Parameter inference

Parameter inference involves estimating the values of model parameters that best fit the observed data. Techniques such as Bayesian inference, maximum likelihood estimation, and machine learning are commonly used. Accurate parameter estimation is vital for model credibility and for understanding key factors influencing disease spread. Challenges include dealing with uncertainty, limited data, and computational complexity.