Monique Chyba, PHD

PH: Good morning, Monique.

MC: Good morning.

PH: How are you?

MC: I'm OK. What about you?

PH: I'm doing well. Thank you so much for joining me and devoting your time to this project. I appreciate it so much. Do you have any questions for me before we get started?

MC: No, I think it's OK.

PH: OK, great. Well, we're already recording, so I'll just jump right in and introduce myself. We're doing an oral history with you this morning. My name is Pono hicks and today is October 5th, 2021. The time is 9:01 a.m. And we're doing this interview over Zoom. Just to start things off, can you please state your full name?

MC: Yes, my name is Monique Chyba.

PH: And where were you born, Monique?

MC: I was born in Switzerland in Geneva.

PH: Can you tell me a little bit about your maternal ancestors and how you and your family arrived in Switzerland?

MC: Yes. So that was a very complicated journey, actually. My parents are from Czech Republic and it was Czechoslovakia then. So for my mother, she comes from a very wealthy family. But during the 1950, when Czechoslovakia was under the control of the Soviet Union, they lost everything. And then my grandparents, they spent four years in jail after they tried to escape through the mountains. And my mom was a teen at the time. But then a few years later, she was forbidden to study at the university. But she managed to get a civil engineering diploma, which is honestly quite admirable because she had to take some exam under a false name in (inaudible). Then, years later, in August 1968, when the Soviet Union invaded the country to crush the Prague Spring movement, my parents decided to flee the country together. So they left with nothing but my three years old sister under their arm. And they traveled through Italy, and eventually they landed in Switzerland, which at the time was welcoming of Czech immigrants.

PH: Thank you. And when your family arrived in Switzerland, I guess during your upbringing, how did that influence you culturally growing up in Switzerland, but also with your parents being from Czechoslovakia?

MC: Right. So I was born in a very wealthy country, but from immigrant parents. And the one thing to know is that in Switzerland, the right of citizenship is the right of blood. It's not based on a birth place, so that means they had no citizenship for the first 12 years of my life. And I think as a kid I was pretty oblivious to it. But it sort of instilled in me the resilience as I was observing my parents restarting their life from scratch and including learning a new language later in life. So I do feel that as a result of that, I don't belong anywhere and that at the same time, I do belong everywhere. So it's a little odd to explain that, and it's a little odd not having roots, but I think it has some positive aspects as well. One thing is that education was always a focus in my family despite the difficulty and like my mom couldn't study at the university. They always pushed me and it was seen as a way out when you have obstacle being educated then play a major role. And also, Switzerland is one of the country with the best public educational system in the world. So again, I was really lucky to have my parents putting emphasis on education and as well be in a place that was really well set up, and I had those influences. I think it impacted my life a lot.

PH: Talking about your education, can you tell me what drew you to the field of mathematics?

MC: Yeah, so it's not very glamorous. It was always nice and playful to do math. I always like that, but I actually really decided to embrace them when I was about 19 years old. And as a woman, I was told that I should not study mathematics at the university, that it was hard and that it was a field for men. And so that totally convinced me to study mathematics.

PH: That's wonderful. Can you tell me where you received your education and training and what specifically you were studying in mathematics?

MC: Yeah. So I did everything in Switzerland, all my upbringing and all my education to the PhD. So first I got a degree in civil engineering and then I went into mathematics. And so I did everything B.A., M.A., Ph.D., at the same place in Geneva, at the university there, the Department of Mathematics, and I was doing what's called differential geometry.

PH: OK, and you're now in Hawaiʻi. Can you tell me what drew you here and that journey?

MC: Yes, so I was in Santa Cruz before Hawaiʻi, and I was looking at position in the U.S. in math department, and I think I applied to two one in San Francisco and one in Hawaiʻi, and I got the interview here in Hawaiʻi and then I got at the university at UH Mānoa and then I got an offer. And here again, I found myself as the only woman coming into the math department. They had no woman. So that really drew me. All combination, the fact that Hawaiʻi is such a nice place, the fact that it was a tenure track position and the fact that I could be the first woman in the department and hopefully pave the way that was really attracting.

