Introducing: “Before the Outbreak” — A Three-Part Series on Disease Surveillance and Pandemic Preparedness
Robust disease surveillance systems are the foundation of strong public health systems and are essential to preventing, detecting, and responding to health threats before they escalate. Commitments to and investments in quality disease surveillance systems are key to smart, cost-effective public health decision-making, which is needed more than ever.
Before the Outbreak is a three-part podcast series produced in partnership between Global Dispatches and the United Nations Foundation, in which we explore how the world sees, anticipates, and prepares for current and emerging health threats. Through stories and science, this series highlights the critical functions that protect us – before the outbreak begins.
Our debut episode features the expertise of Dr. Ciro Ugarte, Director of Health Emergencies at the Pan American Health Organization, and Dr. Pardis Sabeti, a professor at Harvard University’s School of Public Health. We begin by defining our terms—that is, what do we mean by disease surveillance? We then discuss how disease surveillance works in practice and what can be done to strengthen our global defenses against the next pandemic.
The episode is freely available across all podcast listening platforms, including Spotify and Apple Podcasts. You can also listen directly below.
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Transcript edited for clarity
Dr. Pardis Sabeti: Because of the exponential spread of outbreaks, one person can launch a pandemic, and therefore one person can stop it. An outbreak can emerge anywhere, and you really do need to make sure that every person is as empowered as possible to respond.
Mark Leon Goldberg: Welcome to Global Dispatches, a podcast for the foreign policy and global development communities and anyone who wants a deeper understanding of what is driving events in the world today. I’m your host, Mark Leon Goldberg. I am a veteran international affairs journalist and the Editor of UN Dispatch.
Enjoy the show.
Today’s episode is the debut of a three-part series called Before the Outbreak, which examines the role of disease surveillance in stopping the next pandemic. Produced in partnership with the United Nations Foundation, this series explores how the world detects, anticipates, and prepares for current and emerging health threats. Through stories and science, it highlights the critical functions that protect us before an outbreak begins.
In today’s episode, I speak with Dr. Ciro Ugarte, Director of Health Emergencies at the Pan American Health Organization, and Dr. Pardis Sabeti, a professor at Harvard University’s School of Public Health. We begin by defining our terms, that is, what do we mean by disease surveillance? We then discuss how disease surveillance works in practice and what can be done to strengthen our global defenses against the next pandemic.
And one quick note before we begin — In this episode and throughout the series, the views and opinions expressed are those of the guests and hosts and do not necessarily reflect the views of the podcast partners.
And now here is Dr. Ciro Ugarte and Dr. Pardis Sabeti.
Thank you both for being with me today. So, this is the debut episode of a three-part series that examines the role of disease surveillance in stopping the next outbreak. I wanted to kick off by having us define our terms a bit. What do we mean by disease surveillance? Pardis, why don’t you go first?
Dr. Pardis Sabeti: When I think of disease surveillance, well, in this context, we’re talking about infectious diseases, and we’re talking about the microbes and the different organisms, what we call pathogens that cause infectious diseases and cause pandemics. We also include in that potential bioweapons, man-made weapons. But it’s basically the things that are moving around that can cause disease that can move from person to person. And how do we find those things and then track those things and respond to them?
Dr. Ciro Ugarte: Just to complement Pardis, in terms of the systematic collection, analysis, and interpretation of health data. So, to monitor the disease occurrence, but also the trends within populations. That’s pretty much...the importance, but also what is done.
Mark Leon Goldberg: And Ciro, in terms of like systematically collecting that kind of information, like what’s the information we’re talking about? Where does it come from, and what are you looking for?
Dr. Ciro Ugarte: The information is what is the sources that we use. First is the sources of the ministries of health, the national institutes of health, but also the health systems and networks that are happening in each country. At the regional level, each country provides also the information. And at the global level, it’s also through several other institutions — civilian, military, collaborating centers, NGOs, and others. Essentially, that information that’s related with the disease and the trends, and so on, it’s useful for us to identify early outbreaks, but also to identify the trends of the diseases and also how we can intervene to reduce the health impact in those populations.
Mark Leon Goldberg: And you’re talking, what? About information and data, say, from like national reference labs that might like read a sample and identify a pathogen?
Dr. Ciro Ugarte: Yes, but also from the health services, small health services, large health services, municipalities, and so on. Yes, also, of course, at the highest level of the National Institutes of Health, for example, for the genomic surveillance systems, and so on. But in this case, we’re talking about from the ground to the top level.
