Joint extented Kalman filter-based parameter estimation in networked SEIR models with stochastic variability: analysis using COVID-19 daily data from southern India

Graph Laplacian diffusion is utilised to augment the susceptible-infected-removed (SEIR) epidemic model by integrating a weighted network and introducing variability in the transmission rate parameter to simulate population movement across network nodes. Employing the Joint Extended Kalman filter (JEKF), our objective is to derive three critical parameters: the transmission rate (β), recovery rate (γ), and latent period rate (α). The JEKF's iterative approach continuously anticipates and updates the system state based on current parameter estimations, making it adaptable for parameter estimation in dynamic systems. Integration of model predictions with observed data further enhances accuracy. Subsequently, we offer visual insights and demonstrate our methodology through experiments conducted on a simple tree network and a small-world Watts-Strogatz graph. Our ultimate aim is to precisely determine optimal parameter values to formulate effective COVID-19 management plans tailored for South India.