How Statistical Models Are Advancing the Fight Against Neglected Tropical Diseases in Migrant Populations

A recent study focusing on migrants in Italy demonstrates the significant role that statistical modeling can play in identifying and managing neglected tropical diseases (NTDs). NTDs comprise a group of 21 diseases that primarily burden impoverished communities in tropical regions. However, with increasing global migration, individuals infected with NTDs are arriving in countries where these diseases are not endemic, requiring improved detection methods.

Led by Ph.D. student Jana Purkiss and Dr. Emanuele Giorgi from Lancaster Medical School, in partnership with the University of Naples Federico II and the WHO Collaborating Center for the Diagnosis of Intestinal Helminths and Protozoa, the research was published in PLOS Neglected Tropical Diseases. The study centered on soil-transmitted helminth (STH) infections, which include diseases caused by roundworms such as Ascaris lumbricoides, hookworms, and Trichuris trichiura.

Utilizing data from 3,830 migrants from 64 countries—most of whom were male with a median age of 27—the researchers explored how combining publicly available country data with individual screening results could enhance infection detection. By developing predictive models, they assessed how well various factors could identify infected individuals, especially considering migrants from both known and new regions.

The findings revealed that models integrating both individual screening data and country-level indicators outperformed those relying solely on individual information. Notably, country of origin was a key predictor, with Guinea Bissau (25% prevalence) and Bangladesh (18.6%) serving as high-risk sources in Africa and Southeast Asia, respectively.

According to Purkiss, this approach underscores the potential of data-driven strategies to support targeted screening efforts, thereby easing the workload of specialized laboratories. Such models can facilitate earlier detection and treatment, ultimately reducing the burden of NTDs among migrant populations.

This innovative work has been well received, highlighting the advantages of combining national indicators with individual data to improve disease prediction. Future collaborations aim to refine these models further, enhancing their accuracy and utility in global health initiatives.

For more information, see the full study: Jana Purkiss et al, Combining country indicators and individual variables to predict soil-transmitted helminth infections among migrant populations: A case study from southern Italy, PLOS Neglected Tropical Diseases (2025).