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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Approaches for evaluating veterinary epidemiological models: verification, validation and limitations.

A Reeves¹, M A Salman, A E Hill

¹Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 80523, USA. AaronReeves@colostate.edu

Revue Scientifique Et Technique (International Office of Epizootics)

|October 4, 2011

Summary

This summary is machine-generated.

Evaluating animal disease models requires verification and validation. This ensures epidemiological models accurately represent disease spread and control strategies for informed decision-making.

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Area of Science:

Veterinary Epidemiology
Mathematical Modeling

Background:

Accurate epidemiological models are essential for effective animal disease control and management strategies.
Model evaluation, including verification and validation, is critical for ensuring the reliability of these tools.

Purpose of the Study:

To outline the importance and processes of model verification and validation in veterinary epidemiology.
To emphasize that model evaluation aims to build confidence in model application, not to prove absolute accuracy.

Main Methods:

Defining model verification as confirming correct operation and adherence to design.
Defining model validation as assessing how well a model represents the real-world system it models.
Highlighting the continuous and iterative nature of both model development and evaluation.

Main Results:

Verification ensures the model functions as intended.
Validation assesses the model's applicability to disease dynamics and control measures in specific populations.
Thorough documentation of purpose, assumptions, limitations, conceptual model, testing, and data is crucial for facilitating evaluation.

Conclusions:

Model evaluation is an ongoing process, not a one-time event.
The goal of evaluation is to establish appropriate confidence for decision-making.
Clear communication of model details aids in robust verification and validation.