Infectious Diseases Team Aims to Curb Neglected Parasitic Disease

Much of the work performed in Lo’s research group focuses on studying transmission of infectious diseases. Their goal is to reduce the burden of diseases by improving public health decision-making and interventions. The research team has a special interest in working on schistosomiasis with organizations like WHO to better target and improve approaches to public health control of that disease. Their work is centered on one particular challenge that is hindering progress against schistosomiasis: the inability to identify geographic hotspots of transmission.

“Hotspots are places where, despite mass administration of praziquantel, disease remains quite high, with people getting constantly reinfected,” Lo explains. His motivation for this specific work stems from his work with the WHO guidelines group on schistosomiasis and recognizing the key challenge of hotspots in global control efforts.

With better intelligence on where schistosomiasis hotspots are likely to occur, additional interventions can be brought to those high-risk communities to reduce transmission of the disease.

“In an effort to better identify those hotspots, we developed statistical models to predict the areas that have very intense transmission of schistosomiasis versus those with much lower transmission of the disease,” says Benjamin Singer, DPhil, an Infectious Diseases postdoctoral scholar and lead author of a paper describing the research. The paper was published in the January 5, 2024 issue of the Proceedings of the National Academy of Sciences (PNAS).

The team’s statistical models relied on data collected over four years from high-risk communities in five countries: Niger, Mozambique, Côte d’Ivoire, Kenya, and Tanzania. The data was collected as part of a large-scale trial.

After feeding data into their statistical models, the researchers found that their predictions of hotspots were moderately accurate in identifying actual hotspots, depending on which of three definitions of hotspot was used. One definition was based solely on prevalence; that is, simply how many people are infected. A second definition was based on how many people are infected with a high number of parasites, and a third definition is based on whether prevalence is improving over time. The statistical model using the prevalence hotspot definition was most accurate, so it will be useful in future work.

“The research shows that the models can predict some hotspots, but there are definitely limitations. It also highlights key opportunities for additional data that would be helpful for these sorts of models, so we expect to improve these models as we move forward,” Lo says.

Looking ahead, the team will be collecting data to improve these models and then testing them in the countries where schistosomiasis is endemic to see how the models perform.

Singer points out that “schistosomiasis is a neglected tropical disease that affects a lot of people, especially some very vulnerable people in the world.”

He was inspired to join Lo’s team because “there are just insufficient resources dedicated to helping reduce its prevalence. I was excited to get to work on a project that could help make a contribution towards combatting a disease that causes a lot of pain and misery and doesn't need to because we have good treatments for it.”

For his part, Lo notes that “schistosomiasis is near the very top of the list of worst diseases caused by neglected parasitic diseases, which is one of the reasons that we started working on it in the first place.”

“From my time working with WHO, I realized that identifying hotspots is a key barrier to reducing the disease burden globally. It's a challenging problem, but it's important to work on because this is where we'll make the most progress.”