July 4, 2025

Beyond the Sequence: People, Pathogens, and Power Dynamics

Send us a text The cutting edge of infectious disease control isn't just about vaccines or treatments—it's increasingly about data. Dr. Stephen Molldrem, Assistant Professor at the Institute for Bioethics and Health Humanities, takes us deep into the world of pathogen genomics and the remarkable ethical questions that emerge when we sequence disease-causing microorganisms. What happens when genetic analysis can potentially reveal who infected whom with HIV? Why do some communities welcome th...

Send us a text

The cutting edge of infectious disease control isn't just about vaccines or treatments—it's increasingly about data. Dr. Stephen Molldrem, Assistant Professor at the Institute for Bioethics and Health Humanities, takes us deep into the world of pathogen genomics and the remarkable ethical questions that emerge when we sequence disease-causing microorganisms.

What happens when genetic analysis can potentially reveal who infected whom with HIV? Why do some communities welcome these technologies while others resist them? From the controversies surrounding HIV surveillance in America to the enthusiastic adoption of TB genomics in Botswana, Dr. Molldrem reveals how the same scientific tools can take on dramatically different meanings depending on context, trust, and community involvement.

The COVID-19 pandemic accelerated the global adoption of pathogen sequencing, bringing terms like "variants" and "mutations" into everyday conversation. But this technological revolution has also revealed deep inequities—when South African scientists identified the Omicron variant and transparently shared this information, their reward was travel bans rather than support. This pattern reveals how scientific advancement doesn't happen in a vacuum but within complex social and political realities.

At the heart of Dr. Molldrem's work is a fundamental reminder: behind every genetic sequence is a person, a community, and a set of lived experiences. As one HIV advocacy slogan puts it, "We are people, not clusters." The challenge for public health isn't just implementing new technologies but doing so in ways that respect human dignity and build rather than undermine trust.

Whether you're fascinated by the science of disease tracking, concerned about health privacy, or interested in how new technologies reshape our understanding of outbreaks, this episode offers a thought-provoking journey through the socio-technical landscape of modern infectious disease control. Join us as we explore what happens when cutting-edge science meets complex human realities.

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00:09 - Introduction to One Health and Hosts

03:38 - Dr. Moldrum's Work in Health Ethics

08:55 - Understanding Science and Technology Studies

12:23 - Pathogen Genomics in Public Health

19:40 - HIV Surveillance and Treatment Evolution

29:00 - Media Coverage and COVID Variant Tracking

36:15 - The Politics of Pathogen Data

51:25 - TB Research in Botswana

01:07:51 - HIV Criminalization and Public Trust

01:12:38 - Book Recommendations and Closing

WEBVTT

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This is a podcast about One Health the idea that the health of humans, animals, plants and the environment that we all share are intrinsically linked.

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Coming to you from the University of Texas Medical Branch and the Galveston National Laboratory.

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This is Infectious Science.

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Where enthusiasm for science?

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Is contagious.

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Welcome back to the Infectious Science podcast.

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We are excited to be back here.

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We have Christina and we have Camille.

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How are you guys doing?

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Doing well.

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We're hanging in there.

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Yeah, it's been a lot, it's been a big beginning of the year and we're coming to the close of our academic year, and so it's just everything's piling up, but we're getting there.

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Christina, since we're coming to the end of season number two of the Infectious Science Podcast, we've gained quite a few new followers.

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Let's do some introductions Like who are you, christina?

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I am Christina Rios.

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I am a second year medical student here at the UTMB School of Medicine.

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I am originally from San Antonio, texas, and I love my hometown very much.

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I am of Latino descent and I'm very proud of that.

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I'm a daughter, I'm a sister, I am an aspiring scientist and physician, and I love animals.

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Camille, who are you?

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Not as good of an intro as Christina.

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Yours is much better.

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I'm Camille Adu.

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I'm one of your co hosts.

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Thanks for listening in to Infectious Science.

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We appreciate all of our listeners and it's always great to hear from everyone who's been reaching out.

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I am coming to the end of my academic journey.

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I'm defending next month.

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So right now, as we record this it's January 31st 2025.

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2025.

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And I am very excited to defend next month.

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You must be so proud.

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I am so proud and also so stressed.

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But we're getting there and I'm really excited.

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When I am not in the lab or writing my dissertation, I'm reading a lot.

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I think my book count so far for the year is at 11 books, so we've restarted again.

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What was it last year?

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Oh, what did I finish out at?

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That is a great question.

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Oh, it was more than a hundred.

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Yeah.

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Let's see.

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What did I end up with for 2025?

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I'd just like to preface this by saying I think it was like January 2nd, that Camille sent Dennis and I a text that she had finished her first book of the year.

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That is true.

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Yes, last year was 127 books, and I yelled at my phone when I got the text because I wish I can finish a book in three months.

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Yeah, so last year I read 127 books.

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This year I'm hoping to do more, because I will not be spending as much time in grad school, so I'm excited to finish and start my work as a medical writer.

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Amazing, who are you?

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Yeah, dennis, you're looking very snazzy today.

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Reintroduce yourself, just for you guys.

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Hi, I'm Dennis Bent.

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I'm a professor of microbiology and immunology and I'm a German.

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I'm a man, a son, and what else did she say, christina?

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But I'm also very excited to talk about our guest today.

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So it's a great pleasure to introduce Dr Stephen Moldrum.

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He's an assistant professor in the Institute for Bioethics and Health Humanities and we've talked about having him on the show for quite some time because his research is really interested, and I'm really excited about this episode.

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Can't wait to get into it.

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Stephen, do you want to introduce yourself?

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Sure, yeah, thank you.

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Thank you so much for the introduction, dennis, and thanks so much for having me here on Infectious Science.

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I'm looking forward to the conversation we're going to have.

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My name is Stephen Moldrum I use he him pronouns, and, as Dennis said, I'm an assistant professor on the Institute for Bioethics and Health Humanities here at the University of Texas Medical Branch, where the Infectious Science podcast is recorded, and my research mainly exists in the field of science and technology studies, also public health ethics, and I'm an ethnographer, and so I use mainly qualitative methods and policy analysis methods, largely to study the politics of health data and also of infectious disease and the introduction of emerging technologies into infectious disease control, and I think that's what we'll mainly be talking about today, but I'll leave it there.

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I have a few other interests too.

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I mean sexual and gender minority health, mainly in regard to data practices, and I'll also say my research is mostly based in the US, but I also have done studies in global health policy, global media discourses, and also I have an ongoing project in Botswana related to tuberculosis.

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My PhD is in American culture the field is actually called American Studies, but Michigan has a strong identity of keeping the title American Culture and I also have a certificate of graduate studies in science, technology and society from Michigan.

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And I did a postdoctoral fellowship for two years at the University of California, irvine, in the Department of Anthropology, and then I came here and I've been at UTMB in the Institute for Bioethics and Health Humanities for three and a half years.

