York enjoying my work and my family.
Most of my work focuses on genomics of cancers and viruses. I was reading about the virus in the news and looking at the scientific literature and I realized that there was a significant amount of misinformation in the press. There was a lot of information coming, it was hard to process for the press and for the people, and there was confusion around all this noise that is still going on. I was taking my notes, looking at how the epidemic was developing, and decided to organize them. I was then talking to editors—I published a book at the beginning of this year on genomics, big data, and mathematical methods for analyzing big genomic data. I thought it was a good idea to have a small book that frames our knowledge, an introduction on coronaviruses, how coronaviruses spread, how they mutate, and how this new coronavirus relates to other viruses that have been around.
For example, one of the chapters is on how this virus compares to SARS, an outbreak in South Asia and the rest of the world in 2002 to 2003. This virus is very similar to SARS, but there are several important differences. And there’s a chapter on how we know how infectious a virus is, how effectively it’s transmitting in the population, how lethal it is. Another chapter is about coronaviruses in general— where this coronavirus fits in the structure of the other coronaviruses we have around like the ones associated with common cold.
I was just compiling all this information and thought it was a good idea to share it. That was basically the main motivation and the main outline of the book. The structure of the book is based on questions and answers: what the closest relatives to the virus are, how it relates to SARS, how it relates to the flu pandemics and the seasonal flu, etc. So the book has these kinds of questions explained in the common language, but following only the scientific literature instead of all the noise around.
Do you think that since the scientific research is changing and developing so quickly, you’re stopping the story at one point in time by publishing a book now?
It’s just a picture, a timeframe picture, of the current situation. With this book, we’ll have a historical account of what the knowledge was at the time and how the knowledge has changed. There have been some developments—there are more things that are known about the virus than in the beginning. Therapies have not evolved very much, as you are probably aware. We are still far from getting a vaccine approved. At the moment, even the therapies that have been approved have very mild effects. But there are many ideas, and they are being tried, and things probably, hopefully, will change in the next few months. The therapeutic component, hopefully, will change the most.
There are other things that are not going to change: nothing is going to change about how this virus relates to other coronaviruses (it is a coronavirus, that is something we know), how this virus is evolving (the mechanisms of evolution have already been described), and how it relates to other viruses, like the SARS virus. Reading the literature in 2003 about SARS, it’s amazing how similar some of the patterns we are observing now are to what occurred in the SARS outbreak. We’ll know more about public health measures and measuring the effect in the next few months, but there’s a significant amount of knowledge accumulated from past pandemics. This is not something we do not know how to think about; we have the tools to start thinking about and framing some of these questions.
You talked about rectifying misconceptions--are there any out there that you’d like to talk a bit more about?
There is, for example, some controversy about the origins of this virus, how this virus appeared. We understand how this virus fits: what we know about the genome, how it relates to other coronaviruses circulating, and how coronaviruses evolve. Basically, there are two mechanisms of evolution of these viruses. One is mutations, small changes in the genome, where there’s a base changed to another base. The other mechanism is recombination, where there are different viruses infecting the same cell and creating new varieties. We know these things happen very frequently in coronaviruses, so by invoking these two mechanisms, we are able to explain the evolution and how this virus appeared. All of the ingredients were there—there was a deadly cocktail of ingredients that were already there in nature. I think it’s important to understand the origin of this virus before we start pointing fingers.
We already know, for example, that public health measures of control and mitigation we are doing, like social distancing, have been tried in the past. We have very nice historical accounts of the effectiveness of these measures, and we already have data from different countries. Some countries in Asia, like Hong Kong, China, Singapore, and Korea, have been very effective in dealing with the outbreak. I think the people there already had the mentality—probably going through SARS and MERS in the past— that this was a serious thing.
In the West, we were not prepared; the general public was not very well aware of what it was, and that happened also with politicians. The reaction was suboptimal, I would say. When measures were being implemented, there was already a severe overloading of the healthcare system, and it’s harder to contain once it’s widespread.
These measures work and we know that they are working. I think it’s important to have an understanding of data in the past and the data we have now. In the very beginning there was a scarcity of data that was quite significant, but now we are getting more information. For instance, we are getting better numbers of how many people are really infected here in New York. I think it’s very important to be data-driven. In the beginning, there was a lot of confusion regarding these numbers in part for the lack of data. These are some of the misconceptions that have been there and that we can start addressing.
