By: Makena Binker Cosen (CC '21)
Tell me a little bit about your background as a researcher. I study infectious diseases. I’m in some sense an infectious disease epidemiologist; although my training is in climate science, atmospheric science and the like--actually geophysics. I do a lot of work studying how the environment, whether hydrology and climate, affect infectious diseases. I look at some other elements of health as well, but my big focus has been infectious diseases. Over the years, I’ve worked on the two of those together, but I've also worked on each topic separately quite a lot. I’ve done some just straight research on climate; and I've done a lot of work where I’m just looking at infectious diseases. When I work on infectious diseases, I very often work on using mathematical modeling systems, in which I try to represent the virus as a series of deterministic equations that describe the propagation of the pathogen through a population, a community, some aspect of its transmission characteristics. We often use these models now, in the last decade in particular, in conjunction with what are called “Bayesian inference methods.” These are statistical methods that allow assimilation of observations and use these measures to arrive at a truer representation of what the model’s behavior should be. In other words, we inform the model by observations of what’s actually going on. This is something that is done in a lot of disciplines-- it’s done in engineering design, it’s done in numerical weather prediction-- where you take information about a system and use that information, which you know in and of itself is flawed, because observations have error and noise associated with them, and you’re trying to bring it into a system so that the system you’re mimicking, is better constrained. So, we’ve done a lot of work with those systems; and there are a lot of questions you can ask. You’re going to ask questions like, “Can I understand the fundamental properties of the virus, or pathogen, that I’m studying? What are its transmission characteristics? How long is a person infected on average? How long are they latent? How transmissible is this virus? How are environmental conditions modulating transmission? Are they important and to what degree?” You can also use it to study interventions. Once you have a model that you think is representative of the system it describes, you say, “Well, what if we did this or what if we did that? What if we had done these?” These are counterfactual simulations that allow examination of varied situations-- if we were to immunize this many people over time, how effective would that be, given this efficacy of the vaccine, this rate of compliance in uptake, this timing of deployment--what does that mean versus another strategy. And the last thing you can do with those types of systems, is you can also try to develop forecasts of what is going to happen. These methods, which combine mathematical models, data observations, and these Bayesian inference systems, are similarly used as the basis of numerical weather prediction. That’s how they make any and all the forecasts that we rely on all the time. We’ve taken that same suite of methods and we’ve transferred them into an infectious disease system, where we have a mathematical model of an infectious disease system. We have the same types of Bayesian inference methods, and we have observations of the disease system, like the number of cases of somebody with COVID-19 or Influenza, or a diarrheal disease. So, we work in sort of that milieu--where we’re trying to understand the properties of what’s going on, trying to use that understanding to inform what it means about what we should do and scope out types of interventions, controls, and consequences of our actions. We’re using it to make projections; and if the system is what is called stationary, when we’re not really impinging upon it, then we can make forecasts that have some sort of accuracy that we can quantify. With this background, what features of the coronavirus do you think have made it particularly dangerous as a pandemic? It has three features that are particularly problematic, that make it the most challenging pathogen we’ve faced since 1918, as a world population--humans that is. Firstly, it is a newly emergent respiratory virus, to which the majority of the world’s population has no immunity. We’re sort of like the dry kindling to it, and it can spread really quickly through our population. The second is that it behaves as a lot of common respiratory viruses do, in that the majority of infections are undocumented. People have little symptoms or mild symptoms; not enough to motivate them to go see a doctor, and not enough to often motivate them to stay at home. So, they’re out and about, spreading the virus broadly in the community, contributing to the silent or stealth transmission of it. The majority of time, you or I, get an infection from a cold in the winter time, we get a sniffle or sore throat or body aches, we don’t stay home. We still go out, go to school, go to work; do the things we need to, go out with friends. If we have a trip, we’ll still get on an airplane. What we’re doing when we do this is we’re spreading the virus broadly in the community. This is how the common cold gets around. This is how influenza gets around. Because there are enough of these infections that set up these silent, stealth trains of transmission that allow a virus to go along undetected by people unwittingly spreading it to other people, not really being cognizant of just how ill they really are. And we’ve seen this ourselves because we’ve done testing of this in the broader community, where we can go to a major tourist attraction in NYC and we can solicit people for testing. They tell us their symptoms, we swab them, we take it back. In February, we found that one-in-nine persons walking around, ambulatory adults, was shedding a common respiratory virus. It’s out there. There’s a lot of it out there and there are many chances for an infected individual to potentially give it to somebody else unwittingly. This virus has that property. Not only do you have the majority of infections being undocumented, additionally for documented infections there is evidence that people shed the virus before they are actually symptomatic. That’s pre-symptomatic shedding. Even the people who will ultimately seek clinical care and be documented as having COVID-19, they were contagious before they were aware that they had symptoms. So, because of these properties, this virus gets around really fast. That’s why it spread so quickly in China, that’s why it started hopping on planes and going to Thailand, the US, Japan, and South Korea. That’s why it spread throughout the world in a matter of a month, really from mid-January to mid-February. The third ingredient unfortunately is that while the majority of infections are undocumented, there is still a fat tail. There is still a substantial number of people for whom this causes severe outcomes, critical complications, and puts them at risk for death. There is a substantial infection-fatality rate, that’s probably somewhere between 0.2 and 1.3% overall. That’s the infection-fatality rate, not the case-fatality rate-- which means it’s probably really between 0.5 and 1% I would guess. This means between one in one hundred, or one in two hundred persons, who get infected from this, including all the undocumented, will die from it. When you do the numbers, and you say, this is a novel, respiratory pathogen, to which the majority of the planet has no immunity-- everyone is susceptible to it--and given how aggressively it’s transmitted, you’re expecting 50-70% of the world to be infected with this before it really burns itself out or settles into a pattern of endemicity. That’s four billion people, let's say, who will be infected by this virus, if we do not check it, if we don’t come up with an effective therapeutic or vaccine, if we don’t slow its progress by all the non-pharmaceutical interventions that we’ve been utilizing in the intervening time while we wait for those pharmaceutical interventions. If four billion people were to get this, and one in two hundred of them were to die, that’s twenty million people. That’s the worst pandemic we have seen since 1918. The worst-case scenario is clearly very bad. The combination of characteristics this has: high susceptibility because its a novel pathogen, the fact that the majority of infections are silent, undocumented and they allow and support the transmission of this virus freely in the community, when people don’t know that they are actually contagious; and the fact that there is still a substantial number of people for whom this will have critical consequences. You put all that together, and that’s why we’re looking at such a really dire circumstance. What is your team doing right now? What kind of simulations is your lab running? How do they work exactly? The models can be more simple or they can be more complex. We’ve run models at NYC scale, in conjunction with the Department of Health and Mental Hygiene; this is work led by Dr. Wan Yang, who is an assistant professor in epidemiology, and tried to provide them projections of what is going on and understanding of where this virus is and where it may be going within the city. We’ve looked at it in China, and actually have tried to quantify those undocumented infections and how contagious they are. That is something that we did back in January and February. We’ve applied a similar model at a county scale to the entire United States, and used that to try to understand the epidemiological characteristics of the virus, whether particular locations were in a position they should be loosening restrictions. In other words, was the reproductive number is well below one. And one is that critical line between growth and diminution of cases as you go forward, and you’d like it to be below one, so the outbreak is dying out as you’re thinking about opening up things and allowing for more activity. We’ve used it to make projections as to what’s going to happen. I call these projections, and not predictions, because the system is not stationary. Not only do we have uncertainties as to the data quality, differences in testing rates, and things that make it hard to actually understand and identify the system well to make what would be a forecast, we also have a blindspot that we don’t know what happened over the last ten days, because there’s a lag between when someone acquires the infection and when they’re confirmed as a case. Even more importantly, we don’t know what people are going to do in the future. When you make a prediction, you’re assuming that behaviors are the same. When you make a weather forecast, your assumption is that you can’t change the weather, and nobody’s going to go out there and try to disrupt a hurricane if it’s coming your way. We have no means of actually doing that. When we make a flu forecast, there’s the assumption that we’re not going to do too much different than what we typically do to disrupt the movement of the flu and its propagation through the population. That’s not the case here. We’ve completely disrupted our economy, we’ve shut businesses and schools. We’re leading very different lives right now than we were formerly. That has had a marked impact on disease transmission. We don’t know how much we’re going to stick with that going forward. We don’t know what kind of loosening will take place. We don’t know, even if we have that loosening, which we know when they’re going to do it, they’ve already done it for a lot of these...we don’t know what the outcome will be. We don’t know what the consequences of those loosening restrictions will be on opportunities for the virus to be transmitted from person-to-person, which is really the fundamental outcome we’d like to know. The reality is that if a particular location opens their businesses, or allows their businesses to open, we don’t know how many businesses will open. Some will choose not to do so, because they consider it unsafe for their employees or their customers; or it’s not economically viable for them to work in a partially open economy. Then, we don’t know how many customers