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Are you originally from Michigan?

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I am not originally from Michigan.

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I traveled around a lot growing up.

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I went to undergrad in Washington DC at the George Washington University.

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But yeah, lived many places growing up.

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Very cool.

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What is science and technology studies for our listeners?

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Yeah, science and technology studies is a field that emerged from the sociology and anthropology of science and technology in the 1970s.

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That was a heady time for the social sciences.

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There were a lot of challenges to, for example, institutional ways of knowing in the sciences from the social sciences that were really interested in using the tools of the social sciences to understand, for example, how the quote unquote hard sciences develop knowledge and then how knowledge takes on material form in society in the form of, for example, the formation of disciplines, the translation of scientific knowledge into policy, and so the field that's often talked about is this new upstart field, but it's 50 years old at this point.

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There are different branches of science and technology studies.

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Of those, I am primarily probably working in social studies of biomedicine and social studies of health and also infrastructure studies.

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We use the term in socio-technical a lot, so in science and technology studies, or STS as I will call it as we move along is always interested in thinking about how technology, the tools that scientists use, are co-mingled with the social and professional lives of scientists and how the operation and maintenance of things like large data infrastructures, which I think about a lot in the public health context, are always bound up with the people who are using and building and maintaining those systems and the technologies that they're using to do so.

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A lot of this has actually become diffused throughout the social sciences and the culture at this point, but it was a really radical notion in the 70s and 80s that you could, for example, treat the emergence and construction and use of scientific facts using the tools of like sociology and anthropology and science technology studies as many things, and I think we can continue to revisit it as we talk.

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Yeah.

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No, I think that's an excellent overview and I appreciate it as well because it's not something people tend to be like siloed in their field.

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So I always appreciate hearing what other people are doing and studying.

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So, in regards to like more specifically, what you're doing, when we first talked about this episode, we really talked about your work looking at pathogen genomics and pathogen phylogenetics, and so, before we get into that, I just wanted to quickly define them for our listeners.

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Pathogen genomics is the study of genetic material of microorganisms that cause disease.

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Pathogen phylogenetics is the study of how microorganisms evolve.

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It's often used in public health to track disease outbreaks and reveal transmission patterns.

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So, with those definitions out of the way, of course, if you have anything to add to those definitions, I want to hear it.

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But could you tell our listeners why pathogen genomics and pathogen phylogenetics matter, particularly right now, like in the present moment?

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Yeah, totally no.

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I think those are good definitions of pathogen genomics as a field.

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There are many things that are gathered under that signifier and pathogen phylogenetics being both like a method right that is used to track pathogen evolution but then also has specific applications in research and public health, whereas pathogen genomics isn't necessarily about pathogen evolution but can be used in, for example, tracing the emergence of in surveillance, for example, drug-resistant variants of pathogens or within an individual patient, clinical applications of genomics.

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To take a little bit of a step back in terms of how I got focused on studying this on studying this, my work initially was about the emergence of the application of digital infrastructures and public health programs in the United States, really starting in the period after 2009,.

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When there was a law passed it was called the High Tech Act.

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That was actually part of the 2009 American Recovery and Reinvestment Act, huge bill, spending bill that digitized the US healthcare system.

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And when I say I'm interested in emerging technologies and infectious disease control, it is about pathogen genomics.

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But the use of pathogen genomics in routine public health practice was really facilitated by this much larger phenomenon, which is the digitization of the health system in the United States and health systems generally, and this is something that really took off in the 2010s and began with the process of digitizing electronic health record systems, which was a very fascinating policy battle in the United States, but then also electronic laboratory reporting infrastructures to public health, and so what I've tried to document in my work over time is how basically this digital base has been developed that enables different forms of interoperability between the domain of clinical care and the domain of public health practice.

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Since pathogen genomics became an object of interest for me when I was actually studying this process in regard to changes in HIV care, surveillance and prevention during the 2010s, and so I was doing research, I did over two years of ethnographic field work in Atlanta, georgia, in the HIV safety net from 2016 to early 2019.

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From 2016 to early 2019.

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And I was studying there there were these multiple transitions happening during that time in regard to HIV and how it was managed by public health institutions.

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One of them was the introduction of knowledge about undetectable viral loads becoming undetectable, making one untransmissible, or U equals U in the kind of public-facing messaging, and when operationalized as a public health strategy, this is often called treatment as prevention, and so this was taking off in the period when I was doing my field work and what I was paying attention to most closely was the ways in which public health systems.

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Then they had a much greater incentive to monitor in real time who was virally suppressed in their jurisdictions, because there was just a real reorientation, this time in HIV care and public health, around bringing viral loads down and then in identifying.

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During this period in the US there were a series of programs rolled out that were focused on identifying people who had fallen out of care based on whether or not the public health department had received blood work from them.

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Identifying people who had fallen out of care and then reaching back out to them to bring them back into care.

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So this was a very new set of programs in the United States.

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Historically, like these, hiv surveillance systems and clinical care and prevention infrastructures were quite separate from one another, that's really interesting actually seeing how the actual clinical aspect of HIV care was translated into a public health outreach in ways that a lot of people maybe didn't know about.

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So that's really cool.

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Yeah, and this is as part of this.

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In 2018, in all health departments, cdc began requiring the use of HIV genetic sequence data, which are generally ordered in the clinical context to monitor drug resistance, the potential emergence of drug resistance in a patient and CDC standpoint was using phylogenetic analysis to monitor the growth of emerging transmission clusters of HIV.

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This is what actually brought me to thinking about pathogen genomics and pathogen phylogenetics.

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I was initially interested in the digitization of health systems and how that facilitated different uses of data such as this, and how that facilitated different uses of data such as this.

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I had a question about this.

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So do you think this kind of reorientation has?

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Has it increased the people that were retaining in care?

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Because I know and I know this because this is just in my dissertation, we were just talking about it, so I just found the stat so the CDC says that for every 100 people living with HIV in the United States, only 54 are retained within the care system and about 66 achieve viral suppression, which is certainly, with as wealthy of a country as we are, we could do better.

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So do you think that it changed like how we're maintaining people in care and like the level of care that people were receiving?

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Yeah, so this is actually quite a fraught question.

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So this is sort of where I think we can think about where the science and the public health practice meet politics in this regard, because what I discovered when I was doing my research is that these programs and the reorientation of the US National HIV-AIDS Strategy so it was the first National HIV-AIDS Strategy for the United States came out in 2010.

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It was this big moment and it has been updated since then.

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But if we situate ourselves in the period of the mid-2010s, there was a great deal of optimism around technology digital technology generally to solve social problems which has really waned in recent years right this sort of what we call an STS techno-optimism right around digital technologies to solve social problems.

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There's a great deal more of societal skepticism of that.