PH: That is amazing. I guess now coming to the current pandemic that we're in today. Can you tell me when you first became aware of COVID 19 and maybe was there a point when you became concerned it posed a threat to Hawaiʻi?

MC: Yeah, I became aware, a little bit like everybody at the end of 2019. We started having those words that in some part of the world there was some type of virus that was spreading, but I think it was like everybody never imagine what it would become and that we would be in the situation we are now. I was thinking they're going to get a handle of it and it's going to be localized. And and then it started spreading. And I think it is really in like February, March, that it started to hit home, and then university all went online and then it was starting to really think, yeah, this is a major threat to Hawaiʻi.

PH: And you were teaching at that time, and I know you've done a lot of work with pandemic modeling. Can you talk about how you incorporated pandemic modeling into your teaching?

MC: Yeah. So Spring 2020, I was teaching a graduate class on introduction to dynamical system. So the students... I got them an assignment to look at the models that are used typically for epidemiological modeling and to try to do a little project. I proposed to do a team just with the students that were interested to work outside the class and start thinking about all those models. So that was done in a very ad hoc we in the spring of 2020 because we went online suddenly. So that added a little bit of a complexity to create a team and to work and to create some type of fusion. Yeah, that's what I tried. Like now, it's in more classes, but in the spring that was it.

PH: Did that research in those projects contribute to real world research? I mean, in other words, they weren't just for the purpose of learning, but they actually had practical application, I suppose.

MC: Yes, that came a little after like in March. Then in April, I started thinking with a colleague, Professor Mileyko here in the math department and Professor Kearney. She's a computer scientist from the the Maui Supercomputer Institute unit. And then we started thinking about like, can we try to... You know, the modeling was not really.... There were very little literature about how to do the modeling properly for enclosed environment like an archipelago or islands. So we decided to try to look into that. And then as we got the grant and created the group we hired students. Then that work really materialized in research that is now under review by good journals. We got good feedback. So it did materialize, but it took a while.

PH: Sure. Can you talk a little bit more about that grant and just the scope of what you were studying?

MC: Yeah. So we proposed to precisely look at those model and try to understand how to tailor them to that type of population where you can actually seclude yourself. And in an enclosed environment with smaller population that depend a lot on tourism or traveler, you have to really tailor to this specific location. So we received a grant from the National Science Foundation. It's called the rapid mechanism. So that means they disperse the funding fairly quickly. So you can address the issue. And most of the funding was requested for manpower for students in order for us to hire them and to train them. And it turned out that it became much bigger than that because especially with the Delta variant, it took like a few months to develop the models to really write them for Hawaiʻi. We went into two different types of modeling, so it was a lot of learning, adjusting, calibrating. And then when the Delta variant came, we were actually ready. And then it started becoming something big.

PH: Yeah. In terms of the students that you were training, did they all already have a background in mathematics or were they studying mathematics? Or were you able to train students that maybe you didn't have as much prior experience?

MC: So they're all graduate student in mathematics that we've had and that we still have working on that modeling. But the undergraduate student I have like, they come from all different type of majors, and they're learning. That's the job. We're training them. We're telling them how to look at data, how to be careful, how to ethically do modeling and prediction. So it's a bigger training component that they're receiving.

PH: I guess in as simple terms as you can, can you describe what factors you consider when modeling COVID and how you come up with predictions?

MC: Yes. So it's rather complicated. It started fairly simple because one thing to understand is that epidemiological modeling is in early stages of development. It is like the first type of model that we're using now they studied not even like a hundred years ago. When you compare that to physics that has centuries in the making right, we are the very birth of what we do and we're doing that in the midst of a pandemic. So we started with baby models, basically. And then we started growing them to incorporate all the important features like tourism, like small population, age, demographic, ethnicity, all all of that. So in lay term what is difficult, what are the parameters that we have to use? We have to understand the specificity of being on an island, and we have to put that into consideration in the model. So the main parameters for COVID transmission from person to person. And that's one thing that we did not understand globally at the beginning. We did not have a good understanding, like people thought that you could get COVID by touching a surface at the beginning, that you could transmit that way. But it's not really the case, and we didn't really know how it transmitted from person to person. There are still some question marks about asymptomatic and how they transmit, so that's one parameter is people to people transmission one of the important variable. Then other variables that are important and that came into the model is travelers, then other parameters that are important are vaccine. Those have been very key in the model. And every time you have a new parameter, your model grows. It's conceptually more complicated. You have to create buckets of different communities. Here are the vaccinated. So what happened to them and how do they interact with the unvaccinated? And how do you link the bucket with each other? So that becomes quite intricated. And then you have mitigation, like lockdown restriction on gathering. So all those are the important components that goes into the model.

