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• W3015701143 abstract "The problem of tracking epidemic spreading on networks is relevant to the control of morbidity. The transmission dynamics of an epidemic can be described by a simple compartmental model. Specifically, the estimation of epidemic spreading on networks can be achieved by a nonlinear Kalman filter, which is a tool for state estimation of nonlinear systems. In this article, epidemic spreading on networks is described by compartmental models, such as susceptible-infected-susceptible, susceptible-infected-recovered, and susceptible-infected-recovered-susceptible models. The dynamics of epidemic spreading on homogeneous networks, including Erdös-Renyi network, Watts-Strogatz network, and Newman-Watts network, are estimated by several nonlinear Kalman filters, including extended Kalman filter, unscented Kalman filter, and third-degree and fifth-degree cubature Kalman filters. The performance comparison in terms of accuracy and stability forms a guideline of utilizing nonlinear Kalman filters for tracking epidemic spreading. The theoretical analysis has been validated through numerical experiments." @default.
• W3015701143 created "2020-04-17" @default.
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• W3015701143 date "2020-12-01" @default.
• W3015701143 modified "2023-09-26" @default.
• W3015701143 title "Performance Comparison of Nonlinear Kalman Filters in Epidemic Tracking on Networks" @default.
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• W3015701143 doi "https://doi.org/10.1109/jsyst.2020.2979221" @default.
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