Dr. Pardis Sabeti: I think Ciro explains it very well. And I think one of the things that he’s pointing to is just that there’s so many different data sets at every level that are coming in. And I think that is where this work matters a lot. It’s from somebody taking a diagnostic test at home to somebody, you know, being seen in the ER and have a big workup happening to municipalities doing wastewater testing. All of this data, there’s so many different disparate data sets being collected on different platforms by different groups of people. And I think when we think about surveillance, it’s about how do we combine all of that data? How do we bring all the data streams in together?
Because right now it’s very siloed. It’s very bespoke. And within those massive blind spots that’s created, that’s where these infectious diseases get a head start. And we really need to figure out how to integrate all of this. And then beyond those data streams that we’re talking about within clinical networks, it’s just individual actors and the symptoms they’re having that they might be able to share with us on their iPhones or other kinds of environmental data, weather, flights, all of those types of information. How do we use all the data streams to have the most real-time information, and how to stop an outbreak from unfolding?
Mark Leon Goldberg: So, Pardis, I’m wondering if you could maybe make this like a little real for us and give an example or two of instances in which a disease surveillance system worked as it ought to have and potentially stopped an outbreak before it metastasized.
Dr. Pardis Sabeti: The last couple of decades now, I’ve been working with my colleague Christian Happi in Nigeria, and together, with a number of partners in West African countries, we’ve created something called Sentinel, which is this type of surveillance system. We talk about three pillars that we have with it. Detect, you know, find ways of detecting pathogens wherever they occur, connect that information in real time, and then empower every actor in the system. And within our own efforts, we work very, very closely. It’s really important that we work closely with the local partners, the local clinicians, and the Nigeria CDC, the Africa CDC. They’re always informed.
We’re working on their behalf, like we’re partners to them, and we recognize that, you know, we are trying to serve them. But together, we have had opportunities where, you know, there was an outbreak unfolding in Nigeria. People didn’t understand what it was. A number of children had gotten sick. We quickly were able to work with the Nigeria CDC, get access to those samples, sequence it, identify it being yellow fever, and then be able to think about, okay, we understand a little bit about how yellow fever transmits, understanding the at-risk populations, making sure vaccinations happen for those who were at risk. So, we had the right information to help public health responders respond in the right and appropriate way. And we’ve seen this time and again.
In Rwanda, we worked with the Ministry of Health there and helped sequence and analyze an unfolding Marburg outbreak. So, it’s that ability to just identify the cases at the first setting and particularly note when there’s clusters that are unusual and bring the right attention, the right technologies to figure out what’s going on and how best to address it.
Mark Leon Goldberg: How does disease surveillance work the same or different, say, in the Americas? And are there examples you could share of how it’s worked successfully to change the course of an outbreak?
Dr. Ciro Ugarte: There are several examples. Actually, the broader surveillance that we do is essentially to prevent those large outbreaks and prevent epidemics and pandemics. And that is through the application of the international health regulations that also provide certain specific areas, times, and problems and situations that must be reported. So, through that, in the Americas, we have currently several health emergencies going on. We have yellow fever, we have measles, we have chikungunya, and also we have cholera and respiratory infectious diseases and others.
And how it works, it works essentially through the network of the information that is being provided by the national authorities. They collect information from the subnational and local levels. They analyze the information, they produce a report, and that report is shared. And those reports provide regional information that also goes through the epidemiologic outbreak updates or epidemiologic alerts. So, with that, all the countries in the Americas, but in the world, also are aware that something is going on.
And also, together with that information, also recommendations of which are the measures that must be taken to early identify those type of cases in the countries, but also how we can implement the control measures and reduce the possibility of becoming a large outbreak or the impacts in the countries. In Haiti, there was a surveillance system that gathered information from the small health villages, but also to analyze what is the quality of water. When those community workers reported that the quality control of the water was showing that it was bad, you know, there was not enough chlorine, for example, immediately the health departments in Haiti deployed their people, their staff, and they were able to control cholera in Haiti for several years already.
So, there are a low number of cases in Haiti, including, of course, as you know very well, all the security issues and all the fragility of the health systems. But through surveillance, we are able to have cholera under control. Not control, but under control.
Mark Leon Goldberg: So, Pardis, as you and Ciro are describing it, I mean, it sounds to me like a well-functioning surveillance system is one in which you have these disparate networks that are collecting different kinds of information and reporting it through the chain, all somehow communicating and interacting with each other in ways that can inform policy decisions.
Dr. Pardis Sabeti: I think that is the goal. And the question is, how do you get there? And I think you get there by making sure that at the end, you’re serving the local, the frontline. You can’t ask the frontline to share that kind of data all the way up to the public health chain. So, someone, a big decision maker somewhere in the capital city can make a decision if the local responders don’t have what they need. And so, for us, as we think about it, we think about how do we provide the frontline with the information that they need that incentivizes them to give us real time information into the system?