will frequent those businesses. Are people going to restaurants? Are they actually going to use them, or are they going to be shy about doing it, and how that might vary from place to place? We don’t know what kind of social distancing will be enforced--if at all. Will they enforce it or will they not? Will people wear masks? Will it be outdoors or indoors? What are the effects of changing weather conditions and humidity on this, is this virus seasonal? There are so many things unknown, that making a prediction is not possible. So we instead make projections. And you might say, “so, why make them at all?” The reason is to scope out what are the possible outcomes. When we make a projection, we assume certain things will happen. We assume that maybe they’ll be a five percent increase on a weekly basis, in the transmissibility as more and more people get relaxed, and there’s less and less compliance with control measures, more businesses opening, and less compliance with social distancing and face-mask wearing. Maybe we’re getting more cavalier. That might be one scenario- when you say that the ultimate outcome of all those effects is this gradual increase in the force of transmission. What’s the consequence of that? Or maybe there will be no effect at all, and you can just say that it’s all going to stay the same. Business as usual. But you have to lay out these scenarios. By doing that, you can see what the sensitivity of outcomes are to those future paths. No matter what we do looking at growth, or no matter what we do looking at the virus declining in locality, or is it dependent on what we do? And if so, what does that mean about tracking and understanding what needs to be done, and the responsiveness that we need to have. It’s really important in this situation that we’re very responsive. When there are outbreaks taking place, we would like to see the states and communities responding aggressively. Unfortunately, that has not been the case. How easy has it been to get reliable data? Do you expect there to be any limitations or biases based on this? Certainly there are biases in the data. The deaths are undercounted. Not everybody is tested, not everybody is known to be having COVID. If you look at some of the preliminary studies, the NY Times even did one for some European countries showing excess mortality versus COVID deaths; and they found that there was like a 20-30% gap between them, indicating that excess deaths were higher than they would be otherwise. Right now, you might expect, maybe there are a few more deaths because they can’t do heart surgery the same way in places where hospitals are overrun with COVID, or they can’t treat accidents the same way; but you could also expect there to be fewer traffic accidents because there are fewer people on the road. So how that all comes out, I don’t know. But what it really indicates is that there is a sizable undercount. I’ve also had someone reach out to me from a hospice place in rural Georgia saying that, “We used to get 1-2 people coming in with sepsis each year. Now, everybody has sepsis, whose coming into die, and none of them have been tested. They all are COVID patients, but they are not known to be.” This can be very bad in some places. Brazil, news reports suggest, is grossly undercounting cases in deaths due to COVID. I mean, substantially, maybe by a factor of two or three or more. So, there are real problems, and it varies considerably from country to country based on what their practices and policies are for it, and what their capacity is to test; and even serve people in their health needs, particularly when you get into the much more marginalized portions of the developing world. In the US, there are biases in the death data. There are shifting biases in the case data that depend on testing and what not. Our job on using these models in inference systems is to allow for and account for biases in the noise in the data. Remember, in the very beginning I said, “We know that data is not perfect. They are noisy in and of themselves.” That has to be explicitly accounted for, because you can’t trust the observations. They are not the Gospel. There is some unknown truth out there that we’re trying to estimate better, by using both the model and the observations in conjunction with one another. With that in mind, what is the importance of testing and contact tracing right now? It’s vital. If you look at the countries that have been very successful with dealing with this. It’s a hard problem. Basically, we are looking at something where we’ve been tasked with a very hard problem. We need to suppress the virus using non-pharmaceutical interventions, and not substantially disrupt economies in the process of doing so. If you look around the world, every country in the world is tasked with this problem, and almost all of them have failed. They are ignoring the problem, or they are outright not dealing with it well. There are a handful of countries that have done it well. Vietnam, Taiwan, Thailand, South Korea did well after dealing with a large outbreak. China, in a very different, authoritarian way has been able to handle it. It’s interesting that these are countries, near the epicenter, have also had to deal with SARS and MERS, have had experience recently in dealing with infectious diseases, which I would argue has primed them for it. There are other places as well. Germany has done better. Israel, New Zealand, Iceland. They have done well as well. South Korea, because it is a densely populated, highly populated modern country, that is well-connected with the world economy, and people fly through it all the time, is probably the best example to look at. They aggressively developed tests when this virus emerged, the same day by the way that it emerged in the US. They had their pharma companies develop a lot of diagnostic tests, that were then verified by government labs, and then flooded the market and made ample opportunity for anybody who really wanted to be tested. They had laws in the books already that allowed them to use people’s credit cards, video surveillance, and GPS records from phones to contact trace. They could actually go into people’s information to actually support this effort to control the virus. They also had a quarantine act that allows them to enforce quarantine for individuals who have been in contact with someone who is infectious. They used this, they shut their schools. High compliance in use of face masks there. They didn’t shut their businesses. That’s not to say that their businesses didn’t take a big hit initially-- no one was going to them comparatively. But, they managed to really squash the virus. Since then, they’ve further reopened things. More people are using the businesses, high use of face masks. They’ve opened houses of worship, baseball season going at stadiums. They’ve opened night clubs. They’ve driven it down to single digit numbers of cases per day. Since opening the night clubs, they’ve had clusters of outbreaks. They had up to eighty cases in a day, usually around forty. Now, forty cases a day, if you were to just go with that number, for a thousand days, which is almost three years, that would be forty thousand cases, right? If we were to say that maybe there are ten times as many infections overall as there are cases, which is probably a high number for Korea, because of how well that they test. Let’s just go with it. That would be 400,000 people infected in the next three years roughly. That’s less than one percent of their total population. What it means is that South Korea, in their new normal, with reasonable economy, maintaining their unemployment rate at four percent, could hold on for three years while the world tries to develop an effective vaccine or therapeutic. And they could deploy it before 99% of their population is ever infected with this virus. Now, Korea’s population is 51 million people. It’s about one-seventh the size of the US, actually a little bit more than that. Forty cases a day would be 280 cases a day in the US. In the US right now, we are at 20,000. We’re nowhere near that; and we’ve disrupted our economy and skyrocketed unemployment. So we’ve unfortunately failed at both. We’re not alone. Lots of countries have. Right now, we seem unfortunately to be ignoring the problem too much, and ignoring the fact that this can take off exponentially again, and that we may be catching a break in summer, because it may be somewhat less transmissible in summer. But if we’re really complacent about this, and we’re not willing to reimpose the kinds of isolation and shelter-in-place methods--this virus will run over us. And accepting that in the US, accepting that a million people will die from it, seems really, really like a failure of leadership. That’s a lot to take in. With how quickly everything is evolving, how often do you find yourself needing to update your projections, or how often should people that are following up on projections check again to say that they still hold over. We update our projections twice a week actually. So, we bring in new information from the last three or four days, and we update the projections again. We also change our scenarios. When we were first doing this, we were just imposing scenarios where there were further restrictions. In March and April, we were looking at people using shelter-in-place, restrictions on mass gatherings, closing of businesses and schools, face mask use, and social distancing, to control it. We had scenarios where there was increasing control of the virus invariably so. Then, once they started to loosen restrictions, we had to change our scenarios and say, “Well there are possibilities of growth because they are loosening them now.” We don’t know how much. We’re again having to reconsider them based on what is going on. Has your team modeled how protests and the response to protests may influence COVID-19 outcomes? It’s too early to tell, because they’ve only been going on for twelve days now. That’s about the length of time between somebody acquiring an infection and being confirmed as a case. Certainly, you look at the protest and demonstrations; and in some places you see a lot of face mask wearing, and it can be during the day time and it’s outdoors, and those all may help preclude the transmission. But you bring people together and you would figure that you are providing more opportunities for transmission. This is not to diminish the need for the protests, because that is a choice, and these issues are very important, equally important, if not more important, and they are public health issues as well. There are other locations where people are not wearing face masks at all. You just don’t see that level of compliance there, and that’s more concerning. But the truth is that we don’t know. We don’t know what this all comes together to mean. If being outdoors in the open air, where the wind whisks things away, and if people are wearing face masks, and if the majority of protestors are in the sunshine, and UV radiation breaks down the virus when it’s exposed to it, then the risk may be slight, may be nominal. On the other hand, it may be large. We don’t know. As of yet, we don’t know because we haven’t also had enough time to see whether or not this is resulted. The same thing goes for all the other loosening restrictions. So we’ve had people getting together and going to the beach on Memorial Day. We’ve had them walking on boardwalks and going in stores. You see shots of people going into nightclubs, bars, and restaurants; and not wearing masks as well. Those environments may be more problematic because they are indoors, at night, and people are pretty packed together as well. They look like opportunities for super spreading. It’s really hard to pin down why we’re seeing growth in some locations in the US and not in others, why some places are still declining, and why some places like California, Arizona, Texas, Florida, the Carolinas, Alabama, are seeing growth in cases. It’s difficult to know what’s going on. There are changes in reporting and testing practices over time as well. We don’t have access to information on the microscale processes that might be supporting or not supporting transmission locally.
