@Article{info:doi/10.2196/59971,
author="Keshavamurthy, Ravikiran
and Pazdernik, Karl T
and Ham, Colby
and Dixon, Samuel
and Erwin, Samantha
and Charles, Lauren E",
title="Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework",
journal="JMIR Public Health Surveill",
year="2025",
month="Mar",
day="21",
volume="11",
pages="e59971",
keywords="disease forecasting; machine learning; deep learning; epidemiology; One Health; decision-making; data visualization",
abstract="Background: Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. Objective: To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. Methods: We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics---prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability---which are essential to strategic data-driven decisions. Results: While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. Conclusions: As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks. ",
issn="2369-2960",
doi="10.2196/59971",
url="https://publichealth.jmir.org/2025/1/e59971",
url="https://doi.org/10.2196/59971"
}