PH: And you also mentioned kind of the unique nature of Hawaiʻi being on an island. For that reason, did you have to rely more on, I guess, the local research? Like you couldn't rely on models or other information pulled from the mainland? Or were you still able to use those to help inform the models here?

MC: Yeah. So we used some models that were created for more global that should be adaptable. And then we had to adapt them. For instance, we had an agent-based, its a network type of model, different conceptually very different, from the Institute of Disease, from the Global Health Bill and Melinda Gates Foundation. We worked with them, and we got their bone structure for their model. And then we mingled with it to tailor it to the specificity that we have here in Hawaiʻi.

PH: Interesting. And can you tell me when you came on board with HiPAM, and what your responsibilities have been there?

MC: Yeah, so HiPAM was Dr. Fan. She started HiPAM from the Public Health Center for Aging when it was clear that working on a pandemic is not a one expertize job. You have to bring people together. It's interdisciplinary. You need the expertize of others, you need expertize of biologists, you need expertize of epidemiologists, computer scientists, mathematicians, people in the public health, in the hospital, Department of Health. So that was created to have this sort of core of people, and I came on board in the fall. They invited me when they saw that I received the NSF grant, and we were meeting once a week, and my role has really been about the modeling, bringing that expertize to the table.

PH: Sure. You talked a little bit about it already, I mean, in terms of living on an island and various groups that interact with each other, but are there any other challenges that come into modeling this pandemic?

MC: Yes, so we like the fact that we are on an island. Disease have been decimating local population for a very long time since people came to the island. So being on an island is nice. You can seclude yourself. But at the same time, if something comes, you are extremely vulnerable. Like we are limited in terms of our capability, our capacity, like suddenly in the month of August, we are very concerned having enough ventilators and having enough oxygen to address the issue. So then you have to bring that from outside from the mainland. It was a very big job to make sure that we had all the capacity we needed as the hospital were getting overwhelmed with COVID patients that had to go into ventilation. So we can protect ourself really well by just turning off the travelers. But at the same time, we are very, very vulnerable to external inputs. And now we are even more vulnerable because if you shut down, you shut down the economy.

PH: Yeah. Who would you say has utilized these models or benefited from these models the most?

MC: Well, that's a good question. That's a very delicate question because I actually do not really know. I don't talk directly to the government or the political leaders. There are some buffer between me. That's not my job to do that. So I'm not sure to what extent they have been used, but they have been mentioned. So I think they have been looking at what the model are saying. I think the model have helped also for the Delta variant, the model, while it was very, very controversial, and there was a lot of criticism. In early August, we made a prediction that the numbers are going to spike to possibly very high level and that was a warning. And I think it was heard actually, even though we got a lot of heat from it. It was heard and that was the purpose of it. And then there was some gathering restriction. There were some action that came that allowed to curb that very exponential. So I think it's been heard. It's difficult for me to point exactly by whom and how. The direct input that I have is the students. I think they have really heard what is needed. They have learned a lot of what it means to work on a concrete application of that scale of that impact. And I'm using it in some other classes like Math 100 right now.

PH: Yeah, and you talked about sharing those models with your students. Can you just talk about generally how you work to make pandemic models more accessible, maybe to your students, but also to the public?

