Detecting infectious disease threats early and responding quickly can dramatically alter the course of an infectious outbreak. Technologies such as wastewater surveillance are transforming how public health officials identify emerging pathogens, monitor community transmission and direct resources where they're needed most. Scripps Research has been at the forefront of this effort, developing computational and laboratory tools that have helped make wastewater disease tracking a reality for global health.
Now, with two new grants from the Gates Foundation totaling $2 million, Scripps Research scientists will further expand these capabilities-developing more comprehensive wastewater surveillance technologies while building artificial intelligence systems to integrate diverse data and improve outbreak prediction. Both awards are part of the Modjadji Initiative, an effort to build affordable, scalable pathogen surveillance systems, particularly for low- and middle-income countries (LMICs).
We are very grateful to the Gates Foundation because these grants will allow us not only to expand the wastewater tools we've been developing, but also to use artificial intelligence to improve global health decision-making more broadly. By working closely with our partners around the world, we're creating systems that can help detect outbreaks earlier and support faster, more targeted responses."
Josh Levy, senior project scientist at Scripps Research and leader on both projects
One award renews a 2023 Gates Foundation grant, led by Scripps Research professor Kristian Andersen and Levy in collaboration with the National Institute for Communicable Diseases (NICD) in South Africa and the University of Birmingham in the United Kingdom.
The researchers will expand the computational and laboratory methods used for wastewater surveillance, enabling monitoring of a broader range of pathogens while reducing gaps that currently limit infectious disease tracking in many parts of the world, particularly in low-resource settings. The team will also adapt these tools for wastewater sources beyond traditional sewer systems, including for monitoring streams, canals and other surface waters that may be impacted by human wastewater.
Central to the project is Freyja, an open-source wastewater analysis platform developed by the Andersen lab that became a widely adopted tool during the COVID-19 pandemic. With the renewed award, Levy and colleagues will expand Freyja to detect additional infectious disease threats while optimizing both laboratory protocols and bioinformatics tools for use in LMICs.
"Our goal is for the laboratory protocols, computational tools and resulting data to remain openly available so they can be used by researchers and public health agencies around the world," Levy says.
The second grant, led by Levy and institute investigator Karthik Gangavarapu, moves beyond disease detection to outbreak prediction. Researchers will develop AI and machine learning approaches that integrate multiple data types (testing and sequencing) as well as modalities (wastewater and clinical) into a unified picture of disease transmission. By combining these complementary data sources, the models will help fill surveillance holes and guide public health officials toward the most effective interventions.
The project will initially focus on South Africa and Zambia, where Scripps Research will partner with the Zambian National Public Health Institute (ZNPHI) alongside other collaborators that are part of the Modjadji Initiative.
Beyond strengthening preparedness for future pandemics, the work will expand monitoring for diseases such as measles and tuberculosis. In Zambia specifically, the integrated platform will help identify cholera transmission hotspots to better target clean water interventions and vaccination campaigns, while also improving understanding of community transmission risk for mpox.
"Bringing these different sources of information together is essential for responding to emerging threats, and to prepare for the threats we haven't seen yet," says Levy. "Every community has different risks and different health needs. By integrating these diverse data streams, we can build robust and scalable public health surveillance that provides more accurate and actionable data for decision-makers."
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