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You both are RN, PhDs. What does having that degree mean in terms of the work you do everyday? Is it purely research/teaching? Is there any clinical work? Rossetti: My background is as a critical care nurse. I worked in many different intensive care units over the course of my clinical career. I do not practice clinically anymore. What I do now is full-time clinical informatics research. I received my PhD at Columbia from the nursing school in nursing informatics, and I completed a postdoc in biomedical informatics also at Columbia. Much of my work is in the applied space—informatics brings information science and computer science into the clinical sciences. I work a lot with electronic health records focused on patient safety, studying how systems can really come together and make patient care safer and more efficient. Let’s back up to the pre-COVID days. You 2 are the principal investigators, or PIs, of a study called the CONCERN study. I’ve now done enough interviews to know that scientists love acronyms. Can you talk about CONCERN? Rossetti: It stands for “Communicating Narrative Concerns from RNs.” Essentially, we know that nurses are at the bedside in the hospital with patients observing them the most of any clinicians in the hospital. They observe when patients are not doing well, they observe when patients have signs and symptoms before vital signs change that indicate that they are going to have a potentially bad event in the hospital, such as a cardiac arrest or death. We are actually able to detect when nurses have this level of concern about a patient because we look at how they record information about the patient. They record information more frequently, because they’re going into the patient’s room more frequently, and they’re paying close attention to certain things. So we can see that pattern in the electronic health record (EHR) and we’re able to use different data science techniques to identify early when a patient’s at risk for deterioration for a bad event. We can use that information in a prediction model so that the entire care team can be better aware earlier that the patient is at risk. So this really is a way to improve communication among the care team; the nurse is worried about the patient and is noticing these things, and we want to escalate it and make communication really clear that this is an at risk patient. Clinicians, the whole team, they’re very busy and they’re caring for multiple patients at once. So we surface that information to say, “Hey, you need to look at this patient more closely.” How did you both get together and come up with the idea for this study? Cato: Dr. Rossetti had started the preliminary work on the CONCERN study, and we were both in graduate school at the time at Columbia. She reached out to me to do some natural language processing work on the study. We started trying to find various signals in the data to predict patient degeneration. It was through those analyses that we started realizing that for our first paper, there was qualitative work that had been done, where nurses had indicated their concern about a patient and the increase of surveillance that they placed a comment in a nursing flowsheet. But it was really the quantitative analysis that showed that signal for deterioration. There was a signal for increased nursing surveillance that had an association with deterioration of patients. That analysis led to deeper analyses and other data mining methods. It all stacked up on each other and here we find ourselves now 8-9 years later. What are the problems with the existing electronic health record system? Cato: I wouldn’t exactly phrase it as problems, but with any system or interface, you have constraints because of the way it’s built. Before we had electronic health records, it was easier for clinicians—nurses, doctors, social workers—to instantly get certain information. It was easy for them to know if there was a really sick patient or if there was a patient who had been in the hospital with lots of interactions because their paper chart would be thicker. Also, information could be highlighted very quickly, like pulling the paper out of the folder a bit so it stuck out or putting it on top of a section so you’d know it was the most important thing. All of those kinds of ways of communicating information got lost with the transition to electronic health records. Our project is less about prediction and more about fostering communication between clinicians. In the EHR, if nurses are concerned about a patient, they can’t just put a note in the front of the folder. That’s what our work is really about; doing data mining and analytics to pull that information out, looking at what the nurse has done and saying, “this nurse is more concerned about this patient,” based on what that nurse is doing and metaphorically pulling that patient note to the front of the chart so that other people can see it. I’m sure that communication was strained once the pandemic came around. That leads me to your COVID-19 project, which is called “Scaling up for Surge Capacity and COVID-19 Patient tracking in the Electronic Health Records: Leveraging Healthcare process modeling”. Can you break that down? Cato: The idea behind this project is that during a community-wide emergency like COVID-19 or 9/11, where you have lots of people using hospital resources at the same time, there are a couple things that happen. One is that the hospital has to change how it’s configured physically where it has to change what types of beds are being used. The EHR is configured to work during normal times. Let’s say we have a bed that usually has only a medical/surgical patient, but you have a lot of intensive care, very sick patients coming in. If you change that type of bed, the EHR still thinks that bed is a medical/surgical bed. So someone has to physically go into the EHR and say, “No, this bed is now being used for really sick patients.” You can imagine in a time like COVID, when a lot of really sick people are coming in, the city, state, and federal government are trying to track these things: they’re asking, “how many really sick patients do you have?” So they created manual processes, which are very resource intensive. What we were trying to do was help the hospital track patients in a more automated way to understand how many really sick patients were on ventilators. We used the same modeling methods that we used in our CONCERN project and applied that to tracking resources for COVID. The non-technical summary is that we can look at how a clinician is interacting with a patient and understand what type of patient it is. So when a clinician is interacting with a really sick patient, they write different types of notes, they take different types of measurements compared to when they’re interacting with a patient that isn’t as sick, that doesn’t need as high a level of care. We applied data science methods—it’s actually really similar to when you go to Netflix and it says, “this movie’s a comedy,” as opposed to “this movie’s an action film.” Those methods aren’t based on someone actually going in and tagging it as that, because then you’ve led yourself to a human error. What we wanted to achieve was two goals: one was to reduce that human error, but two, we wanted to automate it so we could have it in real time. The reason we wanted to do it in real time is because we also had a situation where it’s not just beds that turned into really sick patients’ beds; sometimes, a really sick patient would be in that bed and be treated as an ICU patient, and the next day, a not so sick patient would be in that bed. So you want, in real time, to understand what type of patient was being treated. That’s basically what that project was about, because the research shows that resource allocation is actually one of the number one things that is associated with how well patients do. If you run out of trained people, ventilators, other resources, then the patient outcomes won’t be as good. Our goal was to help in that process to make sure we had the highest level of quality care as we should. After you made the models, you evaluated their precision. How were your results? Rossetti: They were very good. This was a classification; we weren’t predicting, but we were looking at the retrospective data to look at what the bed was and what the patient was. It was extremely accurate. It was a good process to be able to see how we could increase automation and validation of these manual configurations that have to occur in the EHR. Normally, they can occur manually and we have the time and resources for them to occur. With COVID, the resources were much more constrained and things had to happen quickly, and this was a way of ensuring accuracy. Cato: From a data science perspective, our methods actually can perform really well because we’re not asking the program to learn things that are really complicated. We’re just determining whether this is a really sick patient type or a not so sick patient type, type of bed, type of person in the bed. We had really good results; our precision was about 99%, which means that for that type of situation, if you had 100 really sick patients, our method would know that almost 100 of those patients were really sick patients. The take home message from that is that human beings aren't right all the time. What are your next steps in terms of COVID-19 research? Cato: There are three things we’re doing now. One is the resource mapping that I was just talking about, the bed tracking. We’re going to take that and we’re going to look for funding to build our automated systems. The idea is that we want to be able to build applications that people can use throughout the country and world where this can happen automatically and a human being doesn’t have to go and count the resources every day. Once we can build that functionality, then we can also build dashboards or reporting on top of that so that if or when something like this happens again, we can get better reporting in real time. We’re going to look for funding to continue to do that and build those systems. Two: Our work overlaps a lot with documentation, notes that clinicians are writing. We know that documentation is a big issue, especially in terms of documentation burden: how many notes a clinician usually has to write when they’re working. The EHR is great for some things, but one of the issues that has come along with the EHR is that clinicians have to write more notes than they used to, and it’s taking up a lot of time. During COVID, some of the rules around documentation are relaxed. It’s a great opportunity to see what documentation might be necessary and what might not be necessary. We’re going to do research to look at how clinicians are documenting during COVID and see what ways that we can reduce the documentation burden. That’s a grant proposal that we just submitted last week, so hopefully we get funded to do analysis for that. Finally, we have lots of data that were produced during this epidemic, and there are lots of clinical questions that need to be answered. One of them, in the emergency department, is trying to understand which patients are going to have bad outcomes and which patients aren’t. There’s a lot of science around COVID that we don’t know yet, so we’re working on projects that help to predict; we’re applying the same methods that we applied with our other work of modeling how clinicians interact with patients, to produce prediction to say that this is a patient that, based on how everyone in the emergency department is interacting with them, might get much sicker in the next 24, 48, or 72 hours, and maybe you shouldn’t send them home. Rossetti: Another related issue that we were looking at is which of our patients are on ventilators. Ventilators were a huge issue during COVID. In the EHR, you can only identify the information that is recorded. The information about ventilators is recorded in some ways that makes it complicated to identify the precise time at which a patient started on a ventilator and stopped on a ventilator. We’re able to apply this healthcare process modeling technique in order to have more accurate timeframes for when patients are on ventilators. These are really important things that can then be used in different types of research. For instance, to say, “let’s look at patients who were ventilated and what their outcomes were” or “for patients that were ventilated for this period of time, how well do they do versus the patients who were ventilated for a shorter period of time?” You can start to use that information to answer some valuable research questions. This is applied clinical informatics research that can benefit data science work in many broad areas. For any students out there who want to pursue the career path you’re on, do you have any advice? Cato: I think the major thing is that there are a lot of different pathways to learn data science right now. Data science is very “in.” What I would say is that when you’re pursuing that, it doesn’t matter if you’re in the healthcare field, finance, environmental science, criminology, social work, English, or history; it’s important, as you’re learning, to try to get experience in those fields as well. For example, if you’re interested in data science in criminology, trying to get experience in criminology to understand the actual field, the domain. Right now, we still have a big issue with analysis. Dr. Rossetti and I are very lucky in that we have, over the years, developed domain expertise, both clinically and analytically. It’s very helpful if you start off trying to develop that experience and that knowledge in the area you’re interested in. It’ll help you understand how applicable the different types of analyses are and how you can use those different types of analyses, if they make sense for the questions and problems you’re dealing with. For anyone interested in learning more about what you’re doing, where can they look? Rossetti: We have a website: concernstudy.partners.org. Our study is a multisite study, but primarily at Columbia. People can also look at my profile, as well as Dr. Cato’s profile. Last question: To end with some optimism, what gives you hope for the future? This can be related to your research, science more broadly, or life in general. Rossetti: So many things have occurred in the last few months and weeks. I think we’re seeing a lot of change—needed change and good change that needs to happen in the world regarding Issues around racial disparities and systemic bias. These conversations need to happen and are happening, so I see that movement being escalated, and hopefully, will continue. Related to the specific work that I do, if you look at what happened during the COVID response, we have amazing clinicians that were and are our heroes. We hope that we can begin to demonstrate that great work through the research that my team and I do. We actually can show from the health care records the decisions clinicians are making for their patients, and the expertise that they’re applying is really amazing. We want to continue our work and continue perfecting these data science techniques, these healthcare process models, to really show the great work that clinicians are doing. Something that we’re also working on is the issue of documentation burden. We ask clinicians to document a lot in the record. But really, clinicians are the experts. They know what patients need. The work that we’re trying to do is to surface the great expertise and decisions that clinicians are making and know the patients need so that we can hopefully start to decrease the documentation requirements on clinicians, so that the information in the record is really essential information. That can really drive better patient outcomes so that clinicians aren’t as tied to manual data entry in the record but really are able to use the record to support decision making and be with patients more for direct care; to get the systems to work for them. That’s where I think we can go; I think there’s a lot of movement and recognition that we need to help our clinicians in that regard. I’m excited for that in the context of the specific applied clinical informatics work that we do. Cato: A couple of things. I’m a fairly geeky person. I’m going to be 50 years old next year, so I’ve been around a while. When I was younger, we didn’t quite see the analytical things as much in the popular world. You didn’t see people from the NIH on the news as much. I think that there is a lot of talk about distrust in science, but I think that science is being more infused in popular culture. The term “flattening the curve” is an epidemiological concept, and it’s being talked about; kids are talking about it. It gives me hope because I think society moves forward when you can combine science and other fields. You can bring science into the mainstream and bring it into leadership and politics and other domains. Science shouldn’t just stay in the field of academia. I’m very encouraged when I see epidemiology, data science, statistics, machine learning, talked about on the nightly news and the internet. I feel very optimistic about that. I also feel very optimistic about how, when you look at COVID, we’ve been able to get different scientific domains to ramp up very quickly and throw a lot of research and work at these problems relatively quickly, so hopefully we can utilize those workflows for other types of problems that come up, other questions that aren’t during an epidemic or a pandemic, but other intractable problems that we’ve had. Hopefully these teams and all these great scientists that have been working on this problem can be able to pivot and work on some of the other things that we thought we couldn’t fix or solve previously.
