President Ruth Watkins: Welcome to the U Rising podcast, where you have the opportunity to meet some of the wonderful people who are helping the U achieve great things. Now, this is especially important podcast at the time of this really unprecedented and challenging COVID pandemic. We are all learning a lot every day, and there are no better people to guide us than University of Utah researchers.
I'm Ruth Watkins, the president of the university, and my guests today are three all-star researchers from the U—Lindsay Keegan, who's a research assistant professor in the Division of Epidemiology and also a theoretical biologist, Daniel Mendoza, who's an assistant professor in City and Metropolitan Planning and Atmospheric Sciences, and Jennifer Weidhaas, who's an assistant professor in Civil and Environmental Engineering.
These leaders and researchers come from very different departments, but they are all conducting research related to coronavirus and COVID-19. And we want you to know, listeners, that there are more than 100 researchers at the University of Utah now that are working on helping us solve the problems that have been associated with COVID-19. So, Lindsay, Daniel, Jennifer, warmest welcome.
Lindsay Keegan: Thank you for having us.
Jennifer Weidhaas: Thank you.
Daniel Mendoza: Thank you.
President Watkins: I'm glad that you've been able to take a few minutes during this really busy time. Well, Lindsay, I'm going to start with you. You were a guest on the U Rising podcast in March, which now seems like almost a lifetime ago. During that podcast, you told listeners about models and projections regarding the spread of coronavirus. Tell us what you've learned and bring us up-to-date on what we have all learned about coronavirus since March.
Lindsay Keegan: Yeah, it feels like years ago that we last spoke. It feels like years ago that this all started. I've been really busy working with a lot of really great researchers across campus. So, when we talked last, we were talking about some scenario-based projections of COVID-19 across the state of Utah. I was working on exploring different interventions, such as lockdowns, mask-wearing, comprehensive testing, and isolating and contact tracing. We've learned a lot here about what are the most effective interventions to both balance economic recovery and public health, making sure we have the health of the public at our first and foremost. What we've learned is that comprehensive testing and isolating is incredibly effective as is mask-wearing. Likewise, that work spun into work with Daniel Leung, who's in infectious diseases, and we were working on developing a clinical prediction rule.
We were looking at what are the predictors of a case of COVID-19? Are there demographic or symptom or clinical predictors that would make you most likely to test positive? Through that work, we actually found that in Utah, there's a lot of racial and ethnic differences in who tests positive. We found that Hispanic and Latino Utahns are two times more likely to test positive than white, non-Hispanic Latino Utahns. But thankfully, unlike other states which have found similar results, we didn't find a major difference in race or ethnic differences with hospitalization or symptom presentation, so that's a good sign.
Likewise, we looked at how we could potentially implement this clinical prediction rule and how it might help things. And what we found is that if you use this clinical prediction rule to prioritize testing through simulation, we found that by ordering tests by who is most likely to test positive is a really great way to help both flatten the curve, as we've all been saying, and also reduce the peak number of infections because you're able to get the people who are most likely to be positive, get their test results back quicker to them when you start to have limited testing availability. So thankfully in Utah, we haven't had limited availability, but a lot of other states have been suffering from lack of testing access. That's been a big one that we've worked on. Oh, it's so much. I'm trying to scroll through my Word document really quickly.
Another thing that we started looking at is as the epidemic progressed, we had initially only been thinking about who's going to be in the hospital. We've been talking a lot about ICU beds and hospital capacity, and one thing that really came up in mid-April was where are these patients going to go after they're discharged from an acute care hospital? A lot of them need continued care, continued support, and many people discharged from the hospital aren't actually COVID-negative. They're just no longer needing hospital care. So, we built this model with Michelle Hoffman, Matthew Maloney, and Peter Weir up at the hospital. We built this model of where people are going to go when they're discharged, what post-acute care needs they have.
Working with people at the state government, we actually were able to spin up a fully—I say we, but I did the modeling side of this, I had nothing to do with that. That was all Peter and Michelle, but we were actually able to spin up a COVID-specific long-term care facility. In talking with my colleagues across the country, this is something that we believe is unique to Utah. Patients who are coming out of the hospital here in Utah who need additional care, who are COVID-19 positive, can go to this specific long-term care facility and receive the care they need without risking spreading to other long-term care facility residents.
