Cracking the Code of Epidemics
By Sarah Colwell
Isabel Rodriguez-Barraquer, M.D., Ph.D. Associate Professor Medicine UC San Francisco
COMPUTATIONAL HEALTH
During the past decade, the world has been caught in a relentless cycle of viral outbreaks—Zika, dengue, chikungunya, and, of course, COVID-19. As global temperatures rise and human mobility increases, vector-borne diseases are emerging in new regions, challenging public health systems in ways never seen before. Dengue, for example, which was once considered a tropical disease, is now creeping into parts of the U.S. where it was previously unknown. In 2023 alone, the Americas saw a record-breaking 4.5 million dengue cases, a chilling reminder that infectious disease outbreaks are not relics of the past, but a pressing modern threat. Statistics such as these are what motivate the work of Isabel Rodríguez-Barraquer, M.D., Ph.D., and Nicola Müller, Ph.D., professors at the EPPIcenter at UC San Francisco. Specializing in computational health, Rodríguez-Barraquer and Müller are pioneering methods that integrate multiple data sources—ranging from pathogen genomes and wastewater samples to seroprevalence surveys—to track outbreaks with unparalleled precision. It is work that is reshaping how we understand and combat disease outbreaks. “My hope is that our approach could one day revolutionize epidemic forecasting, thereby helping public health officials get ahead of the next outbreak and, hopefully,” Rodríguez-Barraquer said, “save thousands if not millions of lives throughout the world. A Neighborhood Approach to Outbreak Detection Traditionally, disease surveillance has relied on isolated data streams: lab-confirmed cases, hospital records, or genomic sequencing. But each of these alone provides only a fragmented glimpse into an outbreak’s true spread. Dr. Rodríguez-Barraquer’s research takes a different approach—combining multiple datasets to paint a more complete picture of disease dynamics at the neighborhood level. Her work is powered by a multi-compartment simulation framework, which allows scientists to recreate how infections spread over time and space. This framework combines transmission histories, wastewater data, and seroprevalence patterns, to enable a more accurate reconstruction of outbreaks than any single data source could achieve alone.
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