Improving Data Collection Enhancing the quality and granularity of data collection can significantly improve model accuracy. This includes better surveillance systems, increased testing, and more comprehensive data reporting.
Incorporating Real-World Complexities Developing models that better incorporate real-world complexities, such as social behavior, environmental factors, and heterogeneous mixing patterns, can provide more realistic predictions. Agent-based models, for instance, can simulate individual behaviors and interactions more accurately than compartmental models.
Robust Parameter Estimation Using a variety of data sources and methods for parameter estimation can reduce the risk of bias and improve model reliability. Techniques such as Bayesian Inference can provide more robust estimates by combining prior knowledge with observed data.
Regular Model Validation Regularly validating models against new data and real-world outcomes can help identify and correct inaccuracies. This iterative process ensures that models remain relevant and accurate over time.