I've been working with a number of students on campus as part of the UROP program, and this has been probably the most rewarding work that I've done. Stemming from some of the work that we were looking at on racial and ethnic disparities, I was working with a graduate student, Theresa Sheets, and an undergrad, Alison McElroy, to use network models to understand if the differences in disparities in who tests positive by race can be explained exclusively through the difference in contact networks. We know that Hispanic and Latino individuals in Utah are much more likely to be essential workers, so is this explaining why they're more likely to test positive or is there something else going on?
Likewise, I've been working with a grad student, Emerson Earhart, and an undergrad, Jake Waldorf, and we've been looking at using genomic data to understand how many asymptomatic or undetected individuals we've had in Utah just by looking at who's already been tested, try to figure out those missing gaps.
And finally, I've only been peripherally involved, but I'd be remiss not to mention my work with Fred Adler, Alex Beams, who's a grad student, and Rebecca Bateman, our undergrad, working on what would happen as COVID moves from an epidemic disease to an endemic disease, what happens with COVID loss of immunity, and basically how this might cycle in the future. So, it's been a lot.
President Watkins: Lindsay, I have to say, that's an incredible list since March, since we last talk.
Lindsay Keegan: It's been crazy.
President Watkins: It’s highly relevant work, and I’m very grateful for what you're doing. Now, you mentioned students a little bit. Let's talk about that. I think you have some guidance, and you mentioned it briefly, about the things we can do ourselves with our behavior to help support health, safety, and wellbeing. Just give a little run-through of that again, from what you know from science that really does help us promote health, reduce the disease, and reduce the spread of disease. Because, of course, we're all thinking about that just about every minute of the day right now.
Lindsay Keegan: Yeah. The way that COVID spreads is through droplets that come out of your mouth and nose as you're speaking, as you're coughing, as you're sneezing, and the direction that they go is about a triangle extending out from your mouth, and the droplets themselves can really only travel about six feet before they start to taper off and fall to the ground. If you can remember from calculus, if you shoot a bullet, it's not going to go horizontal forever. Gravity will bring it to the ground. It's the same idea with these droplets.
There's some evidence that these droplets may be aerosolized or the virus may be aerosolized, which means that they're not just hanging in droplets in the air, but that they're actually floating particles. What this means is that mask use is incredibly important. First of all, if you put a mask over your face, if it's not aerosolized, you're going to stop how far and how many of those particles, those droplets, can get out.
Imagine if you put your hand in front of your face and you start talking, you'll feel the droplets hitting your hand, and that's actually what's spreading the disease. Same when you sneeze. There's a reason that kids say, "Say it, don't spray it." That's what you're trying to prevent with the mask. Likewise, if they're aerosolized, by wearing a mask, you're preventing some of them from getting into your body. Mostly when we talk about wearing a mask, it's for preventing you from potentially infecting anybody, but we're now finding that mask-wearing is actually preventing you from getting infected as well. Or if you do get infected, you're much more likely to get a lower dose of virus.
A study out of UCSF recently just found that in outbreaks where 100% of people are wearing a mask, of those who get infected, 95% are asymptomatic, which is a much higher asymptomatic rate than in general. So, wearing a mask, we're finding, is really protective for you. And obviously if you're not around other people, particularly indoors, this is the social distancing part of it, then that's going to reduce your risk as well. This part isn't that exciting. It's not so new as some of the other research I was talking about, but really maintaining that six to 10-foot distance from people, trying to avoid being indoors with other people, and wearing a mask whenever you're indoors with other people and whenever your closer than six feet out-of-doors.
President Watkins: Lindsay, I think that was about the best explanation that I have heard of the strong, powerful rationale around a mask, both for yourself and others, so thank you for that.
Lindsay Keegan: Thank you.
President Watkins: Thank you for the energetic way you have thrown yourself into this work.
Lindsay Keegan: It's been really exciting, and I haven't been at the university very long, but this has turned into a really great way to meet all of my colleagues across campus really rapidly, so it's been fantastic. I mean, a pandemic is never fantastic, but it has been an opportunity.
