Several challenges can arise during model validation:
1. Data Quality: Poor quality data can lead to inaccurate validation results. Ensuring high-quality, relevant, and representative data is crucial. 2. Complexity of Models: Complex models with many parameters can be difficult to validate, requiring advanced techniques and computational resources. 3. Changing Epidemiological Patterns: Diseases can evolve, and new strains can emerge, making it challenging to validate models that were developed based on older data. 4. Ethical and Privacy Concerns: Using sensitive health data for validation purposes must comply with ethical guidelines and privacy regulations.
