RESEARCH ARTICLE A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea Ben J Brintz1,2*, Benjamin Haaland3, Joel Howard4, Dennis L Chao5, Joshua L Proctor5, Ashraful I Khan6, Sharia M Ahmed2, Lindsay T Keegan1, Tom Greene1, Adama Mamby Keita7, Karen L Kotloff8, James A Platts-Mills9, Eric J Nelson10,11, Adam C Levine12, Andrew T Pavia4, Daniel T Leung2,13* 1Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, United States; 2Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, United States; 3Population Health Sciences, University of Utah, Salt Lake City, United States; 4Division of Pediatric Infectious Diseases, University of Utah, Salt Lake City, United States; 5Institute of Disease Modeling, Bill and Melinda Gates Foundation, Seattle, United States; 6International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh; 7Centre Pour le De´veloppement des Vaccins-Mali, Bamako, Mali; 8Division of Infectious Disease and Tropical Pediatrics, University of Maryland, Baltimore, United States; 9Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, United States; 10Departments of Pediatrics, University of Florida, Gainesville, United States; 11Departments of Environmental and Global Health, University of Florida, Gainesville, United States; 12Department of Emergency Medicine, Brown University, Providence, United States; 13Division of Microbiology and Immunology, Department of Internal Medicine, University of Utah, Salt Lake City, United States *For correspondence:
[email protected] (BJB);
[email protected] (DTL) Abstract Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and Competing interests: The authors declare that no seasonal factors, may improve predictive performance.