Interview by: Maria Trifas (CC '21)
The following is a heavily condensed version of the full interview. If you're interested, read more here. What has your team been working on during the COVID-19 pandemic? Our team is looking at the immediate impacts of COVID-19 using Poverty Tracker data. The Poverty Tracker is a quarterly survey of adults in New York City for tracking dynamics of poverty, hardship, and other forms of disadvantage.
policy or new quality of life concerns come up, we’re able to add questions pretty quickly to our surveys and take the pulse of what’s going on in New York City.
By mid-March, the pandemic was in full force and we had developed a new survey module asking specifically about its immediate economic impacts. With it, we were able to link that data to our earlier data sets to identify who was losing work and what their experiences were before the pandemic. What we found was that the people who were losing work were more likely to be in disadvantaged circumstances beforehand, so they were much more likely to be in poverty or material hardship. For example, of the people who lost their jobs, 37% of respondents were rent-burdened (paying over 30% of household income on rent) prior to the pandemic. Now we’re working on another survey asking about a more comprehensive set of hardships, like falling behind on rent and risks of eviction, as well as food insecurities and inability to make utility payments. We’re also asking about how effective social networks of support have been during this time. How is the Poverty Tracker survey distributed to New Yorkers? We have a rotating panel design: we recruited the current sample in 2015, and we follow up with people for four years. Every second year, we recruit more people into the panel via random-digit dialing. Random sampling is important to produce samples that are representative across all boroughs, ages, demographics, etc. We also evaluate peoples’ use of government benefits and support, as well as employer-provided support, such as paid leave, childcare help, unemployment insurances, stimulus checks, and all those sets of support that people especially need to deal with the crisis. We have questions on discrimination experiences, and will field themin a few months to capture the perceptions, experiences, and coping mechanisms, especially among the Asian-American population, during this pandemic What’s the demographic of New Yorkers surveyed? Since it’s a rotating panel, it changes over time. We also have a sister research project called the Early Childhood Poverty Tracker, which is around 1,500 parents of young children, so it was children aged 0-2 in 2017. In total we survey 3,000-3,500 people with an emphasis on families with young children. We have historically surveyed only in English and in Spanish, but we’re now surveying in Mandarin as well to reach a broader demographic. Any pandemic-related misconceptions you’d like to address? We’ve been working on this project for almost 10 years, but if you look at our long-term statistics, poverty, material hardship, and economic insecurity were rampant in the city before this, and there’s a misconception in the idea that this is now the focus of a lot of people. It didn’t come from nowhere; we were operating on a very fragile place to begin with. So a return to “before the pandemic” isn’t necessarily ideal for many New Yorkers.
So, there’s a lot of movement underneath those statistics that our study reveals, especially during. the COVID-19 crisis. We try to come to an improved measure of poverty, but we also try to understand how well families are doing using broader measures. The impact of our studies comes from their comprehensive measure of hardships that reveal more effective ways for other institutions to help alleviate them.We stay in close contact with state and local governments and many community social service agencies so that we can translate our findings into real policies or services. What’s your perspective on the future trends that New Yorkers may face? There’s been this idea that there’s an on-off switch for the city, like “everything’s re-opened and people are back to work!” so it’s just going to flip back to whatever this prior norm was. We know anecdotally that this isn’t the case: people aren’t getting full work schedules; they’re going to be working part-time. We’ll be very well-poised to track the downstream impacts that this has had that are more nuanced than the “stores have opened again” measure. We’ll be able to provide evidence of those changes and how people will be still struggling for a while. So that’s a misconception with the phrase “re-opening the economy” as well? It isn’t an open door. We cannot think of COVID-19 as the pre vs. post; it’s ongoing and it will be for a long time. It’s challenging to measure all of the impacts going on, including economic impacts, social impacts, and discrimination – plus, now we’re reckoning with the impacts of police brutality and systemic racism. With so many influencing factors, it’s likely that “post-pandemic” life will not return to the normalcy many people are imagining – after all, even our study struggles to capture the whole complexity of what’s going on right now. For more information on Robin Hood Foundation and Poverty Tracker, see here. The Robin Hood Foundation also released two recent blog posts on COVID-19, Poverty & Food Hardship and Material Hardship. Also, check out the Robin Hood Report on Paid Sick Leave and COVID Policy Response, Early Childhood Poverty Tracker Report, and Latest Annual Report.
By: Maria Trifas (CC '21)
What has your team been working on during the COVID-19 pandemic? Gao: Our team is looking at the immediate impacts of COVID-19 using the Poverty Tracker data. Wimer: The Poverty Tracker is a quarterly survey of adults in New York City for tracking dynamics of poverty, hardship, and other forms of wealth and disadvantage. What kind of metrics does the PovertyTracker survey evaluate? Wimer: The Poverty Tracker collects annual data on three core measures of disadvantage: an income poverty measure (based on the supplemental poverty measure, an improved income poverty measure that incorporates the value of in-time benefits and after-tax resources), material hardship (people’s inability to meet their material expenses), and health problems/challenges. We collect those data annually and have quarterly surveys that look at topics important to New Yorkers. We have modules on assets and debt, a deeper dive into health, consumption, employment, etc., so we collect a wide variety of information about how New Yorkers are doing over time. It’s a nimble tool such that when new policy or new quality of life concerns come up, we’re able to add questions pretty quickly to our surveys and take the pulse of what’s going on in New York City. Collyer: We’re constantly in the field surveying New Yorkers, and in early March, we thought that we should probably add some questions about this pandemic that people are talking about and started drafting some questions. By the end of the week, the whole world had changed and we had a much more developed survey module asking specifically about the most immediate economic impacts. We were asking about the loss of work and income, and for our respondents who are small business owners, asking about needing to shutter their businesses and loss of freelance/self-employment income. With that, we were able to link that data to our earlier data sets to identify who was losing work and what their experiences were before the pandemic. What we saw was what you see in most news stories coming out at the same time: the people who were losing work were more likely to be in disadvantaged circumstances beforehand, so they were much more likely to be in poverty before and they were much more likely to be in material hardship. For example, of the people who lost their jobs, 37% of respondents were rent-burdened (paying over 30% of household income on rent) prior to the pandemic For our Black and Hispanic respondents, those rates were even more elevated than pre-pandemic, specifically for the people who lost work. We also found that there was a 30% increase in the experiences of food hardship, just within 2 months of the pandemic among those who had lost work. We picked up on those immediate impacts, and now we’re working on another survey that we’ll field in the mid- and late summer asking about a more comprehensive set of hardships, like falling behind on rent and risks of eviction, as well as experiences of food insecurity and inability to make utility payments. I think we know that we’re in this pause position right now, with the moratoriums on evictions and utility shutoffs; there isn’t really much of a plan for what’s gonna happen afterwards, but we’ll have a sense of how many people are at-risk based on the data. We’re also asking about the social networks of support that people have turned to at this time, and how effective those networks have been. Is this how you holistically keep track of New Yorker’s well-being? Collyer: Exactly. What you often see in these circumstances is sometimes people who might appear to have economic security under some measures are turned to in these periods so they end up supporting a lot of other people, and this also means that we might not be capturing all of the hardships that people are facing. How is the Poverty Tracker survey distributed to New Yorkers to get a representative picture of the population’s well-being? Wimer: We have a rotating panel design, so we recruited the current sample in 2015, and we follow-up with people for four years. Every second year, we recruit more people into the panel. So, we contract with a survey research firm that does random-digit dialing. It’s exactly like it sounds: you dial random phone numbers in the city until you recruit enough panel members to join the panel. Then, the sample gets turned over to us and we have a whole team here, including masters students, undergraduate students, and full-time staff to conduct the surveys every three months. But, it is important to us that we have a representative sample across all boroughs, ages, demographics, etc., and the randomness of the sample ensures that. So, it is challenging to keep people in the survey for four years; our staff does a tremendous job of doing that. This entails a lot of phone work and email work in order to keep people engaged in the survey. Now, we hear stories about people who are home because of the crisis that might have been harder to reach before the crisis, but are now easier to reach. The