MC: Yes. Again that's hard because the model became quite complex, even though we had the early stage of the development. But there is a big misunderstanding about what pandemic modeling is and the purpose. So most of what we've tried with the public, we've done some animation that we're explaining and trying to see if you change some parameters, this is how the model respond to it. It is hard to share conceptually the mathematics that are behind the model. With the student it's OK, but with the large public, it's a little harder. So we're trying to educate about what they can do rather than how they really build it. I have to say, it's been a learning curve. It's not obvious. People want a miracle that is going to tell them this is the number two weeks from now. That cannot happen because a pandemic is people driven and government take actions that you cannot speculate on suddenly. So it is not an oracle, and we're trying to tell this is what it can do, but it shouldn't be seen as an accurate forecasting numbers.

PH: Sure. And you talk about as a professor, you worked closely with your students and I'm sure observed some of the difficulties that they faced during the pandemic. Could you talk about some of those challenges that you observed in your students and how those from different backgrounds responded to the pandemic?

MC: Yeah. I taught Math 100 in the fall of 2020. I taught it in spring 2021, and I'm teaching now in fall 2021. And I have some survey, and we've done a unit on COVID where they looked at the numbers. And then I asked them question how it has impacted you. And we've sent some survey. And I have to say, there is a big range of different impacts. Some it impacted in more shallow life of, like, OK, I couldn't go to graduation for high school. And that I understand, and it's a little bit frustrating. But at the same time, there were other students that were impacted in a much more profound way. Although many of them actually lost their job especially in fall 2020. A lot of them work in a restaurant, and they tried to make some money. They lost that income that impacted them a lot, whether they could continue studying at the university or not. They also lost family members. Some got COVID. So it was really hard. And then suddenly they had to face a whole new world on how you go to school through a screen. A lot of my students, I was the only person that they would see that day and interact with, and I felt it was a lot of responsibility. I think it was hard, and it still is because it's lingering. We are little back, more in-person, but I think the mental health has been affected. The young generation likes to go out. They like social interaction. And it's been really, really hard.

PH: Could you maybe also speak to some of the challenges that you faced as a professor moving online and maybe also maintaining student engagement like you mentioned through a screen and the challenges that poses?

MC: Yeah, I have to say, as a as a professor, I didn't experience a lot of obstacles or challenges. I think I got really lucky because I could still do my job. We were asked to transit to online, but I didn't lose my job, so I feel very grateful for that, and we transited online. It was a little hectic at the beginning, and we all learned, and suddenly, the connection goes out and, (inaudible). But I tried to do as much as I could to keep the student engaged, but it's not the same. When I teach 200 students and I have a Zoom with 200 people, it's really hard to make a personal connection. So we broke it into small pieces. We have breakout room where the students work together. We try to find alternative in order to engage them, but it's not trivial for sure.

PH: I guess going back to your models, how do you think those models and the data acquired from this pandemic can potentially prepare Hawaiʻi for the remainder of COVID 19, but also future pandemics? Do you think we're better situated?

MC: Yeah, that's a very good question. I think if another pandemic was to hit us quickly, we will be slightly in a better shape, but not much. I think there is a lot of work that has been done, and hopefully, we'll have some time between the end of that pandemic and the next one to do the research. We understand the challenges. That's about where we are. We understand what didn't work. We understand what has to be done, but that has not been done. So we're going to need a few years to really go back to revisit that pandemic, to address those challenges, to build the proper tools, to do experimentation precisely, to understand transmission, mask efficacy, like how do you wear them and how it helps, etc. to avoid confusing messages to the public. There was a lot of confusion during this pandemic about what works, what does not, what should be done, when, how and we need a few years to look into that. But your question is interesting. We just applied with a group here at UH engineers, biology, computer scientist, mathematician, public health. We came together to apply for a larger grant for the National Science Foundation that is sort of a development trend toward the possible prevention pandemic center. So the chances are small because it's always small the chances to get a grant. But there is really awareness about the fact that a lot has to be done before the next pandemic hit.

PH: Yeah, definitely. Do you see anything drastic changing in the models themselves or how you go about that process?

MC: Yes. I think the model have to be rethought. They're good, but they're very like, you have that single bump. So they're going to have to incorporate people's behavior. We can now have an idea of some pattern when this happened. This is typically how the public react when this happened. This is what we can expect as a result from the public behavior, which I know we're still speculating a little bit, but as we analyze the past, we're going to have some pattern and we're going to be able to incorporate that into the model and then be less on the speculative side and be more on the forecasting one. And also the model has to become multiscale. Right now it's not doing that. It has to go from modeling the little particle in the air so that we understand transmission to then modeling contact from people to people to then modeling the spread within a population and then eventually worldwide. So there is a lot of different scales that as of now are addressed independently, and they would have to come together as a single model.