So, you have tools for them immediately where, for example, my own lab has spent a lot of our time, first, trying to democratize genomic sequencing and diagnostics by running a lot of courses to teach more people to do it, bringing sequencers to our partners in Africa, training them to do sequencing. So that was like our first big thing was just like, let’s democratize getting the diagnostics and the sequencing of the data out there. And then when we recognize the next issue is now we need to democratize analytics. You know, you can’t ask them to generate data, but not allow them to get the insights from them.
So, we’ve really been focusing on doing education in bioinformatics and beyond, but also building tools. So, we have a new tool that we’re really excited about. There’s something called Bayesian phylogenetics. It’s a workforce of outbreak response. It’s the way you could get a sense of how quickly the virus is, when it emerged, how it’s spreading. But that was something that was difficult to do, and only a few labs could do it. So, we made this tool so that it runs on a laptop within seconds. Anyone can run it.
So, we want to make sure that those frontline responders actually can generate the data and then get the insights of the data. And then that incentivizes them to make it available to others and share what they’re learning. So, yes, it’s exactly how you describe what you’re trying to get to. And now it’s the thinking of how to get there. And it really is making sure that you are incentivizing and supporting every actor in the system.
Mark Leon Goldberg: And Ciro, in terms of that question, how to get there, do surveillance systems all essentially look the same or are there meaningful differences between them? And what would those differences be?
Dr. Ciro Ugarte: There are different systems of surveillance. And also the quality of those types of surveillance are different in the countries and also at the subnational levels. For example, we’re talking about active surveillance that involves actively seeking out of cases of a disease, for example, where the health care workers go to the houses of the population or the communities or actively look at the services particularly. So, to look something that is going on. And it usually is used for stopping an outbreak from going beyond. There is another type that is the passive surveillance. In general, it’s on report of cases in a public health, more or less looking at more cost-effective surveillance, but more or less moving to trends also.
There are other types of surveillance that are called Sentinel surveillance that involves monitoring specific populations on specific diseases because there is not enough capacity to look beyond that. But in particular, it’s also highly particular in particular diseases, and it will provide high quality data on particular diseases. There is also general other types of surveillance like syndromic surveillance that looks at several symptoms other than the disease itself.
So, by tracking symptoms and health indicators, it also provides a general information that what’s going on. If we see large trends, we go and identify why those patients are suffering from those symptoms that are similar in certain areas. But everything that I’m talking about, also Pardis is talking about, is based on trust and also on the capacity of the health systems to identify those trends, those symptoms, those diseases. And in order to do that, we must strengthen that capacity. And also, we’ll have the countries sharing that information in a timely fashion, because if we do not have that trust among the partners, among the networks, it may happen that some countries may have several cases of certain disease – i t is not shared – and it’s identified in other countries when people from one country travel to other places. So, it also happens. But I think we are moving now in the world in a more cohesive way to control diseases together, like one big country that would be the world.
Mark Leon Goldberg: Well, that actually leads me really nicely into my next question, because I’m curious to learn if the need for disease surveillance systems is something that’s evenly distributed around the world, or if there are certain geographies or maybe types of populations in which disease surveillance would be more needed than others.
Dr. Pardis Sabeti: So, there’s certain things like principles about outbreaks that are kind of unusual and interesting. Like, I often say this idea that, you know, because of the exponential spread of outbreaks, one person can launch a pandemic and therefore one person can stop it. An outbreak can emerge anywhere. And you really do need to make sure that every person is as empowered as possible to respond. And so, fundamentally, we really believe in getting outbreak response tools to everyone. But what is interesting is actually the communities that are the most vulnerable communities, individuals who are refugees or migrants or homeless, or individuals that have autoimmune disease or are immune compromised. They are also at greater risk of getting an infection and therefore spreading an infection, starting a pandemic.
So, I’d say we both need to think equitably because a pandemic can start anywhere and any person can launch it and any person can stop it. But also that it does disproportionately affect the most vulnerable communities. And so, we do actually want those tools and resources in under-resourced settings, in under-resourced populations.
Mark Leon Goldberg: Well, Pardis, understanding, though, that resources are limited, what sort of financial or even like political investments can the international community make to ensure that the kind of robust style of disease surveillance that you discussed earlier can achieve its intended goals?
Dr. Pardis Sabeti: What’s interesting is that outbreaks are one of the most devastating threats to humans, not because of just the untold cost of human lives and human suffering it causes, but also the economic cost. That the Ebola outbreak cost, you know, many billions of dollars and the COVID pandemic led to trillions of dollars of damage. So, you would think that there should be an easy case to be made that if we have major outbreaks like this, every decade at least, but often just every year, every few years, that we should just be investing in better systems.