opposite is also true; there are people who are now dealing with family members who might be sick or children who are home from school, so it’s a mix. We do the best we can and try to be sensitive to people’s circumstances. Gao: In terms of survey content, we also evaluate peoples’ use of government benefits and support, as well as employer-provided support, such as paid leave, childcare help, unemployment insurances, stimulus checks, and all those sets of support that people especially need to deal with the crisis. We previously had a module on discrimination experiences; we have now renewed it and will field it in a few months to capture the nuanced experiences, especially among the Asian-American population during this pandemic: perceptions, experiences, and coping mechanisms. We do academic research, but we also share our findings with the general public, sometimes in partnership with our main funding agency, Robin Hood. We produce a series of brief reports, which highlight the results from our study, like how people have been initially affected by COVID-19. Do you also share your work with New York City government officials to improve economic security efforts in the city? Collyer: We have a close partnership with New York City Opportunity, an office in the mayor’s office that produces a similar poverty estimate to ours. Robin Hood also has close ties to a lot of city agencies and leaders, so they do a lot of work to bring our data to that platform and make sure that what we’re finding is communicated. How many New Yorkers do you survey to get this data, and what are the criteria? Wimer: It’s a rotating panel, so it changes over time, but our steady-state (the number of people who are routinely involved in the surveys) is about 2,000-2,500 people. We also have a sister research project called the Early Childhood Poverty Tracker, which is around 1,500 parents of young children, so it was children aged 0-2 in 2017. So, in total we survey 3,000-3,500 people with a broad over-sample of families with young children. So, it’s a moving target because we refreshed the panel in 2017 and are refreshing it right now with another 2,400 people. The key criteria to be in the sample is to be an adult over the age of 18 in New York City. We have historically surveyed only in Engish and in Spanish, but we’re now surveying in Mandarin as well. About a third of the sample has children, and we’re reaching all demographics across New York City (with the language criteria being a limiting factor). Gao: One thing we emphasize in our research is to look at the racial and socioeconomic status disparities. We are more acutely aware of these disparities especially during this pandemic, and armed with our rich data we’re able to do that. Throughout your work, have you found any misconceptions about your work or the state of New Yorkers during the pandemic? Collyer: So, we’ve been working on this project for almost 10 years, but if you look at our long-term statistics, poverty, material disadvantage, and economic security were rampant in the city before this, and there’s a misconception in the idea that this is now the focus of a lot of people. It’s great that that focus is there, but it didn’t come from nowhere; we were operating on a very fragile place to begin with, which is important to remember. Which really emphasizes that a proposed return to “before the pandemic” isn’t necessarily an ideal time for many New Yorkers. Collyer: Exactly. Wimer: When you look at poverty or any disadvantaged statistic year-by-year, you might see that there’s a slight decline or a slight uptick, or in the current context a more severe uptick/downtick, but what that misses is that it’s not the same people year-to-year that are experiencing poverty or disadvantage. So, when you look over time (one of the strengths of our study), there’s actually a much bigger percentage of New Yorkers who are experiencing hardship or a spell of poverty. It’s not just 20-21% from year-to-year; what that means is that there’s 30-50% experiencing an episode of poverty or disadvantage over time. So, there’s a lot of movement underneath those statistics that our study reveals. The COVID-19 crisis right now highlights that to a degree we didn’t understand much before. So, there’s a lot of people who are experiencing a spell of financial insecurity right now, and our survey tries to capture that. We try to come to an improved measure of poverty, but we also try to understand how well families are doing using broader measures, and we look at how that’s changing over time. Gao: I think that our great strength is our rich measure of hardships and the disadvantages experienced by people: crowding in homes, disrupted work, childcare challenges. We also try to capture the dynamics of people experiencing poverty and hardship because people who “enter and exit” poverty all experience hardships at different times of their lives. So, we try to be nuanced in our measures. We also want to highlight how impactful our research has been. We do the research not purely as academics; we stay in close contact with state and local governments and many community social service agencies so that we can translate our findings into policies or services, especially so the general public can be more aware of what’s happening in a dynamic way. That’s great because your research has a high potential impact to social programs for New Yorkers, such that publishing in academic journals isn’t the end goal of your work. Gao: That’s why we have active mailing lists, and we post these briefs and reports on our website. From the work that you’ve been doing, what is your perspective on the future of New Yorkers, with the re-openings and tracking impacts on New Yorkers. Do you see any trends in your data right now? Collyer: There’s been this idea that there’s this on-off switch for the city, like “everything’s re-opened and people are back to work!” so it’s just going to flip back to whatever this prior normal was. What we know anecdotally that this isn’t the case: people aren’t getting full schedules; they’re going to be working part-time; there’s going to be so much continued disruption that we need to keep an eye on. We’ll be very well-poised to do that and to track the downstream impacts that this has had that are more nuanced than the “stores have opened again” measure. We’ll be able to provide evidence of those changes and how people are still struggling for a while. So that’s a misconception with the phrase “re-opening the economy” as well? It isn’t an open door. Gao: We cannot think of COVID-19 as the pre vs. post; it’s ongoing and it will be for a long time. For me, as an academic and as a mother, I think about how we’ll return to our work and how schools will re-open in the fall, how to keep my kids safe and balance my job with being a caregiver. One thing that I keep thinking about doing this research is imagining myself as one of the participants ( I’m not and am not allowed to be). Because it’s a study of New Yorkers, I can put myself into the study to answer all those questions and think about how I’m doing as a New Yorker and how I’ll use that experience to enrich this study. Wimer: We need to keep these surveys short in order not to burden people, and it’s challenging to measure all of the impacts going on, including economic impacts, social impacts, and discrimination. Now, we’re reckoning with the protests and impacts of police brutality and systemic racism that are going on. Our surveys are typically 10-15 minutes, so we want to track and take the pulse of New York on a regular basis, but we struggle with being able to capture the whole complexity of what’s going on right now. I bet you constantly want to keep adding new questions. Wimer: We do. We want every survey to be an hour long.
For more information on Robin Hood and Poverty Tracker, see here. Robin Hood also released two recent blog posts on COVID-19, Poverty & Food Hardship and Material Hardship. Also, check out the Robin Hood Report on Paid Sick Leave and COVID Policy Response, Early Childhood Poverty Tracker Report, and Latest Annual Report.
By: Julienne Jeong (CC '21) Acknowledgements: Paul Spezza (CC '21) and GlobeMed at Columbia University
Her story:“My perspective on global health and social justice takes me back to my experiences in northern Uganda. During a very tense and violent period of war by rebel groups, I had been working with the World Food Program of the United Nations, where I was in charge of general food distributions to camps of Internally Displaced Persons (IDPs). People were fighting and shooting among civilian streets, but I continued my work. I also often carried injured or dead bodies to our nearest hospital. Every time I distributed food, I cried. I saw suffering women with their children waiting in the queue. One woman waited for over 6hrs with her child on her back, witnessing many others raiding the food. After I served her that day, she realized that her baby had died. This was a turning point in my life: whatever food I could give her in that moment could not bring her child back to life. I counseled her and talked to her family and began to realize that the problems she told me about were shared amongst most women in Northern Uganda. Women were bearing more of the burden and the suffering in our community. From talking with women one by one, many women found solidarity in me; they came to me with their problems. I quickly set up a regular venue where women could sit with me for counseling. There was one Mango Tree near my place— that was where we could meet. Many women shared their stories of rape, abuse, torture, and lack of health support. I left my personal work and started figuring out ways we can support women, girls, and communities. I called for friends to help, too. Together, we started Gulu Women’s Economic Development & Globalization (GWED-G). And today, we serve over 150,000 individuals in programs for health, human rights, access to justice, peace- building, psychosocial support, advocacy, and economic empowerment. Due to the COVID-19 pandemic, we are now on lockdown, but that does not mean that our work stops. We are now playing an active role on our District’s COVID-19 Task Force and have been distributing food to over 300 food-insecure families. Our Village Health Teams are travelling miles by bike, door-to-door, to continue providing antiretroviral treatment for those with HIV/AIDS and we are actively responding to cases of Gender-Based Violence (GBV) in domestic households, which have been on the rise because of the lockdown.
