Towards a utility-driven forecast framework for infectious disease outbreak: Study

Forecasting infectious disease outbreaks plays a central role in preparedness, yet current modelling approaches focus mainly on accuracy-based metrics that overlook when predictions are made and how they support actual decision-making. This limits their usefulness for real-world public health planning, where the timing and operational relevance of forecasts directly influence intervention effectiveness, hospital readiness, and resource allocation.

 

This study aims to shift the evaluation and use of outbreak forecasts toward a utility-driven decision-support framework. It focuses on three interconnected domains: (i) population-level case forecasting evaluated using time-sensitive utility scores, (ii) patient-level severity forecasting to anticipate ward occupancy and staffing needs, and (iii) resource demand forecasting and prioritization under operational constraints.

 

If implemented, the study hopes to positively improve outbreak preparedness by supporting earlier public health warnings, better hospital preparedness, and improved resource allocation.

C-AIM Lead：

Michele NGUYEN Asst. Professor of Data Analytics Lee Kong Chian School of Medicine Nanyang Technological University

Related Research Domain： Public Health， Implementation

Keywords: Forecasting, outbreak preparedness, public health