President Watkins: It's really our good fortune that you're here just at this moment. So, Jennifer, let's turn to you for a minute. You're involved in a pilot project analyzing wastewater, and I understand we're beginning to think that can offer some early signs of outbreaks of coronavirus. It sounds like you've had some interesting findings already. Tell us a little bit about this work.
Jennifer Weidhaas: Yeah, I'd be happy to. As an associate professor of environmental engineering, I've been involved with waterborne pathogen detection nearly my whole academic career, so testing for the virus associated with COVID-19 and wastewater was sort of a logical extension of my prior work.
I reached out to the Utah Department of Environmental Quality, gosh, early to mid-March to see if anyone in the state was monitoring wastewater for SARS-CoV-2. That's the virus that causes COVID-19. Then based on those conversations, I oversaw a two-week pilot study that showed we could detect the virus RNA in wastewater samples in Utah. After communicating those results to the state, they were really interested and they funded a pilot study where we looked at 10 facilities across Utah covering about 36% of the population, and we saw some very interesting trends there.
Since then, they have now funded us to study 40 facilities in Utah, covering about 80% of the population. That's in partnership with BYU and USU as well. We don't want to travel around too much, and that helps keep us where we are, to not have to travel around and spread the virus. And so some interesting findings.
We did actually see an increase in the virus signal in wastewater in Logan and Hyrum just before they had that outbreak there in June. We also saw a decline in the wastewater signal in Park City in April and June as their case count started to decrease. In the current study, for most of the areas in Utah we sampled, we do detect the virus. Unfortunately, there isn't a clear relationship between the virus levels and the number of infected individuals. That's something that we're still working on, but it's very exciting, the results we have so far.
I think for the state of Utah, it's also very important to understand that we're not detecting the virus leaving the wastewater treatment plants, so the public can feel confident that those facilities are doing what they're designed to do and are protecting the state water resources.
President Watkins: All very interesting and highly relevant, I would assume. Tell us a little bit about some of the advantages of trying to monitor, I guess, and you probably need to have a baseline understanding of where you are before any of this can be very useful. It seems completely noninvasive from the human side, so that part is good, and probably really an evolving science and understanding, I would guess, as we learn about this new virus. Has it been used with other viruses? And what are some of the advantages and ways that the wastewater analysis has played out over time?
Jennifer Weidhaas: Yeah, great question. It's been used to study poliovirus, actually, and track poliovirus outbreaks in the Middle East and vaccination campaigns there. Wastewater epidemiology more broadly has been used to look at illicit drug use. There's quite an interesting number of things you can find out when you look at wastewater, not just where the pathogens are, as we're using it here. But more specifically here in Utah, what's interesting about this study is that we did find that this wastewater epidemiology for SARS-CoV-2 is a really new application, but it has great potential. It's useful, we think, for detecting individuals who are both symptomatic and have been tested for COVID-19, but also asymptomatic individuals who don't know that they're ill. We can test an entire community at once, say 500,000 people that are feeding to a wastewater treatment plant or even 10,000 people in Moab, and we can get a sense for the number of individuals that might be ill there rather than going in and individually testing each person.
In the study for the state of Utah, we were able to test out in the tri-county area where they don't have a lot of money to do individual testing. We actually found some interesting findings there. They had some communities where they didn't think there was anybody that was ill, and we were finding it in the wastewater. And so since then, the Department of Health has been putting some more emphasis in that area.
President Watkins: And, of course, I can't help but wonder how this could be used on a college campus, and whether it could be used on a college campus, particularly if we could examine wastewater from different residence halls, for example. Sorry, but everything is relevant to the University of Utah lens in my world! I don't know if I put you on the spot too much if I ask you that question, but I'd love your thoughts.
Jennifer Weidhaas: That's a great question. This summer, Ed Clark actually reached out to a colleague of mine, Jim VanDerslice in the Division of Public Health. Jim was working with me with the Utah project, so he called me up, and we have actually done a little pilot on campus here to see whether the methods could be applied at a smaller building-by-building scale. We were able to detect the virus in the wastewater leaving the Health Science campus, but that's not surprising, because we do have COVID-positive patients up at the hospital there. But that sort of proved the principle that on a smaller scale, where you just have a few thousand or 100 people, it could work. We also tested a few dorms and athletic facilities on campus to see at a much smaller scale, would it work. And so this pilot study really helps us understand how environmental surveillance could be done on campus, but I want to emphasize it's still a new application of wastewater epidemiology, and it's exciting. It has potential applications for the campus. We're still discussing how it could really be used to benefit the campus.