After this pandemic, I hope to reunite with my communities from where it all began— under the Mango Tree.” To support GWED-G during the COVID-19 Pandemic, please share her story and consider donating via eventbrite or venmo @covid19uganda. Follow her on Facebook at GWED-G and on Instagram @gwed.g
By: Hannah Lin (CC '23)
Could you describe your COVID-19 research at the moment? We have multiple projects going on in the lab. The biggest effort revolves around isolating monoclonal antibodies from patients who have been infected by SARS-CoV-2, or COVID. As you know, once infected, people mount an antibody response directed to the virus, and we are trying to find special patients whose blood contains very powerful antibodies that could kill SARS-CoV-2. What we’ve done is screen about 40-some patients; we took their blood, looked at the virus neutralizing activity of the blood, and focused on those with the most potent responses. We’ve been working very hard for the last couple months on 5 cases in particular, and we have been able to pull out lots of very potent antibodies that could kill SARS-CoV-2. We’ve been characterizing that, and I think we’ve been pretty excited about our results because we have found some antibodies that have never been described and are also very powerful ones. We’re characterizing them and also trying to figure out the structure of how the antibodies bind to the important part of the virus. That’s our first project. We also have a project that’s focused on developing drugs for SARS-CoV-2 by targeting an enzyme of the virus called protease. Protease is a chemical scissor that is required to cut the viral proteins from big chunks to small pieces, and if you find a drug that can gum up that chemical scissor, the virus can no longer replicate and therefore would be blocked. We’re busy looking at that, and we have a few chemical compounds that could do so, but not at very high activity at this point. We need to continue to work with chemists to synthesize what we call analogs, or related compounds, to see if any one of them will exhibit greater activity against the virus. This is an iterated process that we have to go through, and I suspect it’s going to take a while to get a compound that’s active in the laboratory to a drug that could potentially work in people. Those are the two major projects in the lab. We also have another project that is trying to develop rapid tests, so we can quickly measure whether someone has the infection or not by taking, say, a saliva sample and putting it to test, and in about 10-15 minutes, you can tell whether someone has the virus in saliva or not. That effort is also ongoing. Lastly, we have a project focused on how the virus or its viral proteins could be triggering a bunch of immunological cascades—how that triggers the immune system to do damage to various organs—going from the lung to the heart to the kidneys to the liver, and even to the nervous system. It’s what we call pathogenesis studies, and we have a project focused on that as well. Those are the principal activities related to this pandemic that my laboratory is undertaking. Have you faced any setbacks in your research thus far? In this sort of research, each day we have our setbacks. Certain experiments don’t work and you waste a day. In general, I would say we have not had major setbacks so far. Everybody’s working 24/7. It’s all hands on deck, and the progress we have made in the last 2-3 months would rival progress that a laboratory would normally make in 2-3 years. It’s rather remarkable the speed with which research is being conducted. Not just in my lab, but in many labs throughout the world because of this pandemic. Is your team still keeping prior projects going, and how are you managing both? No, we’ve been asked to only address the pandemic. Existing or prior projects are all put on hold, so only those working on COVID have been working. Our HIV research has essentially halted, although starting next week, some of it might kickstart once again. You’ve been in this field researching for decades now; has there been anything particularly in your COVID-19 research that has surprised you at all? I’m pleasantly surprised by the way the scientific community has mobilized to an extent that’s unprecedented. The collaborative spirit is also a very, very pleasant surprise. People send us reagents and we send others reagents without even thinking about it, just doing it, helping each other out as quickly as possible by exchanging information. We’ve been impressed by the amount of scientific funding also—from the government, from foundations and philanthropists. There’s quite a bit of funding pouring in. All of those are very positive. That reminds me of an article I saw where you talked about how the funding for SARS research trickled off, and that hindered progress for COVID-19 research now. Also from that article, developing solutions that may be more permanent has been a focus of your group in regard to treating potential viruses. Can you talk a bit about why that is one of your important goals and why it is so significant to look ahead? We obviously learned a lesson: that if we had continued the research on SARS, we would be way ahead today. A lot of research on SARS came to a stop, and that’s part of the reason why we’re struggling to deal with this pandemic. Just thinking about coronaviruses, we know these viruses have relatives in various animal species, particularly in bat populations. This is the third epidemic, or pandemic, in the last 20 years due to a coronavirus. Surely, others will emerge, so we need to broadly prepare for those we know about that exist in animal species. We are obviously trying to fix the current pandemic, but in the back of our minds, we are seeking solutions that could be broadened to cover other related viruses. Are there any common misconceptions about COVID-19 that you want to set straight for the public? I think there’s a lot of misinformation out there being spread by various people, from some average Joe on the street all the way to the White House. There are many misconceptions in regular media and also in conversations between people. I think this pandemic is a real threat; one should not trivialize it. Most people don’t, but there’s a segment of society that has been believing that this isn’t such a big deal, and that’s because they haven’t seen the carnage or devastation that we see in the medical center here. While pausing at home is difficult, it is the proper thing to do to bring this outbreak under control. Fortunately, in New York, I think we’ve picked up well over the last couple months. But throughout the country, there are many regions that are not doing all that well, and yet they’re still opening up. As a specialist in this area, it frightens me a great deal. I fear for what may come in the coming months. For some optimism: a lot of people look to you for inspiration because of all the important work you’ve done; have you yourself been inspired by anyone or anything during this pandemic, not necessarily in scientific research, but in general? Of course, we’re all inspired by the frontline healthcare workers, who put their own bodies at risk to help take care of the sick and dying, and in great numbers. It’s pretty admirable. I used to be a physician who sees patients, but I haven’t been doing that for some time, so that’s inspiring, and it’s prompted me and my colleagues in the laboratory to try to match their effort. We realize they’re taking care of the patients now, but we hope to develop solutions that will help take care of the patients in the future. What is your perspective on the future, both in terms of your research and the impacts of COVID-19 on science and society at large? This pandemic is here to stay for a while, and our lives are going to be changed for a long time. We need to take it seriously, and everyone should do everything possible to mitigate transmission of this virus to buy scientists some time to come up with solutions. I think there’s a misconception that solutions are very quick and that in the course of a few months, we will have a vaccine or a drug. I’m quite confident in the success that we will ultimately reach; however, it will take time. It will certainly take more than a year, perhaps up to two years, before anything really, really useful will come out. On the science side, I think we’re getting a real boost, particularly in virus research. It’s a reminder of how important this work is. We spend billions and trillions on defense against a foreign power, and yet we spend relatively little against a foe like this kind of virus; look at the devastation that has caused. It’s teaching us a lot about our priorities. This is nothing new; scientists have been saying a pandemic will come; it’s only a matter of when, not if. Yet the leadership of our country and many other countries simply don’t believe that. Hopefully, this is a wake up call for everyone.
Interview by: Hannah Lin (CC '23)
The following is a heavily condensed version of the full interview. If you're interested, read more here.
Could you describe your COVID-19 research? We have multiple projects going on in the lab:
2. Developing drugs for SARS-CoV-2 by targeting an enzyme of the virus called protease. Protease is a chemical scissor that is required to cut the viral proteins from big chunks to small pieces, and if you find a drug that can gum up that chemical scissor, the virus can no longer replicate and therefore would be blocked. We have a few chemical compounds that could do so, but not at very high activity at this point. We need to continue to work with chemists to synthesize what we call analogs, or related compounds, to see if any one of them will exhibit greater activity against the protease. 3. Developing rapid tests so we can quickly measure whether someone has the infection or not. 4. Pathogenesis studies: how the virus or its viral proteins could be triggering a bunch of immunological cascades--how that triggers the immune system to do damage to various organs—going from the lung to the heart to the kidneys to the liver, and even to the nervous system. Have you faced any setbacks in your research thus far? In general, I would say we have not had major setbacks so far. Everybody’s working 24/7. It’s all hands on deck, and the progress we have made in the last 2-3 months would rival progress that a laboratory would normally make in 2-3 years. It’s rather remarkable the speed with which research is being conducted. Not just in my lab, but in many labs throughout the world because of this pandemic.
Are there any misconceptions about COVID-19 you’d like to set straight? I think this pandemic is a real threat; one should not trivialize it. Most people don’t, but there’s a segment of society that has been believing that this isn’t such a big deal, and that’s because they haven’t seen the carnage or devastation that we see in the medical center here. While pausing at home is difficult, it is the proper thing to do to bring this outbreak under control. Fortunately, in New York, I think we’ve picked up well over the last couple months. But throughout the country, there are many regions that are not doing all that well, and yet they’re still opening up. As a specialist in this area, it frightens me a great deal.
particularly in virus research. It’s a reminder of how important this work is. We spend billions on defense against a foreign power, and yet we spend relatively little against a foe like this kind of virus; look at the devastation that has caused. This is nothing new; scientists have been saying a pandemic will come; it’s only a matter of when, not if. Yet the leadership of our country and many other countries simply don’t believe that. Hopefully, this is a wake-up call for everyone.
