{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:34:32Z","timestamp":1790703272361,"version":"4.1.0"},"reference-count":43,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Public Health Surveill"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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\u2014prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability\u2014which are essential to strategic data-driven decisions.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/59971","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T09:52:06Z","timestamp":1735033926000},"page":"e59971-e59971","source":"Crossref","is-referenced-by-count":7,"title":["Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven 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