Pathogen X is a term coined by the WHO for a hypothetical, unknown pathogen that could cause the next pandemic. It is an acknowledgment that pandemic threats may come from diseases we have not yet identified. But how do you build a surveillance system for something you cannot name?
That is the starting point for Blind Spots in Pandemic Preparedness: Pathogen X and the Development of Epidemic Surveillance after Covid, a new project funded by the Swedish Research Council. I will do it together with Lisa Lindén at Chalmers.
New systems, old assumptions?
In the wake of Covid-19 there has been a major push to build new surveillance systems for pandemic preparedness. Sewage is analyzed to track outbreaks. Bioinformatics is used to identify antibiotic resistance genes. Metagenomic techniques are developed to find unknown pathogens.
These systems promise better preparedness. But studies of past pandemics point to a risk: that new systems reinforce existing biases in what data is collected, which pathogens are detected, and whose health counts. So the project asks:
- Which pathogens are prioritized in emerging surveillance systems, and how does this shape the detection of unknown threats—Pathogen X?
- Which people and places are represented in the systems, and which are left out?
- What do these exclusions mean for pandemic preparedness, for data bias, pathogen detection and health equity?
Where do pandemics come from?
Our preliminary interviews already point to the problem. Informants had strong ideas about where pandemic pathogens would emerge, focusing on China and Africa. Pathogen X, however, might just as well come from thawing “zombie viruses” in the permafrost, or from our own wastewater systems.
This matters because surveillance systems are built on such imaginaries. A focus on particular “virulent zones”—bats or migratory birds in China, for instance—risks erasing the complexity of how diseases emerge.
Ontological overflows
To analyze this, the project builds on a concept I have been developing: ontological overflows. The point is that certain pathogens, populations and places are not just overlooked by surveillance systems. They are excluded—they become overflows in the frame of the system—through its practices, infrastructures and assumptions.
Rather than treating exclusions as accidental, the concept draws attention to how they are assembled: through data scarcity, modeling, risk computation and system design. I first worked this out in a paper on Zika, disease surveillance and mosquitoes, and it builds on earlier work on detecting the unknown in a sea of knowns.
Following the work
Lisa and I will do ethnographic fieldwork at three laboratories and at the Public Health Agency, following three systems: wastewater epidemiology, antibiotic resistance tracking and novel pathogen identification. They correspond to three of the biggest pandemic threats—viruses, bacteria and Pathogen X.
Many of these systems are still being built. That gives us a rare chance to see how assumptions about future pandemics are written into their design, rather than studying systems that are already in place. And, hopefully, to help build systems with fewer blind spots.
More on the projects page.