PH: Yeah, that makes sense. How do you think COVID 19 helped prepare future generations, especially those that you worked with, the mathematicians and pandemic modelers? Do you think that the pandemic, now that we have students who have been trained and experienced it, are more prepared for the future?

MC: They are. When you do a work like that on the pandemic, during a pandemic, it is a little bit of an aha moment of what it means. I think the students have learned. Like, they'll be able to work in urgency situation where something is needed quickly. They understand the challenges. They're learning how to communicate, how to be careful. They're learning how to use data. Data has been a big issue during the pandemic, and unless you start really digging and working with it, you might not realize that the amount of data that we have is so big that then it's called an infodemic. You have too many data, and then you have to be able to sort what is the exact data that I need. And this is not just pandemic related, it's for any type of data driven problem. So those students will be able to move and work on other major issues and challenges like climate change, like renewable energy. They will be equipped much better having gone through the training during a pandemic. I think.

PH: Yeah. For you personally, how do you feel your upbringing coming from Czechoslovakia and then being in Switzerland helped you adapt to the changes that were brought on by the pandemic?

MC: Yeah, that's precisely that. Adaptation. I've moved. We've had to adapt. Speaking one language, having to speak another one, then learning this, having something, having nothing. Being a citizen. Being not a citizen. I've been an immigrant three times in my life as I moved. So I think I was very prepared because you learn to be adaptable and that's exactly what we had to be. And we still have to be during the pandemic.

PH: Yeah, I guess you saw many parts of the world growing up in Europe and then moving throughout the United States and then now here living in Hawaiʻi. From your observation, are there any positive or negative aspects of living on an island during a pandemic? And especially, Hawaiʻi and the culture here?

MC: Yeah, so I had to go to the mainland once during the pandemic. I went a couple of times and I could see the behavior of the people here in Hawaiʻi. People have been very compliant. They've been very compliant wearing the mask. There is some frustration, but I've been very impressed on how people reacted to the pandemic on the island and how they addressed and how they responded. And so a big kudos to the Hawaiʻi population because when I went on the mainland, I could see people couldn't care less about wearing a mask and going into a store or this or that, they were frustrated. They don't like restriction. Nobody likes them. However, you have to go through that if you want to address the pandemic. Vaccination. The State of Hawaiʻi has done fairly well into the vaccination, so I've been impressed with the Hawaiʻi population and community.

PH: And after moving throughout the US earlier in your life, can you talk about what drew you to Hawaiʻi and why you chose to stay here?

MC: Yeah, that's true because maybe I'm at the exact antipodal point from where I started. So I felt if I keep moving, I'll go back to where I started. Maybe it's because I'm as far as possible from where I come from (laughs). No, I don't know. It's just the quality of life is just for me Hawaiʻi is breathtaking, and it has a quality of life that is really very appealing to me. I like the nature. Again, I like the community here in Hawaiʻi. I feel very embraced by the locals, actually. And I found my roots at the University of Hawaiʻi. So this is what drew me here.

PH: That's wonderful. I guess taking away from the pandemic so far, are there any lessons that you've learned from this past year and a half, either personally or professionally with modeling?

MC: Yeah, I've learned to believe in the new generation, and I like that. There is a tendency as you get older to hammer the younger one, or they're not doing this, they're not doing that. But I am exposed to a lot of students, and I like them and I have a lot of faith in their spirit, in their mindset and them helping. I think we messed up. So I like the new generation, and I think the pandemic showed that they might be the one that are going to make it better eventually.

PH: Wonderful. Well, thank you, Monique. I think those are all the questions I wanted to ask you this morning. But is there anything else that you would like to share or any final thoughts you want to leave with?

MC: No, it's OK.

PH: OK, well, thank you so much for offering your time and sharing your insights. I really appreciate it. I'll stop the recording now, and that'll conclude the interview.