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Mbalawata, I.S., Särkkä, S. and Haario, H. (2013) Parameter Estimation in Stochastic Differential Equations with Markov Chain Montecarlo and Non-Linear Kalman Filtering. Computational Statistics, 28, 1195-1223.
https://doi.org/10.1007/s00180-012-0352-y

has been cited by the following article:

• TITLE: Analysis of SDEs Applied to SEIR Epidemic Models by Extended Kalman Filter Method

  AUTHORS: D. Ndanguza, I. S. Mbalawata, J. P. Nsabimana

  KEYWORDS: Epidemic Model, Estimation of Parameters, Extended Kalman Filter, Markov Chain Monte Carlo

  JOURNAL NAME: Applied Mathematics, Vol.7 No.17, November 23, 2016

  ABSTRACT: A disease transmission model of SEIR type is discussed in a stochastic point of view. We start by formulating the SEIR epidemic model in form of a system of nonlinear differential equations and then change it to a system of nonlinear stochastic differential equations (SDEs). The numerical simulation of the resulting SDEs is done by Euler-Maruyama scheme and the parameters are estimated by adaptive Markov chain Monte Carlo and extended Kalman filter methods. The stochastic results are discussed and it is observed that with the SDE type of modeling, the parameters are also identifiable.