Full Interview: Tobacco Control, Social Support, and Coping During the Pandemic With Daniel Giovenco6/2/2020
By: Makena Binker Cosen (CC ‘21)
Tell me a little bit about your background as a researcher. I have been fascinated by public health since I started college. I studied Health Communication at the College of New Jersey, and right after that, I immediately went to graduate school to get my Masters of Public Health Degree at Rutgers University. It was during my master’s program when I really caught the “research bug” and knew that I wanted to be a scientist. I still have a passion for community-based health education, which was the focus of my degree, but during my master’s program, I realized that I also had so many research questions that I wanted to answer. For example, we’ve seen evidence of health disparities for decades, but why do they exist, and what are ways that we can more effectively eliminate them? I knew I needed research training to be able to answer some of those higher-level questions. So, I started a Ph.D. program at Rutgers University and got my Ph.D. in Public Health with a focus on behavioral science. Then, I started working at the Mailman School at Columbia as an assistant professor. How did you get involved in tobacco control research? My mentor at Rutgers University was an expert in survey and policy research, and focused most of her work on tobacco control. It was never really my intent to study tobacco use, but as I started learning more about the field, I felt like there was no better health issue to focus on as a public health researcher. Tobacco use is still the leading cause of preventable death and disease in the United States, which is hard to believe after decades of knowing how harmful this behavior is. It’s become so much more complex with the emergence of e-cigarettes and other novel products. As someone who is really passionate about addressing health disparities, I think tobacco use is what I decided I needed to focus on. If you look at the people in the United States who are still smoking at high rates, it’s a lot of populations that have been historically marginalized or disenfranchised — people living in poverty, certain racial and ethnic minority groups, people with mental health disorders, and the list goes on. We have the potential to minimize smoking disparities with tools that we know work. We need to do a better job designing policies, regulating products, and providing quitting support; research can help on that front. I imagine those disparities have become exacerbated in the pandemic. What do we know about the relationship between tobacco use and a person’s risk of getting COVID-19, as well as the health outcomes related to each of those? What we know for certain is that the health consequences that smoking causes, like COPD, cancer, other types of lung disease, and heart disease, are documented risk factors for poor COVID-19 outcomes. The data on whether active smoking is a strong risk factor is inconclusive. I don’t think we have a full picture of this relationship yet, partly because data collection is not always high quality. So, when we document COVID cases, we don’t know how people are asking about smoking status or if patient responses are fully accurate. One can imagine that a behavior that involves inhaling toxic substances probably isn’t protective when it comes to a respiratory illness like COVID-19. Research is still emerging and results have been kind of mixed, actually. There are a lot of studies that show smoking is related to poorer disease outcomes and increased mortality, as we might expect. Interestingly, there are a few studies suggesting that smoking almost seems protective: the rate of smoking among COVID patients is lower than expected. Could nicotine be playing this protective role because of its impact on the body’s inflammatory response? At this point, we don’t know if these findings are just due to poor data or if there truly is enough reason to question the role of smoking. As soon as the pandemic arrived in the US, I thought to myself, “Well, smoking clearly must be a risk factor, so I wonder what smoking behaviors look like here during this time of high anxiety related to health.” As I continued to think about it, I thought that the precise relationship between smoking and COVID might be less important; the fact is that all of these measures that we put in place to prevent COVID spread — stay at home orders, social distancing, store closures — all of those things are likely going to affect people’s substance use behaviors, including smoking. I got to a point where I almost didn’t care what the relationship was between the two. I just wondered, “What do the behaviors of people who smoke and vape look like during this period of really high stress and anxiety, and how might these persist after we start to control the outbreak?” What’s happened during lockdown periods is an unprecedented shift in how we live and operate. I wanted to better understand that, so we can identify ways to better support the health and well-being of people who use tobacco or vaping products, and who may be addicted to those products. Could you please describe your current COVID-related research? My background is in quantitative research, so almost all of my projects involve survey data analysis or using GIS, geographical information systems. I don’t typically do qualitative research studies like interviews or focus groups, but I knew that, for this topic, that was the most appropriate methodology to use. This situation is unprecedented, and the impact that it has on tobacco use behaviors is probably very complex. So we knew that we needed to talk to people through very in-depth interviews to fully understand how people’s demand for their products and their supply sources may have shifted. We decided to do what is called “semi-structured interviews” with people who smoke or vape around the country. The “semi” in “semi-structured” means we have a set of questions that we want to ask, but are flexible as to where the conversation goes. We’re open to uncovering new avenues if they emerge. We used Facebook and Instagram advertising to recruit participants. We created advertisements that said, “Help Columbia University researchers understand health behaviors during COVID-19.” That advertisement was displayed to thousands of people across the country. If they clicked on it, it took them to a screener survey that assessed their eligibility for participation. To be eligible, they had to be over 18 years old and currently smoke and/or use a vaping product. We got a really diverse set of participants in terms of income level, geography, smoking behaviors, underlying conditions. We ultimately scheduled interviews with fifty people. They each lasted about an hour - we talked to them about the various ways that the pandemic has impacted their lives in general and altered their substance use behaviors. It has been really fascinating. We finished conducting all the interviews, and are in the process of transcribing the audio and analyzing the data to look for themes that are consistent, or findings we didn’t expect. What can we expect are ways the pandemic is influencing tobacco use behavior? Is it affecting different groups in different ways? We thought that, because this is a high stress period, substance use behavior would probably increase among certain people. But for others, acknowledging that smoking can potentially put them at risk for COVID, maybe people would be a little more motivated to quit during this time. Others might not change their behaviors at all. So maybe we’ll find an even split. What we found is that tobacco use was rarely stable. Almost every person in our sample reported changes in their tobacco use during the lockdown period. Increased use was generally more common than decreases in smoking and vaping. Among those whose use increased, they would frequently say things like, “I’ve never been smoking or vaping more. It’s really ramped up. It’s how I’m coping.” They would even say, “I know that this can put me at risk. I know this can be really bad when this is all over, because I might be even more addicted than I am now. But right now, I need to do this to feel a sense of comfort and for stress relief.” When we asked about people’s intentions to quit during this time, there was a pretty resounding “No, now is not the time.” We heard that over and over. When we asked people, “Well, if you did decide to quit, hypothetically, what do you think you would need to be successful? What could better support you in that quit attempt?” People commonly mentioned social support, which is very limited right now. It’s like all the worst parts of this pandemic — things like feeling isolated from people, not having access to certain services or treatments — those are all the things that people need to successfully quit using any type of substance and those are all lacking right now. That’s why, generally people were like, “I know there’s probably no better time to quit, it’s just probably not going to happen now.” Has the pandemic’s stay-at-home measures affected supply of tobacco and vape products? We actually noticed a really interesting divergence between people who smoke and people who vape. I think one of the most illustrative quotes from this whole project was something like “No toilet paper, but plenty of Camel cigarettes.” A lot of people have said they’re having trouble finding basic necessities, but cigarettes are still everywhere. It’s just as easy to get them as it was before. If you think about essential businesses that have stayed open — places like gas stations, drug stores, grocery stores — most of those places sell cigarettes. So, access for smokers hasn’t been an issue. Some noted that they stockpile, so that they don’t have to go out to the stores as often. Some people that we talked to, if they smoke a pack a day, rather than go to the store every day, they’ll buy multiple packs at the beginning of the week. But it’s been interesting that a lot of retailers that sell vape products, like vape shops for example, were considered non-essential in many places and shut down. So, a lot of people who vape have said that they have to order their products online now. They have to wait weeks sometimes for them to get to their house. If someone is heavily addicted to something, not knowing when the next supply is going to come can be a real source of stress. So, generally, vapers have found it much harder to find their product, and some even reported that they have gone back to smoking cigarettes because they’re just easier to get, which is really problematic. We know that cigarettes are generally more harmful than vaping is, although vaping certainly isn’t without risk. But we definitely don’t want people who have switched over to vaping to go back to smoking cigarettes, which is what some of our interviewees reported. Considering younger people are more likely to use Facebook and Instagram, how did you generate a representative sample of participants? The benefit of using Facebook and Instagram as recruitment methods for this kind of study is, based on the results of our screener survey, we could reach out to individuals to make sure that we had representation from certain priority groups. Although there weren’t a ton people over 70 years old that completed the screener, there were some, and we could reach out to them individually and invite them to an interview, to make sure that their voices were heard. So if I was going to do a quantitative study to make representative estimates of health behaviors, I might not use Facebook and Instagram as my recruitment methodology. For a project like this, however, where we were really most interested in having people share their experiences and describe the complexities of their beliefs and behaviors, I think the strategy was optimal. Have you heard about or seen any changes in advertising since the pandemic started? I don’t know the answer to that question. We didn’t hear anything and we didn’t ask about that in this particular study, so I don’t know. We did ask about prices of products changing. Generally, people said “not really” or that changes weren’t noticeable. I know you’re still in the process of transcribing and analyzing data. Is there anything that gave you a push forward or back? Did you experience any setbacks? We talked about some, but were there any other limitations to the study? There weren’t any major setbacks. I know this may sound strange, but this was an enjoyable study to be a part of. It felt nice to directly connect with people, especially during a pandemic. The people that we talked to said they really liked having a conversation with someone about the way that COVID-19 has impacted their lives. They appreciated that people were looking into ways to better support the experience of people who do have an addiction to something. That’s one of the great things about qualitative research — you kind of form these relationships with the participants. You’re not just pulling data and spitting it out. You’re talking to people. It was interesting for me, since I’m usually someone who designs a survey, sends it out, and gets the results in. I felt more human than ever, as a researcher, just talking to people. In qualitative research, it’s okay to say things like, “That sounds really tough. I can’t imagine what that’s like. Can you tell me more about this?” You just feel like a human being connecting with another human being, which is something that you don’t always experience in research studies. I definitely understand that; for me, conducting interviews has been an opportunity to learn more about what is going on and connecting to how people are keeping up with the world today. It’s also just nice to have these conversations, especially to engage with topics that aren’t at the forefront of the headlines of the papers. Are there any online support groups for people trying to control their tobacco use behavior? I’m sure there are, but generally, if I could pick a central theme from this project so far, it’s that people’s tobacco use behaviors are a lesser priority than some of the other critical issues they’re facing. People are trying to survive. These interviews were sometimes emotionally draining. We talked to people who were in really, really dire circumstances — who have lost their jobs, who are financially struggling, who have family members who are sick or who have died. Just the amount of stress, exhaustion, and sadness that people are feeling — quitting smoking is the least of their priorities right now, from what we’ve gathered. Actually, it’s often what’s helping them to cope with their stressors. That’s not to say that they don’t view it as problematic. Many of them did. Many of them said, “I would love to quit. I know I should. I just can’t even think about that now. Smoking for me just allows me to function, to deal with all of the other stuff that is my priority right now.” That was tough to hear, because as a public health professional and as someone who is interested in helping people access quitting resources, I realized that people generally aren’t receptive to that right now, and for very understandable reasons. It made me wonder, “What are the next steps for us, as public health professionals, when people have so many competing priorities other than the one that you’re trying to intervene on?” That’s a big take away. I don’t know the answer to that either, but if you find out, let me know! What is your perspective on the future for your research and your work as a public health professional? What about the broader impacts of the pandemic in general? Yeah, that’s a great question and something that I’ve been thinking about constantly. When you’re working in a particular field, you’re often singularly obsessed with it. You care so deeply about it, that's what you focus all of your efforts on. With tobacco control or any kind of substance use research, we tend to focus on clear, actionable solutions to the problems, such as increasing access to treatment, preventing initiation, and regulating industry manufacturing and marketing. One way this pandemic has affected me very deeply is that it has revealed so clearly that health behaviors and outcomes have very systemic roots and causes. With everything that’s been happening related to COVID, racial injustice, and other social issues, you realize that the reasons why some people use substances can be to cope with and to escape major life stressors driven by these structural inequalities. I’m thinking much more deliberately now about the structural drivers of substance use rather than strategies to solve the problem at a surface level. I think both approaches are important, but we can’t wait around for structural problems to get solved, since they likely will achieve the biggest impact in reducing substance use. In some ways, tobacco use today is just an outward symptom of much larger and deeper underlying problems. Do you have any recommendations for people using substances to cope or people with loved ones who are? I have a much better understanding of why people continue to use any of these products and I empathize with that. That said, my “public health answer” is that this is probably the best time to quit considering the risks of COVID, although I acknowledge that it’s probably the most difficult time to quit. But even though the world seems like it has stopped, there are still quitting resources available. State quit lines are still open, so you can call and get access to behavioral support and even free nicotine replacement therapies. There are support groups and a lot of great tools and resources out there. There’s help available. Do you expect initiation to decrease during the pandemic? Yes, and it has been slowing, even before the pandemic started. But there will still be remaining smokers who continue to use, so we can’t let up. I think the reason why smoking has lost some attention among the general public is because for some, it’s an “invisible” problem. In a lot of people’s circles, they don’t know anyone who smokes, but smoking has certainly not gone away. The national smoking rate is something like 14% now, but if you look at rates among populations that face social and economic disadvantages, rates are similar to what they were decades ago. For example, among people with mental health disorders, the rate of smoking is around 30-40%. So yes, even though I think less people will start to smoke over time, I think there are people who we need to support in their quit attempts more strongly than ever.