President Watkins: I think that's helpful. We talk often about metrics that matter, and in this work, I am beginning to understand that this phase in a pandemic, we cannot rely on one thing. We need an arsenal of tools and measurement that help us really monitor the health and well-being of our community, but it is exciting to think that wastewater might be one more tool that could help.
Jennifer Weidhaas: Something we never think about, but hey, it'll be useful. Yeah.
President Watkins: That's great. Well, thank you for what you're doing. Daniel, let me turn to you. I know you've been involved in work that has looked at some of Utah's communities that have been most negatively impacted by coronavirus, and Lindsay referred to this and talked a little bit about health disparities. Tell us about your work and about what you've learned.
Daniel Mendoza: Yes. We actually were grateful recipients of a 3i Initiative COVID-19 seed grant, so we're appreciative to them for giving us the funding to conduct our study. What we've done, we split up our project into what was more during the acute phase, which really coincided with the Stay Safe, Stay Home directive that was enacted from March 16 all the way through May 1. What we wanted to look at is, we started to examine Salt Lake County at a zip code level, because zip codes are fairly homogenous in terms of social demographics. Really, two indicators we wanted to look at were per capita income and race. What we found was that, unfortunately, there are huge disparities across even an area as small as Salt Lake County, and we found that communities such as Rose Park and Glendale, primarily zip codes 84104 and 84116, had up to 10 times the positive cases than other wealthier communities.
What we found is that there is a significant difference in terms of traffic and in terms of positive cases, and we think that those are associated, because generally speaking, when we look at residential traffic, that's really human activity. And we found very little drop in terms of the less wealthy, which really coincide with higher percent minority communities. We saw maybe a drop of about 10 to 15% in traffic, and then we also found that in the wealthier communities, there's a 50% drop in traffic. What this really tells us is that some communities are much more able to take advantage of Stay Safe/Stay Home, so these measures to try to reduce the virus spread were more efficient in some communities than in others.
Again, I think we have discussed that primarily minority populations are more considered essential workers. I sometimes like to do away with euphemisms and really bring this to the front, and, unfortunately, they are expendable workers towards the economy. That's how they're looked at, and, unfortunately, we rely on them too much. One example I like to bring up is that, for example, in a regular office setting, you may be interacting with 10 to 15 people in close proximity during a whole day. However, if you are, for example, a cash register operator in a supermarket, you're interacting with that same number of people on an hourly basis.
The other impact or the other difference that we can see is also that if you're working as a cash register worker, your customers are not necessarily your friends. Whereas in an office, they are your friends, so you will take more measures to actually protect yourself and protect them, because you may know their families. You may know who they are. They are your long-term colleagues. That's why we think that the effectiveness of social distancing measures and protective measures are much better enacted in an office setting or really higher-income jobs and high-income populations.
Finally, one of the other things that we found that was fairly interesting was that the number of multigenerational families is much higher, and the number of square footage per family members is much lower in lower-income populations. What that means is two-fold. One is that if one family member gets sick, for example, first of all, there are many more family members in the home and they may not each be assigned, or they may not each have a room. So, we cannot, for example, close off a room for the sick individual to try to reduce contagion. There may be three or four individuals living in that room, and that really spreads the virus towards the whole family. So, people who are living in those kinds of residences are definitely much more prone to having the entire family get sick.
And the last part that we found that was actually really critical was if a person is, for example, an hourly worker, it's almost a luxury to take the test, because that would mean that the person has to miss a couple of hours of work, go to a testing center, and then is there really that much motivation to do that? Because most of these jobs are not secure, so they may test positive and their employer does not want them to be at work. Then they may just be at risk of losing their work permanently.