But for whatever reason, I think it’s the same reason why people don’t appreciate infrastructure until it breaks, and people don’t appreciate infectious diseases until they cause an outbreak. It’s just hard to get people to pay for safety, and for the lack of something happening. We try to get people to understand the need for that type of sustainable support. But what we also do from our side being more practical is we just have been doing a lot of work to drive down the cost. So the things my lab is most excited about now is that you can now run that Bayesian phylogenetics on your laptop. Usually, it takes three weeks and many GPUs, making both the cost and the time, because time actually matters too.
The longer it takes you to catch an outbreak, the more devastating consequences and the more cost there is. So, we always keep talking about drive down cost, drive down time. And so same thing with the diagnostics or sequencing technologies. My lab is now investing a lot of time in driving down the cost of something called metagenomic sequencing, which is the ability to take a sample and test for any pathogen in that sample from what is now, you know, can be over a thousand dollars or at least many hundreds of dollars, now driving it to more of a 10 to $50 range so that we can start putting these into practice. So, governments, it seems like it’s going to be hard-pressed to convince them to spend as much money as it needs. But if we can keep investing in technology to drive down the cost and get it into practice, we can hopefully see more of it.
Mark Leon Goldberg: And Ciro, from your perspective, what would be some key investments, financial or political, that would enhance our disease surveillance?
Dr. Ciro Ugarte: Everything that will help the countries and the local communities and the global community to detect timely any trend that is causing a severe health problem must be invested on. It includes, of course, the diagnostic capacity, the laboratory capacity, but it also includes all the systems, the technology that must be in place according to the levels. And regarding why we need that, we need that because for the decision makers to identify which actions must be taken to save their lives, to protect the health of the people, but also to reduce the socioeconomic impact of health emergencies.
And there are several reports on how much does it cost. And it goes from $1 billion to $50 billion worldwide in the investment on the pandemic prevention systems. But of course, several countries may invest more than that in the countries themselves.
Mark Leon Goldberg: I mean, $1 billion to $50 billion is a huge range. Are there capacities or tools that you think we ought to prioritize over others?*
Dr. Ciro Ugarte: Yes, one of them is the laboratory capacity. We need the capacity of the laboratories and laboratory networks in the countries so the early detection of diseases can be made at the centralized level. But also, we need the networks and the systems that will communicate the local level to the national level. And also, we will need investments in new technologies and new medical countermeasures, and also the way to produce, for example, early vaccines or treatments or protective equipment according to those diseases. Those are the main investments that we need to have in order to prevent large outbreaks or epidemics.
Mark Leon Goldberg: And Pardis, what are some of the investments that you think ought to be prioritized in the coming months or years ahead?
Dr. Pardis Sabeti: I mean, I think Ciro said it pretty well. Like, I do agree with him that having local capacity to run testing, whatever version that is, it’s going to be incredibly important, and then having that data communicate. Because I think what you saw during COVID is we ran millions and millions of tests and that data was wasted. All the wastewater testing, it was almost like navel gazing. I said a lot of the college testing that we were doing in the United States was not stopping outbreaks. It was just observing them. And so, one of the big things I talk about is this idea of hypothesis-driven testing that we often just run tests opportunistically of who can pay for it and where they happen to be.
But really what we need to do is get smarter systems that can identify who are the people that are at risk, who are the people you might have been in contact with that might be at risk and test them. And there’s a lot we can do, particularly now with artificial intelligence, to get much, much better at mining data that exists already, to take a patient’s clinical information and predict what they have instead of running a battery of wasted tests — know exactly what to test, who to test, and what to test them for. So, I think that there are many ways of driving down the cost of all these technologies, but also using them much more intelligently, what we would call like an intelligent health system that’s a learning health system that is constantly thinking about efficiently detecting the most number of cases and anomalies when they occur.
Mark Leon Goldberg: Ciro and Pardis, thank you so much for your time. This was really helpful.
Dr. Pardis Sabeti: Thanks for having me.
Dr. Ciro Ugarte: Thank you so much.
Mark Leon Goldberg: Thanks for listening to Global Dispatches. The show is produced by me, Mark Leon Goldberg. It is edited and mixed by Levi Sharpe. If you are listening on Apple Podcasts, make sure to follow the show and enable automatic downloads to get new episodes as soon as they’re released. On Spotify, tap the bell icon to get a notification when we publish new episodes. And, of course, please visit globaldispatches.org to get on our free mailing list, get in touch with me, and access our full archive.
Thank you!
*In the audio-version, I erroneously say “$15 billion” instead of “$50 billion” in referring to Ciro Ugarte’s previous comment.