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But if we position ourselves in the 2010s, you had a few things going on in regard to HIV, because you had the convergence of a very optimistic sort of civil society liberal progressive coalition that had coalesced around Obama.

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You had the digitization of the health system, being able to use data in new ways to link people to care, and then, at this time, you also had the emergence of treatment as prevention.

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So this was a moment of huge optimism that maybe we can use data in new ways and this new knowledge about HIV transmission to end the epidemic by bringing people into care, whether the programs have worked generally, what the evaluation papers in this space has found and this program initially it was called Data to Care and it's still called that when the technical literature and the people who do this if you want to look up these programs, that's what they'll be called, the evaluation literature has generally found that it's very expensive to conduct these investigations to bring people into care and relink them.

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Their effectiveness is debated but in partly because a lot of it is done using cost-benefit analyses for bringing people back into care, have been of this other process that was mixed up in HIV in this time, which was the integration, right of care, prevention and surveillance, where they really put a lot of effort in.

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I'm thinking about a group in Washington and Seattle, for example, to try and keep people retained in care after a successful relinkage intervention, and it was just really hard and a lot of people ended up still falling out of care, not being able to be retained, because people were having trouble remaining in care.

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In the United States we don't have a health care system that's designed to keep people in care.

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We have a very bad health care system, but also you're generally talking, these are folks who are having a hard time in other ways too, maybe with housing, food security, things like this substance use, things like this substance use.

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But in any case, I'm going to answer your question.

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But it's a long way around the barn, because what happened was there was all this optimism around moving HIV toward a treatment as prevention paradigm, and then things really changed after 2016 and into 2017.

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So you had HIV.

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Civil society was really on board with these uses of data and then there was a switch that happened sometime between 2017 and 2018, when actually that coincided with the use of molecular HIV data or HIV genetic sequence data in these programs, doing this cluster modeling.

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So at this time, the organized networks of people living with HIV and others in civil society there had been a switchover from the Obama to the Trump administration began to really voice a different set of concerns around privacy, concerns, confidentiality, concerns around criminalization, because, for example, hiv genetic sequence data and phylogenetic analysis can be used potentially to infer who may have infected whom in a data set.

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So there's a lot of concern around this and then this led to a real backlash during this period to these programs.

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That was led by segments of HIV civil society, which is ongoing to this day, in fact, and I've written a couple of papers tracking this controversy.

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And to answer your question, do I think that these programs have worked?

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I think that we're still at like less than 70%, certainly probably around 65%, viral suppression in this country.

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I would say probably not at a population level, but also even if we look at their implementation.

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Their evidence of effectiveness is very much debated and I always like to say, if that, if someone could walk down the street and get into care easily, there wouldn't necessarily be the need for all of these programs and advanced data uses.

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But that's not the healthcare system we have.

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So they remain an object of a lot of controversy.

00:18:24.950 --> 00:18:25.972
Yeah, Stephen.

00:18:25.972 --> 00:18:36.011
I was wondering can you give us some examples how pathogen genomics and pathogen phylogenetics is in their life or where they run into this during their life?

00:18:36.011 --> 00:18:43.686
For people that probably don't know exactly what this is, Can you give some very 30,000 feet examples?

00:18:43.906 --> 00:18:44.548
Yeah, sure.

00:18:44.548 --> 00:18:49.736
So where an everyday person might run into pathogen genomics, there might be a couple of different ways.

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If you're a person living with HIV and you, for example, are having problems keeping your viral load suppressed with your care provider, your care provider might order a drug resistance test for you.

00:19:02.834 --> 00:19:10.588
That is, the provider ordering an HIV genetic sequence Because HIV is a reportable condition to public health.

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Right, it goes to public health so that they can use it in surveillance programs and also in prevention outreach programs.

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The data that the lab generate will be reported back to the provider and to public health.

00:19:22.721 --> 00:19:35.801
In the clinical context, these data can then determine okay, this patient or this person is having trouble remaining virally suppressed because they have a kind of HIV that's resistant to this particular medication, so we're going to change their medication.

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The guidelines change around this with some frequency.

00:19:38.888 --> 00:19:40.452
So what providers do?

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It's generally in line with the guidelines, but the guidelines change a lot.

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They're like updated every year.

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So a lot of providers will order an HIV genotype test or a drug resistance test for our patient upon enrolling or re-enrolling in care.

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And so an everyday person who is not living with HIV.

00:20:01.501 --> 00:20:13.498
where they might run into this, it's hard to actually say Would you say it could potentially be used in, like the analysis of bacteria, let's say an antibiotic resistance when it comes down to it, used all the time.

00:20:13.958 --> 00:20:14.159
Yes.

00:20:14.159 --> 00:20:19.583
To see what antibiotic your particular infection might be susceptible to, especially if they're concerned.

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You have an antibiotic resistant infection, used all the time.

00:20:21.970 --> 00:20:32.886
Yes, and this is where I was going to go, which was in media coverage, is where I think people might see some of this.

00:20:32.886 --> 00:20:40.627
So if they're hearing, for example, about the emergence of antimicrobial resistant or antibiotic resistant, let's say, gonorrhea is, there are waves of media discourse about this almost every year.

00:20:40.627 --> 00:20:45.659
Cdc has been warning about the coming pan-resistant gonorrhea since the 1970s.

00:20:45.659 --> 00:21:04.909
That has its own fascinating history, but also, for example, in the H5N1, avian flu is being covered in the media a lot right now, when the news articles are basically talking about how pathogens, how bacteria or viruses, mutate and acquire new characteristics.

00:21:04.909 --> 00:21:07.173
This is pathogen genomics in use.

00:21:07.680 --> 00:21:08.462
And so yeah.

00:21:08.522 --> 00:21:16.105
So they will be framed in the media often as like the potential emergence of a super bug or a species crossover, for example.

00:21:16.105 --> 00:21:37.037
I know that's of big interest here in infectious science, but these are media discourses that happen that I think most people would be familiar with and also, I would say, linking it back to HIV the public communication of emerging HIV clusters that have been identified through phylogenetic analysis.

00:21:37.037 --> 00:21:41.367
Public health departments will often work with local media to communicate about these.

00:21:41.367 --> 00:21:57.431
One very well-known example is Scott County, indiana, in 2015, where there were several hundred new cases of HIV because of very distinct practices of needle sharing that were going on in that community.

00:21:57.431 --> 00:22:00.281
But now the health department wants to communicate about growing HIV clusters.

00:22:00.281 --> 00:22:03.594
So if you're hearing this term HIV clusters, this is probably a reference to phylogenetics being used in public health practice growing HIV clusters.

00:22:03.594 --> 00:22:07.666
So if you're hearing this term HIV clusters, this is probably a reference to phylogenetics being used in public health practice.