President Watkins: Well, Daniel, thank you so much for the work you're doing and for that summary. I think the very painful truths that you have articulated and supported through your research of how social justice, racism, has all conflated in the pandemic and in our society over the past months, and certainly revealed how much work we have to do to be a more just society. Interesting analysis or method of analysis, looking at traffic and traffic patterns, and being able to use traffic as a tool to understand who does not have the luxury to stay safe and stay home, and to really demonstrate how that varies by neighborhoods, and how strongly that's related to COVID-19 and disease and illness.
I think many of us have had the luxury of being able to be home and work, and there are also, every day on this campus, people who are working in supporting our students who live in housing and working in facilities who have not had that luxury, and of course our health providers. We are seeing it all the time in our institution as well. Your work on traffic, I think, has related to other aspects, and where we have met each other I think before has been in air quality conversations and work around the environment along the Wasatch Front. So, I think you are also involved in looking at traffic volume and air quality and what's happening there. Are there insights from the pandemic that have informed that work?
Daniel Mendoza: Yes, there are some. I always try to be a little bit more careful with the work that I do along with my team, because I think a little bit of the air quality impact or the improvement has been overblown. We were just coming out of our inversion season, which really ends here in Utah around February, and so March and April are really, really clean air quality days in general. I think that the easy, low-hanging fruit is to think about traffic reductions, which they're very dramatic. We can't take that away. Traffic dropped by sometimes up to 90% on some roads. And while that has a significant effect in, for example, the very close proximity of the buildings immediately next to some of the major highways, overall, the wind would normally disperse that traffic pollution around.
One important aspect, however, that we found was that it's really the restaurant industry shutting down that improved our air quality a lot. We've all driven by, for example, a fast food place or a restaurant that may be charbroiling. We see those large clouds of smoke. One restaurant could really be the equivalent of several hundred cars in terms of particulate matter pollution, and this is really the second phase of our study. Now, because we do have air quality sensors mounted on top of theTRAX trains, we have other additional air quality sensors, so we are really probably the best studied city and county in the world in terms of air quality.
Now what we're going to do is we're really going to break down and then really look at hotspots and where the air was very, very clean, because we can see this at a really high resolution. While maybe one really important factor may actually be the traffic, we think that the overall commerce, which also involves restaurants or other buildings. Because now, for example, many of the commercial buildings that were not in use, they did not need natural gas heating, for example, which also does pollute the air, and maybe some of the industrial facilities were also shut down. So, we really want to see this overall as a whole, what happened in terms of air quality.
Then the phase that goes directly with that, what our study is looking at now is the level of hospitalization. We have a hypothesis that there are really three groups of hospital patients. There's a group that may have, for example, smaller injuries or less severe problems who may just, due to fear of contagion, may just stay home, and those numbers we think may have dropped. We also have the very severely affected patients, patients with cystic fibrosis or COPD or childhood asthma, who also are at greater risk of having a COVID-19 contagion, and we think that those numbers may have also dropped. However, we think that the middle group of patients, for example, let’s think as an example of a broken arm, this is something that needs to be taken care of. We think that those patients may have kept their numbers constant in terms of hospital visits.
We've seen many studies where patients who have heart conditions really went too late to the hospital, and their condition degenerated further than it should have and sometimes may have proven fatal, so what we really want to do is really understand the sociology associated with the hospital visits during the pandemic. That's sort of our last phase of our study, and that, of course, does affect some of the traffic patterns that we've seen around hospitals.
President Watkins: It's impressive work, Daniel, and you have given us a little glimpse into how complicated it is and how much we can get it wrong when we try to assume a simple association between one factor and another, because clearly this is a complex type of work to do.
Daniel, Jennifer, Lindsay—we are so fortunate to have you as part of the University of Utah team. We're very proud of our role as one of America's leading research universities, and you are leading the way. Research universities are the place where coronavirus will be understood and hopefully COVID-19 will be solved, and we're grateful to you for the role that you have played in that. So, thank you for being my guests today. Thank you for the work you're doing and thank you for being part of the University of Utah research community.
And listeners, thank you for taking the time to join us for this really insightful description of some of the research on coronavirus and COVID-19 happening here at the University of Utah. There are many other researchers engaged in this important work. Listeners, I hope you'll join me for the next edition of U Rising podcast. Thank you.