Input-output reduced order modeling for public health intervention evaluation

TL;DR

This paper introduces a new input-output dimension reduction technique for public health models, simplifying intervention planning by reducing computational complexity and parameter space.

Contribution

It presents a novel method that reduces model input dimensions through output space dimension reduction and mapping, validated on an HIV intervention model.

Findings

Effective reduction of model input space complexity.

Validated approach on HIV intervention model.

Potential to improve public health model efficiency.

Abstract

In recent years, mathematical models have become an indispensable tool in the planning, evaluation, and implementation of public health interventions. Models must often provide detailed information for many levels of population stratification. Such detail comes at a price: in addition to the computational costs, the number of considered input parameters can be large, making effective study design difficult. To address these difficulties, we propose a novel technique to reduce the dimension of the model input space to simplify model-informed intervention planning. The method works by first applying a dimension reduction technique on the model output space. We then develop a method which allows us to map each reduced output to a corresponding vector in the input space, thereby reducing its dimension. We apply the method to the HIV Optimization and Prevention Economics (HOPE) model, to validate the approach and establish proof of concept.