00:22:08.087 --> 00:22:10.262
Steven, I'm going to throw you a curve ball right.

00:22:10.262 --> 00:22:28.365
This is a One Health podcast, so are you aware of any relevance in veterinary medicine, animal medicine or environmental sciences where, like pathogen, genomics plays a role, or whether this is being discussed, or is this a purely human topic?

00:22:29.006 --> 00:22:31.991
No, so pathogen genomics are very much used in One Health.

00:22:31.991 --> 00:22:34.523
One Health is not my area, my primary area.

00:22:34.523 --> 00:22:49.769
I will be very clear in saying, however, there is one project I have waiting in the wings that I need to pursue further, which intersects a lot with One Health, with One Health meaning the whole thing human-animal environment, right, and this is the concept of a resistome.

00:22:49.769 --> 00:23:08.059
Which is this very interesting concept that essentially is a conceptual abstraction used to describe the drug resistance profile of all of the bugs that might be in a particular area and they so.

00:23:08.059 --> 00:23:13.464
The technology generally is metagenomic sequencing sequencing everything inside of a sample to see what's there.

00:23:13.464 --> 00:23:23.295
You can generate what scientists who are working in this area call a resistome profile, which will give you the resistance profile of all the bugs in that sample.

00:23:23.675 --> 00:23:32.753
What I find very interesting about this concept is that it scales in these sort of incredible ways, and so one is.

00:23:32.814 --> 00:23:52.655
For example, and when I first became aware of the concept is there was a paper about the specific resistome profiles for bacterial antimicrobial resistance among men who have sex with men who were taking PrEP for HIV, which is an HIV prophylactic medication.

00:23:52.655 --> 00:24:07.021
In the paper, they had done throat swabs and determined that gay men who were taking PrEP had a distinct resistome profile in their oropharynxes this is when I first came across this term in their oropharynxes.

00:24:07.021 --> 00:24:08.205
This is when I first came across this term.

00:24:08.205 --> 00:24:22.935
However, the term resistome is also used, for example, to describe in other papers I've seen, like an entire region where there's like maybe a lot of agriculture accounting for, like water samples from agricultural runoff, sampling from animals and maybe people there, or like a city block.

00:24:22.935 --> 00:24:34.650
So there are some papers where they would have gone around a city block, swabbing various surfaces and then sequencing it all to come up with a resistome, and so that is the closest to One Health that I think my work has gotten on.

00:24:34.650 --> 00:24:37.387
That's a project I want to pursue, but I haven't quite yet.

00:24:37.528 --> 00:24:51.983
And antimicrobial resistance is such a big topic in One Health right, because we have the overuse in human medicine, but we also have the overuse in veterinary medicine, so your resistome projects is probably extremely irrelevant and from my perspective.

00:24:51.983 --> 00:25:02.374
Also coming back to the veterinary medicine right, the phylogenetics is often used as a public health response to fight highly transmissible animal diseases.

00:25:02.374 --> 00:25:21.351
Right, like we see this now with avian influenza, where they track where did this come from this virus, what migratory bird brought this to this agricultural farm or to this, and they can trace it and they can almost like trace the history of where it came from and what farm got infected next, and so on.

00:25:21.351 --> 00:25:25.289
But I think it's way less controversial than it is in human disease.

00:25:25.289 --> 00:25:26.505
Would you agree or disagree?

00:25:26.644 --> 00:25:31.825
I would agree, but I would also say even making some of the claims that you're making there right.

00:25:31.825 --> 00:26:05.256
So, like this idea that you can use pathogen phylogenetics in the H5N1 avian influenza context to track which bird or which flock brought this strain of H5N1, and strain might be the wrong word to use but this subtype, let's say, of H5N1 to another area, you're making transmission, what are called transmission directionality inferences, when you're doing that right, and one of the things I'm interested in in pathogen phylogenetics is where making these kinds of inferences becomes acceptable and when it is problematized it's not acceptable.

00:26:05.416 --> 00:26:11.393
Yeah, that's fair, yeah, so in HIV, for example, and programs that have been rolled out in the United States.

00:26:11.393 --> 00:26:17.292
There's a lot of concern about inferring who may have infected whom and inferring directionality.

00:26:17.292 --> 00:26:18.884
Cdc says they don't do that.

00:26:18.884 --> 00:26:27.945
Activists claim if you're mapping clusters and doing investigations based on the combination of the phylogenetic data and the behavioral data, how can you not be doing that?

00:26:27.945 --> 00:26:29.507
And this is the heart of the controversy?

00:26:29.507 --> 00:26:45.017
But right, if you're talking about birds or, honestly, if you're talking about other pathogens, for example TB, in certain contexts for TB affecting humans, those directionality inferences are made and they're much less controversial.

00:26:45.017 --> 00:26:47.887
So it depends a lot by pathogen and context.

00:26:48.400 --> 00:26:50.327
And I think also depends who you're talking.

00:26:50.327 --> 00:27:05.453
To A vet, that might not be a controversial statement to say here's the farm where it originated, but to a farmer who owns that farm, who then might be responsible for everyone around them in a radius to have their flocks potentially destroyed to prevent the spread of avian flu.

00:27:05.453 --> 00:27:16.526
That might be a very sensitive topic and that definitely happens in the United States and we've certainly seen that where entire flocks have been culled and, as someone who grew up on a farm, there's strong concerns about biosecurity.

00:27:16.526 --> 00:27:22.785
But also, who is the source zero of something like that happening, because it can be devastating for that particular community.

00:27:22.785 --> 00:27:26.853
So, yeah, I think it all depends on who you're asking, on whether or not it's considered.

00:27:26.873 --> 00:27:29.964
But I think this brings back then the question of science literacy, though.

00:27:29.964 --> 00:27:43.570
Do the farmers understand why certain decisions were made based on phylogenetics or not, because often the veterinary health response is so strong and there's this response of culling instead of not what we normally do with humans, right?

00:27:43.570 --> 00:27:45.272
Do they understand what was implemented based on, maybe, right?

00:27:45.272 --> 00:27:48.567
Do they understand what was implemented based on, maybe some sequencing?

00:27:48.747 --> 00:27:50.852
Yeah, I think farmers understand what happened.

00:27:50.852 --> 00:27:56.744
But I think when you're saying there's this like line of transmission, then you always have someone who might be at fault potentially.

00:27:57.025 --> 00:27:59.211
And I think that's what's sensitive more than like.

00:27:59.230 --> 00:28:05.423
yes, certainly, people can lose their livelihoods and there's entire issues with that, and that's an interesting conversation within the ag community.

00:28:05.423 --> 00:28:11.367
But I think the idea of here's where this originated in this local area, at this farm or something that's what's more sensitive.

00:28:11.587 --> 00:28:16.671
Have there been culls of like flocks based on phylogenetic studies?

