From Surveillance to Foresight: Project Management Frameworks for Predictive Public Health Intelligence (2024)

Abstract

Predictive public health intelligence represents a critical evolution in disease surveillance, transforming public health systems from predominantly reactive monitoring mechanisms into anticipatory, decision-oriented infrastructures capable of informing timely prevention and response. While advances in data collection, computational capacity, and analytical techniques have accelerated the development of forecasting and early-warning models, many public health organizations continue to struggle with translating predictive insights into operational action. This persistent gap reflects not technical limitations, but deficiencies in governance, execution models, and institutional readiness.

This research presents a comprehensive project- and program-management framework for the design, governance, and operationalization of predictive public health intelligence platforms. Integrating principles from public health informatics, systems engineering, implementation science, and program management, the proposed framework addresses the full lifecycle of predictive capability development - from data readiness and model governance to decision authorization, response coordination, and continuous performance improvement. Particular emphasis is placed on establishing clear accountability structures, standardized decision pathways, and feedback mechanisms that enable organizations to act confidently on probabilistic intelligence.

Using extensive synthetic datasets representing multi-region and multi-jurisdictional surveillance environments, the study evaluates the relationship between management maturity and predictive intelligence performance. Quantitative findings demonstrate that jurisdictions with mature governance and program execution models achieve significantly higher forecast accuracy, extended preparedness lead times, improved intervention fidelity, and greater institutional trust in predictive outputs. Notably, improvements in foresight effectiveness are driven less by algorithmic sophistication than by disciplined data stewardship, structured analytics lifecycle management, and integrated operational workflows.

The findings underscore that predictive public health intelligence should be understood as a socio-technical capability rather than a standalone analytical function. Sustainable foresight depends on organizational design choices that align data, analytics, governance, and action within a coherent execution framework. By shifting the focus from model development to programmatic delivery, this research provides evidence-based guidance for public health agencies seeking to institutionalize predictive intelligence as a core component of resilient, future-ready health systems.