In this interview with Dr.Venkata Duvvuri, PhD, MS, MPH, data scientist and computational epidemiologist in the Hadlock Lab at ISB, interns Kalea and Yannell discuss his journey and his advice for anyone interested in STEM:
Can you talk a little bit about what a computational epidemiologist does?
When you go back ten years before, I don’t think you will see the term computational epidemiologist. By training I’m a public health guy, but I’m also a biologist. Since public health and bio are statistically very intensive, you need to know the epidemiology. How diseases are happening, how they are spreading, what data is worth rendering, and how to analyze your data. That’s really where you actually need to know the advanced level of biostatics. So, when computers came into the picture such as machine learning and high level computational programs, we started using them in epidemiology to inform what we wanted to understand from the data. That’s where the term computational epidemiologist was coined! I am constantly using computers, computer programs, and packages, to analyze my datasets.
There are two ways to become a computational epidemiologist. One example would be my path. I actually started my education with a PhD in biology, kind of related to public health, and then I ended up doing a masters of public health. Slowly, I see the job market and how it’s going on an educational level, and I slowly started learning machine learning, coding, etc. That’s where I became a computational epidemiologist. Some people having a PhD in computer science, statistics, or mathematics, can become a computational epidemiologist too. That means they know very strongly about programming and all those things, but they have to learn the science of epidemiology and so on. Suppose you have a very strong computer science background. To become a computational epidemiologist, you would have to learn the biological mechanisms, and vice versa if you started with a strong biology background. It depends completely on how you start your life.
How did you become a computational epidemiologist and how did you know that this was something you wanted to build a career off of?
I did my PhD in India. Where I lived, mosquito borne diseases were highly prevalent there. So, I became fascinated that this small mosquito creature that is just flying around is causing a lot of mess. I started wanting to understand how this mosquito spreads diseases from person to person or animal to person, different mosquito borne diseases, the different life cycles they have based on the virus they carry, etc. That’s the reason I started doing my PhD in Osmania University.
I worked on filariasis. I started looking at how filariasis is actually spreading. Is it only because the mosquito is spreading the disease or are there other factors that are influencing the speed and spread of the disease? Another interesting phenomenon that I looked into was the climate and host. In countries like India, in the rural areas, you can see that the drainage systems are not well established so you can see the stagnated water there. Those are actually where mosquitoes grow very nicely because of all the organic matter and everything else there. So, by moving some of the chemicals into the insect sites and removing those stagnated water supplies, you can possible control the disease. Those were the types of things that I was trying to look at. I was trying to see an infectious disease from multiple perspectives. The mosquitos will go and infect animals and take parasites from them in turn so that when they go for another blood meal from a human, they can inarticulate that parasite into the human. That’s how it keeps going on, so if you break any of these cycles so you can control the disease.
For that, I actually did a study where we identified filariasis in a part of Southern India and every 15 days we collected mosquitoes and identified them. Since there are many types of mosquitoes, we needed to know what mosquitoes are causing this mess. Female mosquitoes are the only ones that suck the blood and the male mosquitoes feed on the plant cells, so we needed to know if the mosquitoes in the data we were collecting were male or female and what type they were. We also needed to identify them and then dissect them to see if they have parasites in them.
Then, now that you have the data, out of the 100 mosquitoes from the one area we collected from, 90% have that particular parasite we were looking for. From there, we also had to collect blood from the humans living in the area (a small drop of blood from the finger). We actually microscoped them and were able to see that in this particular area, 90% of mosquitoes were infested and at the same time, we found most of the humans were actually having high concentrations of that particular parasite. Then we go and do a survey in the area that we are going to identify what factors are causing the mosquitoes to grow there and why this parasite hides there.
Suppose in this scenario of filariasis, we don’t have any animals and there are only humans and mosquitoes. If you think about any person already infected with filariasis, it doesn’t go away. They have to live with that all their life until death. So, if the mosquitoes go and bite him, they take some parasites from him and they will go and give it to some other person. In that case, we have to identify what are the determinants in that area and are there any humans that are already infected and have this disease. If there are a high number there, you would then recommend for the people living in the area to take certain medications to reduce the disease.
For my PhD, I worked with the national filariasis control program which is one of the WHO centers there and we worked together to understand this disease. That’s where I started, slowly I started learning all about the disease, how it’s happening in the field, what factors are influencing it, how do we understand that, and what methods we have to use for the analysis of the data. That’s where I started my life and how I completed my PhD, and at the time, I ended up having some small exposures to machine learning.
Then, I moved to the USA as a postdoctoral student. There, my PhD was not in mosquito borne diseases, but in working with asthma. In Maryland and Baltimore, I implemented machine learning again by taking in real time asthma patient datasets. We had a daily medicine kind of research; we provided all the laptops and necessary tools to the asthmatic patients, we identified asthmatic patients, and we collected data from them everyday through the laptops. Once we got the data, we then implied machine learning to help classify severity of patients and predict asthmatic attacks. That was my one year project there.
After that, I moved to Canada to take up another postdoc. But now I’m going into infectious diseases again since Canada is mostly infected with tick borne diseases. Before joining there, I had no idea about mathematics. I knew statistics, biology, epidemiology, but I joined this very intensive mathematics group. I started helping them create new ideas, and talked about how we bring them to life and what models we could utilize, but I didn’t know how to write the equations. I ended up talking to the mathematical modelers and through time I learned high level mathematics. There we created a kind of nation wide tick borne disease map, so Canada can kind of see which regions are infested mostly with ticks.
At the same time, in 2009, the H1N1 pandemic really changed my understanding of diseases. At that point, I was actually really into bacterial born diseases, but the pandemic really pushed me to infectious diseases. Apart from my statistical background, I’m the kind of guy that if something is advancing, I would like to go and learn about it myself. I started learning bioinformatics by looking into and understanding the genome, downloading free software and exploring them, etc. At the time of the pandemic I did a masters in immunology, so I have a masters in immunology, a master in public health, and a PhD in computational epidemiology. I became very interested in the fact that although the H1N1 2009 disease was a pandemic, it was not that severe because we didn’t see many deaths. Normally what happens is that once you are exposed to a new disease, your immune system actually evolves some of the antibodies. Those antibodies will help protect you from the same bug. In this case, this is a completely novel strain. We do not have any preexisting immunity in us, so why the disease was low in terms of deaths was my question.
I took an approach that used a bioinformatics application. Before that, since the 1960s and1970s, we have had seasonal H1N1. My understanding was that the seasonal H1N1 and the novel 2009 H1N1 might have a similarity between genomes which could be a reason why our immune system would be able to stop the virus from propagating into our body so that the disease severity is not that high. I said okay, we have some pre-cellular immunity, normally pre-cellular immunity antibodies help us prevent the disease so that we don’t get infected. Primary adaptive immunity are B-cells and the second are T-cells. T-cells actually have a memory index, so they actually look through entire genomes and normally there are lots of mechanisms in that. So, I looked at 4 T-cells that are memory T-cells, those are the cells that help us reduce the severity even though we are infected with the 2009 H1N1. That’s was my contribution to that. That gave me a position in Medicago.
Then in 2018 I moved to back to the U.S. I was really interested in moving towards the data science side of things because I’ve always seen not only my interest in how education is moving forward but also, what healthcare and the public are looking for. That’s very important.
You really need to crack down on how something is growing and advancing so you can keep up your skills and understanding with the pace of how the world is learning.
Venkata Duvvuri, PhD, MS, MPH
That’s where I started learning a lot of my coding skills. While working, I also spend a lot of time learning! I mostly spend time on work, learning new things, and reading literature. I spend the rest of my remaining time with my family. That’s how I gained this current position at ISB. This position gave me an introduction into a new area, Electronic Health Records or big data, so I needed to equip myself with big data and analytics. I learned mostly how to develop the infrastructure for the EHR because there are a lots of intricacies in it. The data are very much tangled and there is no normalization throughout the clinical labs. We need to know all those intricacies and how to deal with those things, so I needed to know how labs work with EHR and the international classification of disease. You don’t need to necessarily memorize anything, but you do need to understand those things. Very quickly I learned it because of my background in different areas. I joined ISB 5 or 6 months before COVID-19, but COVID-19 really helped me, despite all the trouble that it caused in the human population, by pushing me to learn new things and implement whatever I learned or created. That’s how I drove myself to adapt to the very fast paced environment. Overall, that’s how my life went from just biology to STEM based research and how it’s slowly growed up to this moment.
What advice would you give to high schoolers or anyone else who may be hoping to pursue a career in the STEM or science field?
When I started my PhD, there was no STEM education. When you go back to the 1970s, biology was biology, physics was physics, chemistry was chemistry, etc. there was no association between anything. I did my PhD in 2005 and at that time the statistics and epidemiology were joining together very well. So at that point when I did my PhD, I did not have an opportunity to become a STEM researcher, unlike today. That’s the reason I took all that pain of “okay I learned something but while doing my work I had to learn something else which I never did before.” By watching YouTube or taking courses I learned new things. For you guys, I think you have a great opportunity where you can see everything in one place. It really depends on how you decide your life and if you are interested in developing computational tools or programming. I think if you learn about the biological intricacies and if you understand much about these mechanisms, you can do well with your computational exports. If you want to be a biologist or a medical doctor, you also need to understand those and computational programming as well. In the future, one must know computers. If you understand biology, you can understand computers!
Last thing to leave off on, to excel in any career, I learned that you have to be very proactive and bold. Have no fear in what you want to pursue! That’s the key thing I learned in myself. Also, continuously read literature. Literature reading is very important if you want to do research. Spend time reading papers from different subject areas, it may end up being useful to your work.
Link to ISB Profile: Venkata Duvvuri, PhD, MS, MPH | The Hadlock Lab (isbscience.org)