00:28:17.113 --> 00:28:18.758
Based on phylogenetic studies?

00:28:18.758 --> 00:28:22.964
I'm not sure based on phylogenetic studies, but certainly if it's found in a local area.

00:28:22.964 --> 00:28:30.875
So, like I grew up on a farm, and certainly if you had avian flu, everyone around you who has poultry, their flocks will be cold.

00:28:30.875 --> 00:28:32.864
So there's still this assignment of fall.

00:28:32.864 --> 00:28:35.852
I don't know that it's ever been down to phylogenetics.

00:28:35.852 --> 00:28:37.844
I've never gotten into it or had to deal with it.

00:28:37.844 --> 00:28:38.464
I'm like a person.

00:28:38.500 --> 00:28:40.267
I think it's just more based on epidemic.

00:28:40.267 --> 00:28:41.531
Radius.

00:28:41.531 --> 00:28:42.275
Yeah.

00:28:42.316 --> 00:28:42.857
Like location.

00:28:43.451 --> 00:28:46.234
To connect this back to the conversation about HIV.

00:28:46.234 --> 00:28:55.077
The fundamental question in the controversy about HIV phylogenetics there are these specific issues, right there's is directionality being inferred.

00:28:55.077 --> 00:29:01.542
Can directionality, epistemologically speaking, methodologically speaking, ever in fact be definitively inferred?

00:29:01.542 --> 00:29:27.262
That kind of thing, these questions exist, but they are specific manifestations of a deeper, underlying issue, which is the introduction of pathogen genomics and, in the HIV context, specifically pathogen phylogenetics to inform public health action for reasons other than surveillance, but to inform actual outreach to people, to link them to care.

00:29:27.262 --> 00:29:45.740
Does adding these technologies in some way fundamentally transform the work of public health and what public health agencies are doing in mapping clusters, or is it an additional tool that is in the public health toolkit that informs routine practice?

00:29:46.750 --> 00:29:55.701
And I think that's a really good segue into our next question that we have for you, dr Moldrum, which is you've written about the role of media in bringing pathogen genomics into the public eye.

00:29:55.701 --> 00:30:10.176
But, as you've previously stated, there is still a gap that persists in using this information to better the public health when it comes down to it, and also better the care for the people who do have these diseases and these infections.

00:30:10.176 --> 00:30:14.144
How would you recommend that we bridge this gap?

00:30:15.269 --> 00:30:22.099
Yeah, that's a really big question and so for me, my studies in pathogen genomics, pathogen phylogenetics, have been in three areas.

00:30:22.099 --> 00:30:33.355
Right, I've looked at HIV in the United States and the introduction of molecular HIV surveillance is what it's called in public health right Into routine practice along with using that in cluster response.

00:30:33.355 --> 00:30:47.693
I've looked at media coverage of controversies are about pathogen genomics in SARS-CoV-2, particularly some of the early controversies around claims about evolution of SARS-CoV-2 toward greater transmissibility.

00:30:47.693 --> 00:30:55.288
So for our listeners you've probably heard of things like the Delta variant and the Omicron variant and all of the Omicron sub-variants.

00:30:55.288 --> 00:31:05.740
I've written about those and the media politics of those, but also of earlier controversies even before the COVID-19 pandemic declaration and in tuberculosis in Botswana.

00:31:05.740 --> 00:31:24.823
And in global health policy following, the World Health Organization has recently recommended that all countries with capacity implement a version of sequencing called targeted next generation sequencing, targeted sequencing to identify TB, drug resistance and for use in routine surveillance.

00:31:24.823 --> 00:31:38.152
And I've thought about this technology and this technology has been my entry point to think about these issues in a number of different contexts and so I'd say that, to answer your question, each context has been extremely different.

00:31:38.152 --> 00:31:54.744
Right, for HIV, I think that the use of HIV phylogenetics and cluster detection and response methods in public health and whether or not that is going to become something that's acceptable to the community of stakeholders is something that's fundamentally unsettled.

00:31:55.009 --> 00:32:07.125
We can talk about the political economy of pathogen genomics, which is something that really interests me, which is basically what are the political and economic drivers of health departments investing in these technologies in the first place?

00:32:07.125 --> 00:32:09.374
You know what the COVID-19 pandemic did.

00:32:09.374 --> 00:32:15.044
Is it really accelerated the rate of adoption of sequencing technologies across the world?

00:32:15.044 --> 00:32:31.741
So you had large global health funders really sending sequencing machines to ministries of health around the world along with reagents around the world, along with reagents and saying sequence COVID uploaded into GIS aid and other repositories so that we can understand patterns of SARS-CoV-2 evolution globally.

00:32:31.741 --> 00:32:38.103
Huge increase in the sequencing capacity around the world, which we can talk about later in the podcast.

00:32:38.103 --> 00:32:43.315
But that's played a huge role in understanding the emergence of different variants and also the reformulation of vaccines.

00:32:43.957 --> 00:32:54.942
I'm a little less convinced that media stories about every single SARS-CoV-2 sub-variant of sub-variant are helpful.

00:32:55.303 --> 00:33:18.496
But what I also think is that and have observed and written about, is that the wide uptake and use of genomic discourses to describe the evolution of SARS-CoV-2 have been fundamental to public discourse about the COVID-19 pandemic and I think, have induced changes in how people relate to pathogens generally.

00:33:19.037 --> 00:34:03.342
I was in this really weird position at the start of the COVID-19 pandemic where I had been writing about HIV phylogenetics I've been writing about pathogen phylogenetics in the HIV context and so I knew about the technology and how it could be used and found myself fascinated by early controversies around SARS-CoV-2 and then writing about them, where scientists were having debates in public forums, for example, and forums like web forums, sometimes papers, but in those early days they were often fighting with each other online about whether or not SARS-CoV-2 was evolving to be more transmissible, and then to see this actually become like a major orienting media discourse about the pandemic itself and so like within the span of a few months.

00:34:03.342 --> 00:34:35.713
I was someone who studied something that was this sort of very obscure thing that no one really knew anything about unless they were really in the field, to something that my relatives were talking about uptake of media discourses about pathogen evolution that have been enabled by the increasing infrastructural capacity of the global health system to sequence and analyze pathogen data.

00:34:35.934 --> 00:34:48.123
Yeah, and I can imagine too, just like being able to execute a large public health response really does vary, like you're saying, on what is pertinent to the public at the time.

00:34:48.123 --> 00:35:12.375
So I can imagine it can be frustrating at times too when you have all this data and all this information on variants and viruses and bacteria and a lot of different pathogens that could be really beneficial but that it maybe seems like the public doesn't really care about too much at the time being, and so I guess the battle there is like bridging that gap between the interest and the actual data.

00:35:12.375 --> 00:35:13.036
That's there too.

00:35:13.378 --> 00:35:14.501
I do think it's interesting.

