Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework.

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

SUBMITTER: Keshavamurthy R 

PROVIDER: S-EPMC11951818 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature