With the specter of infectious disease outbreaks looming large across the globe, early detection and rapid response are crucial for mitigating their impact. Schools offer an opportunity as prime disease surveillance hot spots — as long as strong data privacy protocols and linkages with public health authorities are in place.
Traditional methods of general population disease surveillance often rely on insufficient real-time syndromic and disease monitoring data. That is also dependent on confirmatory diagnostic testing, which is expensive and can delay early warning signs during the critical windows for intervention. But a recent syndromic monitoring analysis in Zambia suggests a promising alternative: leveraging schools as hubs for syndromic surveillance where key clinical data and disease or condition risks are assessed using a comprehensive clinical decision support, or CDS, platform.
Since 2019, a partnership between Healthy Learners and THINKMD has equipped nearly 500 Zambian schools with a clinical intelligence platform — a tool to analyze and interpret health care data and provide actionable insights as is done by medical professionals. Over 5,000 trained school health workers use this platform for clinical assessments of school children who are sick and seeking care. As a result, key clinical syndromic and disease risk information is captured for each patient encounter. This rich dataset, encompassing over 1.16 million encounters, includes key disease syndromic and risk information.
About the authors
Dr. Barry Finette
Dr. Barry Finette is a professor of pediatrics, microbiology, and molecular genetics at the University of Vermont College of Medicine and an attending and teaching physician in the Pediatric Inpatient and Critical Care Division at the University of Vermont Medical Center’s children’s hospital. He is also the co-founder and president of THINKMD, PBC and has over 35 years of experience as a pediatrician. He has participated in numerous research, medical global health and humanitarian projects, as well as capacity building and disaster relief missions in multiple countries.
Ollin D. Langle-Chimal
Ollin D. Langle-Chimal is a data scientist at THINKMD and a Ph.D. candidate in Complex Systems and Data Science at the University of Vermont. He is interested in the use of data science, machine learning and mathematical modeling in decision-making processes to create preventive tools and reduce inequalities that affect the most vulnerable populations. This interest has also led Ollin to work previously as a data scientist for the Mexican Ministry of Social Development, the Inter-American Development Bank, and the World Bank.
