Epidemic Informatics and Control: A Holistic Approach from System Informatics to Epidemic Response and Risk Management in Public Health

2026-07-15

AI Summary

This study addresses the systemic gaps in public health emergency response revealed by the COVID-19 pandemic, particularly the absence of a comprehensive, data-driven risk management framework. To bridge this gap, the work proposes the first adaptation of the industrial DMAIC (Define, Measure, Analyze, Improve, Control) systems informatics paradigm to epidemic control. By integrating medical diagnostics, statistical sampling, data visualization, artificial intelligence, telemedicine, and resource optimization, the framework establishes an intelligent decision-support system spanning the entire epidemic lifecycle. This approach not only enhances the resilience and responsiveness of health systems but also fosters interdisciplinary convergence between systems informatics and public health, offering a replicable and scalable methodological foundation for managing future public health emergencies.

Abstract

This paper presents a holistic systems informatics approach, i.e., Define, Measure, Analyze, Improve, and Control (DMAIC), for epidemic response and management through the intensive use of data, statistics and optimization. Despite the sustained successes of system informatics in a variety of established industries such as manufacturing, logistics, services and beyond, there is a dearth of concentrated review and application of the data-driven DMAIC approach in the context of epidemic outbreaks. First, we define specific challenges posed by epidemic outbreaks to populational health, health systems, as well as economic challenges to different industries such as retailing, education and manufacturing. Second, we present a review of medical testing and statistical sampling methods for data collection, as well as existing efforts in data management and data visualization. Third, we discuss the importance to realizing the full potential of data for epidemic insights, and emphasize the need to leverage analytical methods and tools for decision support. Fourth, an epidemic brings imperative changes to health systems. We discuss the new trend of healthcare solutions to improve system resilience, including telehealth, artificial intelligence, resource allocation, and system re-design. In closing, prescriptive approaches are discussed to optimize the health policies and action strategies for controlling the spread of virus. We posit that this work will catalyze more in-depth investigations and multi-disciplinary research efforts to accelerate the application of system informatics methods and tools in epidemic response and risk management.

Problem

• Epidemic Informatics
• Public Health
• DMAIC
• Epidemic Response
• Risk Management

Innovation

• DMAIC
• systems informatics
• epidemic response
• data-driven decision making
• health system resilience