By: Makena Binker Cosen (CC ‘21)
The following is a heavily condensed version of the full interview. If you're interested, read more here. What do we know about the relationship between tobacco use and a person’s risk of getting COVID-19 and developing related health outcomes?
Poor data collection may be responsible for mixed results: we don’t know how COVID patients are being asked about their smoking status or if patient responses are fully accurate. Could nicotine truly be playing a protective role because of its interactions with the inflammatory processor? That’s something basic scientists could investigate.
What inspired your COVID-related research?
Could you please describe your current research project?
What was the recruitment process for the interviews like?
We got a really diverse set of participants in terms of income level, geography, smoking behaviors, and underlying conditions. We used the screener survey results to make sure we had representation from certain groups. For example, there weren’t too many people over 70 years old completing the survey, but there were some, so we could reach out to underrepresented individuals to make sure that their voices were heard, too.
Based on your interviews, how has the pandemic influenced tobacco use behavior? Almost every person in our sample reported that their smoking or vaping behaviors changed in some way during the lockdown period. While some respondents reduced their use, increases in smoking or vaping was much more common. They shared that they felt like they needed to do this to relieve stress, boredom, and feelings of uncertainty. Participants acknowledged that smoking or vaping could put them at increased risk for COVID-19 or intensify their addiction beyond the pandemic. However, when we asked about their intentions to quit, we received a resounding “no, now is not the time.” People right now aren’t thinking about their smoking behaviors. We spoke to people who are in really, really dire circumstances — people who lost their jobs and are financially struggling; whose family members are sick or died. With the amount of stress, exhaustion, and sadness they are processing, it’s just not a priority. Many of them said, “I would love to quit. I know I should. I just can’t even think about that now. Smoking allows me to deal with all of the other stuff that is my priority right now.” As a public health professional, all of this has made me wonder what the next steps are when people have so many competing priorities other than the one we’re trying to intervene on. On a more personal level, what has this research experience been like for you? It felt nice to directly connect with people, especially during a pandemic. The people we talked to said they also enjoyed sharing their experiences about the ways COVID-19 has impacted their lives. They appreciated that people were looking into ways to better support the health and well-being of people who have various addictions. With qualitative research, you’re not just pulling data and spitting it out; you form relationships with people. Since I usually conduct quantitative studies, as a researcher, this experience has made me feel more human than any other project. Has the pandemic affected people who smoke and those who vape differently? Yes, we noticed a significant divergence in access to cigarettes and vaping products.
Sometimes, they have to wait weeks to get them delivered. If you’re addicted to something, not knowing when the next supply is going to come can be a real source of stress.
Concerningly, some vapers reported that they have gone back to smoking cigarettes because they’re just easier to get. We know that cigarettes are generally more harmful than vaping, although vaping certainly isn’t without risk. Do you have any recommendations for people using substances to cope? I now have a much better understanding of why people continue to use any of these products and I empathize with them. That said, my “public health” answer is that this is probably the best time to quit considering the risks of COVID, although I acknowledge that it’s probably the most difficult time to quit.
Where should public health professionals working towards tobacco cessation and prevention be focusing their efforts moving forward? Initiation has been slowing even before the pandemic started and my guess is that it will continue to decrease. Even so, there are people who we need to support more strongly than ever. Smoking has lost some attention among the general public because for many, it is an “invisible” problem: in a lot of people’s circles, they don’t know anyone who smokes. Although the national smoking rate is around 14%, among populations that face a lot of social and economic disadvantages, rates are just as high as they were decades ago. The smoking rate among people with mental health disorders, for example, is around 30-40%. This pandemic has exposed and magnified the major cracks in our society, particularly social injustices. A lot of people said they would need a job, financial stability, and social support to quit successfully: “once I’m at that stable place, I can do anything.” Fixing those systemic issues would hopefully lead to improvements in substance use and addiction. In some ways, disparities in tobacco use today is an outward symptom of a much larger underlying problem.
By: Christina Lee (CC '21)
I saw that past projects have included devices that improve pedestrian safety. Can you first speak a bit about your background and what types of projects your lab has done in the past? My background is in wireless embedded systems. Before joining Columbia, I was at Microsoft Research and then Intel Labs. My research has been focused on using embedded platforms, such as the smartphone, combined with data analytics methods, such as machine learning, to solve real-life problems. I think that’s something that has carried over with me when I joined Columbia five years ago. My lab is trying to work on research that tackles real-life problems. For example, in the pedestrian safety project, we developed technology that could enable pedestrians to better sense the world around them. They can be alerted in advance when a vehicle is on a collision trajectory. A lot of our other projects also have similar real-world motivations. I read recently that you have been developing a way to screen large groups of people for fevers. Would you be able to speak a bit more about this project and how it came about? This project is something we have been working on before COVID-19 started. The project was, at the time, not focused on fever detection, but instead on thermal comfort at scale. It was a project on how to improve energy consumption in buildings while optimizing individual occupant comfort. An important component of that project was to understand whether people are comfortable or not, and that’s actually quite difficult at that scale. One could write an app where the user tells you whether they are comfortable, but that requires direct input from the user. So we wanted to develop technology that could measure the skin temperature of every occupant in the building, which could easily adapt to fever screening. So that’s how this project came about. When the COVID-19 situation started, the hospital that my wife works in was facing a lot of shortages in PPE and personnel. During the check-in process, they needed to take the temperatures of patients. Since they had a lot of patients coming in, the check-in process was very slow, so they needed a better solution to screen patients. This project would potentially address this need. How has your progress been, and have you encountered any setbacks? So there are some technical challenges we are addressing right now. Simply relying on an inexpensive thermal imager that costs $300 is not accurate. You would need something in the order of $25,000 to get an accurate thermal image of a large space. We are trying to find a way to improve the accuracy of thermal images taken using a low-cost thermal imager. We developed a data-processing pipeline where we can first create a 3D model of every single person in the frame. Once we have a 3D model of every person, including his or her head, we can then map the images that we’ve taken so that instead of relying on a single image, we can add more images to the 3D model of the human head with the correct orientations. Since a person is not always looking at the thermal imager, the traditional approach is not going to get a very consistent and accurate shot of the face. With our approach, regardless of which side of the face is facing the camera, we can conform it to the 3D model of the head so we can continuously improve screening accuracy with the data that is incoming in. There is also the other question of calibrating the camera because different distances, humidities, and temperatures can all affect how pixel values translate to actual skin temperatures. This is something that requires time and many experiments to obtain calibration data, so that’s what we’re working on now. We have a working prototype, but we’re still trying to improve it. What do you think the public should know about the timeline of your research? What is your perspective on the future of your research and its impact on COVID-19? We’re trying to publish the design and the software within the next few months. Since this is something that would be incredibly useful for the public, we have been working on an accelerated time frame. We want to make sure that we can get our system out to the public within the next few months. We are hoping that once we have hospitals, schools, and other places use our system, we are able to receive feedback, build a community around this idea, and get others to help improve this system. In terms of a longer time frame, this is something that is useful beyond COVID-19. Creating this system at a low cost is useful for any kind of illness where fever is a symptom. So I really hope that this will have a long-lasting impact on the healthcare industry and many other places that can use a system that provides continuous detection of fever within a population. So maybe in the future, schools, cruise ships, buses, or subways can use this, and I really feel like this could be a game-changer. |