Verification of an agent-based disease model of human mycobacterium tuberculosis infection

TL;DR

This paper presents a comprehensive verification framework for agent-based models, specifically applied to a tuberculosis infection model, to systematically evaluate and quantify numerical errors and improve model credibility.

Contribution

It introduces a general verification workflow for agent-based models, including deterministic and stochastic assessments, demonstrated on a tuberculosis immune response model.

Findings

The verification framework effectively quantifies numerical errors in the UISS-TB model.

The workflow can be applied to other agent-based models for credibility assessment.

Results support systematic error identification in complex biological simulations.

Abstract

Agent-Based Models are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an important step of the model credibility assessment: solution verification. This study overcomes this limitation by proposing a general verification framework for Agent-Based Models that aims at evaluating the numerical errors associated with the model. A step-by-step procedure, which consists of two main verification studies (deterministic and stochastic model verification), is described in detail and applied to a specific mission critical scenario: the quantification of the numerical approximation error for UISS-TB, an ABM of the human immune system developed to predict the progression of pulmonary tuberculosis. Results provide indications on the possibility to use the proposed model verification workflow to systematically identify and quantify numerical approximation errors associated with UISS-TB and, in general, with any other ABMs.