Another set of confusions relates to the comparisons with SARS and with seasonal flu. I think they are very good analogies as they can provide the framework about how these infectious diseases work. But every metaphor has to be taken with a grain of salt. There are certain comparisons with flu that I think are quite inappropriate. They are very different viruses. They are doing very different things. In seasonal flu, we have vaccines, we have a significant fraction of the population that is already immune to it, and we have drugs working. None of these exist for this coronavirus. We don’t have drugs, we don’t have vaccines, and most of the population is not immune, so I think the comparison with seasonal flu is misleading, and it’s been very damaging, I would say. In human knowledge, there is a horizon of different things that we know, and when there are new things arriving, we have to frame them within this horizon. In general, scientists have the responsibility and the challenge of expanding the horizon of our knowledge and providing the framework of how to think about these new things. This is, I think, what we’ve been doing. Another comparison is with common cold viruses. There are coronaviruses that are already circulating in us and they are causing the common cold, but they are not very similar to this one. We have a significant amount of knowledge that can be used for framing what is going on.
Moving on to your personal research--is your lab now doing research on this coronavirus?
Yes—we are working on several things. This is an infectious disease caused by an infectious agent. In this case, COVID is the disease and the infectious agent is the coronavirus, but we also need to understand what the host is. A person can get it and be asymptomatic, showing no symptoms at all or very mild symptoms, and a person can die from the same virus. Why? We need to understand what is different between you and me and all the other people around. There are differences, and these differences are going to play an important role in the disease. There are questions related to the virus: how the virus is spreading, how it is evolving, where it is coming from, and there are questions related to the disease: how the disease is caused, how the genetics of the person are related to the disease, how the immune and clinical history or medications of the person are related. There are very interesting questions on both sides: on the virus and on the host (the person).
My work is mostly on genetics. I work on genomes, and we are compiling and analyzing information on genomes, from viruses and humans, trying to understand the severity of the disease based on several genetic factors. My research is on both sides. We are trying to integrate information from both sides.
I know you come from a physics background, and now you work in the department of systems biology. Your lab is also very interdisciplinary. How do all these fields inform and enrich the research that you’re doing right now?
The research that I’m doing is part of a recent revolution, I would say, in how biology is evolving. Biology has been a very descriptive science—traditionally before the molecular biology era in the 50s, it was just describing new species of animals or how plants interacted with the environment, these types of things—very descriptive. Then, in the 1950s, it became molecular biology. Molecules, proteins, RNA, DNA; the work, instead of going to the Galapagos Islands, was going to a lab. Since the last part of the previous century and the beginning of this century, biology has become very data-rich. There has been a lot of data accumulated. The human genome was probably a turning point. We realized that we have a very large genome and that we can read this genome; this genome provides instructions on how humans are made, and the same thing with many other species. In order to understand this biology, we have to get a lot of data, process these data, and create models where we can extract some information from these data.
And this is where my background and the background of some of my colleagues fits in. This is a realm that is beyond some of the traditional training of molecular biologists, classical biologists or medical doctors. This is what we are doing here at Columbia in the Department of Systems Biology and the Program for Mathematical Genomics: developing a new generation of biologists with different backgrounds, from computer science to physics. We are able to think about biological problems using more quantitative tools that are more traditionally ascribed to mathematics, physics, or computer science. In my group, we have people with very different backgrounds, and we are taking all of our expertise and trying to attack different clinical or biological problems.
What is your perspective on the future, regarding both your own research and broader impacts of COVID-19?
I think we are part of a change in the way we think about biology and the clinical work that many of my colleagues are doing. Biology is not the only example; for instance, the economy is becoming very data-rich. There’s a revolution in data in many disciplines—I think biology is one of the best examples of how this data revolution is evolving. Overall, the amount of data we are collecting is increasing dramatically. But getting from data to knowledge requires some thinking. We have to understand the problem we are working with and, on the other hand, we also have to have people with quantitative backgrounds who can develop these methods. I think this is generally the direction where things are going in the future. There are many of my colleagues here at Columbia in data science, computer science, physics, in different departments and the medical school, systems biology and genetics, that are embracing the fact that we are moving to a data-rich world in biology and medicine.
I think that’s a trend, and I think it is very significant, a part of a bigger trend that is happening across many disciplines. Journalists have to be able to handle big data if they want to report about, say, COVID and the deaths. You have to understand where the data is coming from. It is complicated, sometimes, all these things, but we need people who are able to understand and conceptualize what the problem is. And I think this example of COVID is nicely illustrated by people who are modeling, for example, how the virus is spreading or methods for designing drugs and finding antibodies. I think it’s just pervasive in all the domains of science and this is just one example. We need a combination of domain expertise, the doctor or the biologist, and enough understanding of quantitative methods in order to be able to put it in a framework where a computer can address important questions.