00:35:14.501 --> 00:35:16.217
I worked as a contact tracer in New York.

00:35:16.217 --> 00:35:22.070
I just graduated with my degree in infectious disease biology Again, something everyone was like why do you study this, what do you know about?

00:35:22.070 --> 00:35:23.574
And then the pandemic happened.

00:35:23.574 --> 00:35:32.463
And then suddenly everyone was calling me, and when I was a contact tracer in New York state, so we certainly collected data on what variants we were seeing in populations.

00:35:32.463 --> 00:35:45.581
But we would, of course, call people and then do contact tracing and people wanted to know what variant they had and we and it's just not something that would populate for us Like we collected on a an aggregate level but not on an individual level.

00:35:45.581 --> 00:35:57.898
We weren't like oh, like, you have Omicron or whatever, and so I think that it is interesting that people were relating to it in that way that they wanted to know, like what particular variant they had basically gotten infected with.

00:35:57.898 --> 00:35:59.483
So it was very wild.

00:35:59.809 --> 00:36:23.436
That's really cool, and I think that it also shows how, even if you don't have a strong science background, people really are invested in their health, and I think they're becoming more and more invested in their own personal health, and so I think that this is hopefully a promising direction for pathogenomics and pathogen phylogenetics too, because if people want to know more about what's happening to them or what they're susceptible to, hopefully you guys can step in and play that role.

00:36:23.677 --> 00:36:43.141
Well, just to follow directly on something that Camille said and yeah, I totally agree, I think this is where science and technology studies is really useful, because it's like people, because there was the possibility of having public discourse about different kinds of variants, that was enabled by the underlying public health infrastructure changing.

00:36:43.141 --> 00:36:50.610
They were then able to ask you this question, but actually, infrastructurally, for you to deliver that information then was not actually possible.

00:36:50.610 --> 00:36:52.094
There were no best practices.

00:36:52.094 --> 00:37:02.945
Laboratory infrastructure was set up to receive this information but not to deliver it back to patients because it wasn't set up that way, it was only for our purposes as a public health agency.

00:37:03.045 --> 00:37:07.300
But you're right, yeah, it was not set up to basically deliver that to patients.

00:37:07.481 --> 00:37:27.025
Yes, and we have different identifications with pathogens happening at multiple different levels of the public health infrastructure as people interact with it, which is capacitated by new uses of technology that then acquire what we can think of as like a public life, right In the media ecosystem around the pathogen.

00:37:27.351 --> 00:37:44.391
So I vividly remember that time early in the pandemic or like when things were sequenced, and I think often what was discussed in the media were like the bad sides of phylogenetics, right Like where like a new variant was discovered in South Africa and it was claimed as the South African variant and flights were banned from South Africa.

00:37:44.632 --> 00:37:45.271
That was Omicron.

00:37:45.271 --> 00:37:46.413
Yes, Omicron.

00:37:46.572 --> 00:37:57.900
So do you think there's also a positive side to ramping up the sequencing capabilities, and so you were really into this or you're still into this?

00:37:57.900 --> 00:38:02.164
What were the benefits of uploading all of the data you think?

00:38:02.384 --> 00:38:03.005
Yeah, totally.

00:38:03.005 --> 00:38:05.110
These are conversations, right?

00:38:05.110 --> 00:38:06.835
We have in STS all the time, right.

00:38:06.835 --> 00:38:07.505
So we are.

00:38:07.666 --> 00:38:14.052
You want to be critical of technology, but also accounting for more than just in the full sense of critique, right?

00:38:14.052 --> 00:38:38.130
Not in the sense of just pointing out the bad, but in really understanding something in its full, rich complexity, by generating empirical accounts of how technologies are actually used, how they produce certain kinds of users, and then how they generate representations of the organism or the problem that they are intended to generate, and then how those acquire a material life, right?

00:38:38.130 --> 00:38:40.134
So we're always trying to do this in STS.

00:38:40.134 --> 00:38:57.641
And there's a phrase in technology studies I always forget the actual person who said it, but it's a truism in critical studies of technology, or something that you say in your STS-101 lectures which is that technology is neither good nor bad, nor is it neutral, right?

00:38:57.804 --> 00:39:04.577
Technology, whether it's doing good things or bad things in the world, is dependent on who's using them and why and in what contexts.

00:39:04.577 --> 00:39:24.253
And, for example, in the HIV context, phylogenetics were central in validating the undetectable equals untransmissible paradigm, because in the partner and opposites attract studies which were the name of the studies that showed this there were people who became HIV positive during those studies.

00:39:24.253 --> 00:39:31.248
These were studies designed to tell if HIV viral suppression actually was effective at preventing transmission.

00:39:31.248 --> 00:39:34.175
Several people in the studies did seroconvert.

00:39:34.175 --> 00:39:41.047
However, through phylogenetic analysis they were able to determine that it did not come from their primary partner.

00:39:41.307 --> 00:39:45.949
And so because the strains were dissimilar from each other that had gotten it from another partner.

00:39:45.949 --> 00:39:49.184
So this is an example of a positive use of phylogenetics.

00:39:49.184 --> 00:40:00.996
Like I said in regard to SARS-CoV-2, the reactions to the discovery of the Omicron variant in Botswana and South Africa closing of the borders, stopping of the flights is absolutely unconscionable right.

00:40:00.996 --> 00:40:10.534
However, tracking the emergence and spread of variants globally was very important to pandemic management and then also reformulation of vaccine.

00:40:10.534 --> 00:40:24.826
So there was a lot of media discourse around the first major reformulation of the vaccine, which was called the bivalent vaccine, and phylogenetics help and have helped for a long time, for example in flu help.

00:40:24.826 --> 00:40:37.010
Public health agencies and, like the industry partners that they work with, determine what kind of vaccine reformulations or formulas will be important to roll out that year for flu and now in the case of like SARS-CoV-2.

00:40:37.010 --> 00:40:43.275
So like it's not good, nor is it bad, I think that we have to pay attention to specific uses of the technology.

00:40:43.275 --> 00:40:43.898
Dennis.

00:40:44.340 --> 00:40:49.016
So I guess my follow-up question to that is is then, can we use it better, like for good?

00:40:49.016 --> 00:40:57.351
And so, in particular context, my question is really about like HIV surveillance, and I'm someone who works in a lab where we study HIV.

00:40:57.351 --> 00:41:12.498
We study the neuro effects of HIV, which can be myriad and really deleterious for people, and so in I don't know that a lot of people know this outside of the HIV research field, but basically the recommendation is that everyone get HIV tested like at least once in their life.

00:41:12.498 --> 00:41:21.199
Certainly, if you're in what's considered like a high risk group and that varies depending on like where you are geographically on what high risk is they recommend you get tested more often?

