Estimation of seasonal transmission parameters in childhood infectious disease using a stochastic continuous time model

The development of accurate disease models is desirable for the purposes of gaining a better understanding of the underlying dynamics of infectious disease spread and for designing and implementing appropriate control measures to curb infectious disease spread. In this work we develop an estimation framework for long-term continuous time infectious disease models that considers both model and estimation noise. We present a nonlinear programming approach for efficient estimation of model parameters, including seasonal transmission profiles. We then demonstrate the effectiveness of this framework using measles data from New York City and Bangkok, and show that a strong correlation exists between estimated seasonal parameters and school term holidays.