What Are the Challenges in Making Realistic Assumptions?
Creating realistic assumptions in epidemiological models is fraught with challenges:
- Data Limitations: Often, there is a lack of detailed data to inform realistic assumptions, especially in low-resource settings or during emerging outbreaks.
- Complexity vs. Usability: More realistic models are often more complex and computationally intensive, which can make them less usable for real-time decision-making.
- Dynamic Nature of Epidemics: The characteristics of an epidemic can change rapidly, making static assumptions outdated. Models need to be adaptable to evolving situations.