00:41:21.804 --> 00:41:31.940
What are your thoughts on including something like HIV testing as part of what's a normal like CBC panel as a way to help people just be informed of their health and a CBC panel?

00:41:31.940 --> 00:41:39.876
Christina can probably explain it better, but basically, if you're getting blood work done at like your annual visit or for anything, you go to the ER or something, you're getting blood work.

00:41:39.876 --> 00:41:40.784
That's what they're running.

00:41:40.784 --> 00:41:43.929
They're checking all your values just to make sure everything's looking normal.

00:41:43.929 --> 00:41:46.335
What are your thoughts on like including something like that?

00:41:46.335 --> 00:41:54.797
So it's not something you have to opt into, because I think there's a stigma to opting into it, right and so if it's just run as, like this is normal, let's just do this.

00:41:54.797 --> 00:41:57.989
Does that help destigmatize it or does that cause more problems, or is it both?

00:41:58.692 --> 00:41:59.673
This is a great question.

00:41:59.673 --> 00:42:02.570
I'm going to take it in two parts, so the first on criminalization.

00:42:02.570 --> 00:42:13.697
I hate to be the bearer of bad news, although it's important to raise awareness about this, but HIV criminalization is still a very live issue in the United States, and it varies a lot state by state.

00:42:13.697 --> 00:42:25.655
There are some states that have laws on the books that have very outdated information, such as that spit and bodily fluids that do not transmit HIV can cause HIV transmission.

00:42:25.655 --> 00:42:40.121
There are some states that HIV transmission isn't criminalized, but even allegations of, for example, non-disclosure of one's HIV status, even if it was a very low risk event, for example if condoms were used people have been criminalized.

00:42:40.121 --> 00:42:57.509
Based on this, I really recommend Trevor Hoppe's book Punishing Disease, hiv and the Criminalization of Sickness, and also Alexander McClellan's new book Criminalized Lives, which is about the experiences of people who've experienced HIV criminalization in Canada and so that.

00:42:57.889 --> 00:43:00.336
HIV criminalization is still a very live issue.

00:43:00.336 --> 00:43:10.407
Hiv decriminalization movement, hiv justice movement.

00:43:10.407 --> 00:43:19.074
That has gotten some states to reform the HIV criminalization laws to require something like malicious intent malicious intent without consent and so to make their HIV criminalization statutes very narrow.

00:43:19.074 --> 00:43:23.393
But there are other states that have very broad and sweeping HIV criminalization.

00:43:23.512 --> 00:43:24.416
I did not know that.

00:43:24.416 --> 00:43:26.565
I guess I'd only ever focused on the federal level.

00:43:26.565 --> 00:43:27.827
But that totally makes sense.

00:43:27.827 --> 00:43:29.768
That in the United States you've got to look at the state laws.

00:43:29.768 --> 00:43:32.030
Oh my gosh, that is wild to think.

00:43:32.030 --> 00:43:34.532
We are in 2025 and there's still criminalization.

00:43:34.532 --> 00:43:41.458
It was 2010 that we decriminalized giving visas to people who were coming to the United States that were HIV positive.

00:43:41.476 --> 00:43:44.260
So 2010, which is very recent remembered that it was like a.

00:43:44.260 --> 00:43:59.751
That was when the obama administration was doing a lot around hiv around that time, so that would have been right right, right also around the time of the launch of the first national hiv aid strategy in 2010, which is talk about having a non-functional health care system in this country.

00:43:59.751 --> 00:44:07.471
We did not have a national hiv aid strategy until 2010 that is insane yeah, that's been almost 30 years into the epidemic right.

00:44:07.871 --> 00:44:11.697
Wow, when we talk about criminalization of HIV.

00:44:11.697 --> 00:44:13.699
I personally don't have a lot of knowledge on this.

00:44:13.699 --> 00:44:15.248
Are we literally talking about?

00:44:15.248 --> 00:44:27.088
You can be like put in the penitentiary for things that have to do with HIV and if you are someone who's HIV positive, yes, and so this operates at a couple of different levels there are.

00:44:27.248 --> 00:44:38.541
One level is we can talk about HIV and criminalization being like populations and groups of people who tend to be criminalized tend to be more associated with HIV.

00:44:38.965 --> 00:44:49.577
So sex workers, transgender women, gay and bisexual men, people who use substances these are people who tend to be criminalized in other ways and who also tend to have higher rates of HIV.

00:44:49.577 --> 00:44:50.280
So there's that.

00:44:50.280 --> 00:44:59.025
But then there are actual specific HIV criminalization statutes, and so the Williams Institute at UCLA has really good reports about this.

00:44:59.025 --> 00:45:07.235
For example, I remember this report came out when I was doing my fieldwork in Atlanta about HIV criminalization in Georgia.

00:45:07.235 --> 00:45:32.378
It's like outside of the Atlanta metro area, 10% of people living with HIV have experienced some kind of criminalization directly related to their HIV, and so this doesn't mean that they had to have transmitted HIV to someone, but it could, for example, be that they spat on a police officer when they were being arrested and it like got added on as something like resisting arrest.

00:45:32.398 --> 00:45:35.795
Another charge, and then that would be added to their charge as a kind of enhancement.

00:45:35.795 --> 00:45:55.199
And then there is HIV criminalization, where there is alleged nondisclosure or transmission of HIV, and again this varies a lot from state to state and country to country and the HIV Justice Network is the organization that kind of globally tracks and drives conversations around HIV criminalization reform.

00:45:55.721 --> 00:46:10.994
Wow, yeah, and if you want to know more about that you can check out the website of the Williams Institute has some good reports there at UCLA, or, the Center for HIV Law and Policy maintains like a compendium of laws, like they have a profile of every state's HIV criminalization laws.

00:46:11.565 --> 00:46:28.400
I mean to connect this to the second part of your question and to bridge it, I would say, also to phylogenetics, this idea that you can use phylogenetic analysis, which is a different question than should HIV testing be part of routine blood panels when someone comes in for a primary care or ER visit?

00:46:28.400 --> 00:46:44.853
The fear that HIV phylogenetics could be used to infer, even with a degree of uncertainty, who may have infected whom in a transmission cluster was a real and remains a real source of fear, because HIV criminalization laws do remain on the books.

00:46:44.853 --> 00:46:47.327
Now I will give public health credit.

00:46:47.327 --> 00:46:51.570
Public health is very good at protecting and safeguarding data.

00:46:51.570 --> 00:47:02.092
This would be taken for granted for people who work in the public health space in any way, but the data that we're talking about, that are reported to public health as part of routine care for people living with HIV.

00:47:02.092 --> 00:47:09.597
This is done without consent, which is a longstanding practice in public health because these are routine reportable infectious diseases.

00:47:10.025 --> 00:47:16.317
Actually going to be my next question, but that does also apply to all reportable infectious diseases, not just HIV.

