So how do you track spread of disease? By the numbers

Ivan Specht started at Harvard on track to study pure mathematics. But when COVID-19 sent everyone home, he began wishing the math he was doing had more relevance to what was happening in the world.

Specht, a New York City native, expanded his coursework, arming himself with statistical modeling classes, and began to “fiddle around” with simulating ways diseases spread through populations. He got hooked. During the pandemic, he became one of only two undergraduates to serve on Harvard’s testing and tracing committee, eventually developing a prototype contact-tracing app called CrimsonShield.

Specht took his curiosity for understanding disease propagation to the lab of computational geneticist Pardis Sabeti, professor in Organismic and Evolutionary Biology at Harvard and member of the Broad Institute, known for her work sequencing the Ebola virus in 2014. Specht, now a senior, has since co-authored several studies around new statistical methods for analyzing the spread of infectious diseases, with plans to continue that work in graduate school.

“Ivan is absolutely brilliant and a joy to work with, and his research accomplishments already as an undergraduate are simply astounding,” Sabeti said. “He is operating at the level of a seasoned postdoc.”

His senior thesis, “Reconstructing Viral Epidemics: A Random Tree Approach,” described a statistical model aimed at tackling one of the most intractable problems that plague infectious disease researchers: determining who transmitted a given pathogen to whom during a viral outbreak. Specht was co-advised by computer science Professor Michael Mitzenmacher, who guided the statistical and computational sections of his thesis, particularly in deriving genomic frequencies within a host using probabilistic methods.

Specht said the pandemic made clear that testing technology could provide valuable information about who got sick, and even what genetic variant of a pathogen made them sick. But mapping paths of transmission was much more challenging because that process was completely invisible. Such information, however, could provide crucial new details into how and where transmission occurred and be used to test things such as vaccine efficacy or the effects of closing schools. 

Specht’s work exploited the fact that viruses leave clues about their transmission path in their phylogenetic trees, or lines of evolutionary descent from a common ancestor. “It turns out that genome sequences of viruses provide key insight into that underlying network,” said the joint mathematics and statistics concentrator.

Uncovering this transmission network goes to the heart of how single-stranded RNA pathogens survive: Once they infect their host, they mutate, producing variants that are marked by slightly different genetic barcodes. Specht’s statistical model determines how the virus spreads by tracking the frequencies of different viral variants observed within a host.  

As the centerpiece of his thesis, he reconstructed a dataset of about 45,000 SARS-CoV-2 genomes across Massachusetts, providing insights into how outbreaks unfolded across the state.

Specht will take his passion for epidemiological modeling to graduate school at Stanford University, with an eye toward helping both researchers and communities understand and respond to public health crises.

A graphic designer with experience in scientific data visualization, Specht is focused not only on understanding outbreaks, but also creating clear illustrations of them. For example, his thesis contains a creative visual representation of those 45,000 Massachusetts genomes, with colored dots representing cases, positioned nearby other “dots” they are likely to have infected.

Specht’s interest in graphics began in middle school when, as an enthusiast of trains and mass transit, he started designing imagined subway maps for cities that lack actual subways, like Austin, Texas. At Harvard, he designed an interactive “subway map” depicting a viral outbreak.

As a member of the Sabeti lab, Specht taught an infectious disease modeling course to master’s and Ph.D. students at University of Sierra Leone last summer. His outbreak analysis tool is also now being used in an ongoing study of Lassa fever in that region. And he co-authored two chapters of a textbook on outbreak science in collaboration with the Moore Foundation.

Over the past three years, Specht has been lead author of a paper in Scientific Reports and another in Cell Patterns, and co-author on two others, including a cover story in Cell. His first lead-author paper, “The case for altruism in institutional diagnostic testing,” showed that organizations like Harvard should allocate COVID-19 testing capacity to their surrounding communities, rather than monopolize it for themselves. That work was featured in The New York Times.

During his time at Harvard, Specht lived in Quincy House and was design editor of the Harvard Advocate, the University’s undergraduate literary magazine. In his free time he also composes music, and he still considers himself a mass transit enthusiast.

In the acknowledgements section of his thesis, he credited Sabeti with opening his eyes to the “many fascinating problems at the intersection of math, statistics, and computational biology.”

“I could fill this entire thesis with reasons I am grateful for Professor Sabeti, but I think they can be summarized by the sense of wonder and inspiration I feel every time I set foot in her lab.”