Interview Highlights: Writing a Book About Coronavirus & Our Future in Data With Raul Rabadan
By: Hannah Lin (CC '23)
The following is a heavily condensed version of the full interview. If you're interested, read more here.
When did you start writing your new book, Understanding Coronavirus?
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.
I was taking my notes, looking at how the epidemic was developing, and decided to organize them. 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.
The structure of the book is based on questions and answers explained in the common language, but following only the scientific literature.
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 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 now known about the virus than in the beginning. There are other things that are not going to change, such as 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.
Are there any misconceptions out there that you’d like to talk a bit more about?
1. There is some controversy over the origins of this virus.
There are 2 mechanisms of evolution of these viruses:
Mutations: small changes in the genome, where there’s a base changed to another base.
Recombination: different viruses infect the same cell and create new varieties.
We know these happen very frequently in coronaviruses, so by invoking these two mechanisms, we are able to explain the evolution and how this virus appeared. I think it’s important to understand the origin of this virus before we start pointing fingers.
2. 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.
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. In the very beginning there was a scarcity of data that was quite significant, but now we are getting more information. I think it’s very important to be data-driven.
3. There is also confusion over 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.
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. Since the last part of the previous century and the beginning of this century, biology has become very data-rich. 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.
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.
What is your perspective on the future?
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. Overall, the amount of data we are collecting is increasing dramatically. But getting from data to knowledge requires some thinking. We need people with quantitative backgrounds who are able to develop methods to understand and conceptualize what the problem is.
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