00:47:16.336 --> 00:47:20.356
Correct, so anything that is reportable is yes, it's just given.

00:47:20.664 --> 00:47:22.934
Correct, and this is connected to your question about testing.

00:47:22.934 --> 00:47:56.528
Hiv data in the United States have a very specific history where, since data about HIV were first being collected or aids before they identified the causative agent HIV beginning in the early 1980s, hiv data have been an object of political contestation by advocates and people living with HIV that have made them a special class of data in the public health system.

00:47:56.528 --> 00:48:09.577
Because initially, in the early years and still, but particularly, in the early years, having a positive HIV diagnosis was so likely to result in things like housing discrimination, job discrimination, there were no effective treatments, and so there were these fights.

00:48:09.577 --> 00:48:17.096
That happened at the federal level, but then also each kind of state has its own story about how this played out in each state.

00:48:17.096 --> 00:48:30.150
Because of the US system of federalism, states have a lot of say in terms of the actual data elements that they collect and how they do it, about whether or not HIV tests would be connected to people's first and last names, or if they would be connected to people's first and last names, or if they would be de-identified.

00:48:30.150 --> 00:48:40.813
If anonymous testing should even be possible, and then also this applied to HIV testing, should there have to be specific consent to receive an HIV test rather than what is called routine opt-out.

00:48:40.813 --> 00:48:50.005
And so, to give the big picture, these fights have happened at regular intervals in the 40 plus years of the HIV AIDS epidemic.

00:48:50.585 --> 00:49:03.773
But when effective treatments for HIV became available in 1996, and then more and more widely available after that, the ethical case for names based HIV reporting became stronger and stronger it was.

00:49:03.773 --> 00:49:21.523
Basically we know, if we can have a better epidemiological picture of who is acquiring HIV, who's living with HIV, who is out of care, we can then have better surveillance data more complete and accurate surveillance data and better serve people living with HIV.

00:49:21.523 --> 00:49:32.297
And the privacy concerns began to be positioned lower than the kind of public health benefit because people were living longer lives.

00:49:32.297 --> 00:49:40.239
There was a real, substantial benefit to getting people connected to care, and so this process of the transition to names-based reporting was completed in 2008.

00:49:40.239 --> 00:49:48.351
And that really forms the basis for all the programs that we've talked about that identify people living with HIV who've fallen out of care and bring them back into care.

00:49:48.932 --> 00:50:13.393
Part of those fights, which are again very live and differ a lot from state to state, is the question of routine opt-out HIV testing or opt-in HIV testing, which that means do you need a person or a patient's specific consent to order an HIV test for them, or can it just be part of a regular test panel, maybe with asking them hey, we're going to order this, do you want to opt out?

00:50:13.393 --> 00:50:19.717
I will say there has been a major move toward routine opt out for exactly the reasons that you're saying.

00:50:19.717 --> 00:50:28.327
Let's destigmatize HIV, let's expand testing, let's de-exceptionalize HIV and make it like other tests that we run.

00:50:28.327 --> 00:50:37.429
There's not 100% consensus on that, though, and there are still some advocacy organizations that would prefer opting in, and it varies a lot from state to state.

00:50:37.550 --> 00:50:51.576
And then also, when states implement, for example, a routine opt-out policy, oftentimes providers don't want to implement it because they're afraid of the consequences of ordering HIV tests for patients, and so this is something that you hear a lot about.

00:50:51.576 --> 00:51:04.588
Again, this makes me think about STS and how we think about infrastructures, where, for example, a health system you can think about that as an infrastructure sets a policy, puts a policy in place that they're going to implement routine opt-out HIV testing.

00:51:04.588 --> 00:51:15.728
The policy is only as good as those who are to implement it right, and so you can actually then think about what's the material life of a policy Is it implemented or not, and why?

00:51:15.728 --> 00:51:31.193
If you talk to people who are working in health systems in the US, where they're doing a lot of HIV testing, and particularly in ERs, you will probably find if they've had an initiative like this, there was some resistance to it and it's very uneven across different health systems.

00:51:31.554 --> 00:51:32.137
Interesting.

00:51:32.338 --> 00:51:48.659
So Stephen you expressed your interest in I think you called it the directionality of transmission and where you draw the line right If a transmission occurred, proven by phylogenetics, from A to B.

00:51:48.659 --> 00:51:50.565
Right Like, where do you draw the line?

00:51:50.565 --> 00:51:52.510
What is acceptable, it's not acceptable?

00:51:52.510 --> 00:52:01.454
And we spend quite a bit of time talking about HIV and how you can criminalize or you could use it in a bad way.

00:52:01.454 --> 00:52:04.025
Right, if you say this came from you or from you.

00:52:05.369 --> 00:52:10.023
So a lot of people here in the gnl work on the other end of that spectrum.

00:52:10.023 --> 00:52:13.757
Right where you want to find out the zoonotic spillover of a disease.

00:52:13.757 --> 00:52:16.244
Right, like from an animal to a human.

00:52:16.244 --> 00:52:19.813
And the directionality is like the goal.

00:52:19.813 --> 00:52:28.385
Right, like you want to find out did it come from a mink or did it come from a bat or something like that, where it's very essential.

00:52:28.385 --> 00:52:43.248
And then often we're like with our backs against the wall because it's all and something that happened in the past and we are trying to put the clues together to see what happened in the past and it's often just an inference.

00:52:43.248 --> 00:52:51.898
But I'm curious can you talk more about drawing the line right, like we have on the very strong side, the HIV transmission?

00:52:51.898 --> 00:53:00.472
You don't want to have this directionality of who transmitted to who, but then we, on the zoonotic spillover, we must find out where it came from.

00:53:00.472 --> 00:53:07.097
So when you, with your interest in transmission and the directionality of it, where would you draw the line?

00:53:07.885 --> 00:53:22.170
Yeah, I'll take your question by drawing on one of my favorite books, which is a book called the Epistemology of the Closet, by a literary theorist named Eve Sedgwick, and this is one of the foundational works in the field of queer theory, which is another field where I work.

00:53:22.170 --> 00:53:26.204
And she opens her book with several axioms or axiomatics.

00:53:26.204 --> 00:53:44.032
One of them is that people are different from each other in the same way and this is like her starting point for thinking about the diversity of human sexuality, right in that context and of human experience period, right, a very powerful, simple notion to return to all the time People are different from each other and people like different things.

00:53:44.032 --> 00:53:45.911
Right To relate this to the discussion we're having now.

00:53:45.911 --> 00:53:47.097
Pathogens are extremely different from each other.

00:53:47.097 --> 00:53:48.242
Right To relate this to the discussion we're having now.

00:53:48.242 --> 00:53:50.590
Pathogens are extremely different from each other, right.

00:53:50.590 --> 00:53:51.974
So even this term.

00:53:52.014 --> 00:54:09.956