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UBC Theses and Dissertations

Nonlinear model predictive control for the suppression of the COVID-19 pandemic based on an agent-based model Niu, Yue


In this thesis, Nonlinear Model Predictive Control (NMPC) is used in conjunction with an Agent-Based Model (ABM) in order to find optimal control measures in containing the Coronavirus Disease 2019 (COVID-19) pandemic. The control measures are designed so that the hospital can have some level of available beds and at the same time the economy is minimally adversely impacted by the restrictive measures imposed by the government. To achieve this goal, it is crucial to predict the effects of control measures and tuning them adaptively. The ABM and NMPC are presented as solutions to these issues. The ABM is capable of predicting how control measures affect people's disease status by using various traits of individual agents such as symptom levels, latent periods (i.e., the time during which people are infected but not infectious), infectious periods (i.e., the time during which people remain infectious), and mobilities. Based on these predictions, NMPC can be employed to tune the control measures adaptively by taking into account the upcoming changes. Computer simulations demonstrate the superiority of NMPC in keeping the number of hospitalized patients below the hospital bed threshold with the least economic cost compared to on-off control and Proportional (P) control using the same ABM. According to the 100-triple simulations among NMPC, on-off control, and P-control, NMPC is shown to be the best control technique for 95 out of 100 times.

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