Although system dynamics [SD] and agent-based modelling [ABM] have individually served as effective tools to understand the Covid-19 dynamics, combining these methods in a hybrid simulation model can help address Covid-19 questions and study systems and settings that are difficult to study with a single approach. To examine the spread and outbreak of Covid-19 across multiple care homes via bank/agency staff and evaluate the effectiveness of interventions targeting this group, we develop an integrated hybrid simulation model combining the advantages of SD and ABM. We also demonstrate how we use several approaches adapted from both SD and ABM practices to build confidence in this model in response to the lack of systematic approaches to validate hybrid models. Our modelling results show that the risk of infection for residents in care homes using bank/agency staff was significantly higher than those not using bank/agency staff (Relative risk [RR] 2.65, 95% CI 2.57–2.72). Bank/agency staff working across several care homes had a higher risk of infection compared with permanent staff working in a single care home (RR 1.55, 95%CI 1.52–1.58). The RR of infection for residents is negatively correlated to bank/agency staff’s adherence to weekly PCR testing. Within a network of heterogeneous care homes, using bank/agency staff had the most impact on care homes with lower intra-facility transmission risks, higher staff-to-resident ratio, and smaller size. Forming bubbles of care homes had no or limited impact on the spread of Covid-19. This modelling study has implications for policy makers considering developing effective interventions targeting staff working across care homes during the ongoing and future pandemics.
As an important project on the golden waterway of the Yangtze River in China, the Three Gorges–Gezhouba Dams (TGGD) plays a pivotal role in the construction of the Yangtze River Economic Belt. To improve the efficiency and safety of ship traffic, some novel navigation regulations have been implemented that change the TGGD operation obviously. For example, a piecewise control strategy proposed in the regulations is applied to control the traffic flow of ships under a sectional manner. With the implementation of these regulations, how to understand the dynamic effects of new changes on TGGD has been an important problem. The purpose of this work is to evaluate the navigation performance of the TGGD via a data- and event-driven hybrid simulation model developed by multi-agent and discrete-event modeling theories. The model simulates the three significant navigable scenarios inherent in the actual operating environment: dry season, wet season, and flood season, reflecting the real situations. The input data come from the statistical analysis of the actual navigation data provided by the Three Gorges Navigation Administration. The validity and reliability of the model are verified by comparing the output results with actual data. Moreover, a set of test experiments are designed to explore the TGGD navigation limit and analyze the key factors that restrict the navigation capacity of the TGGD system. The work is expected to provide a certain decision support for the future cooperative scheduling optimization of the TGGD.
Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation is extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation are conceived as being deterministic. However, associated uncertainties are present, whilst simulation deviation, due to their presence, could be significant. In this regard, global sensitivity analysis based on Sobol’ indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol’ sensitivity indices requires an unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques using machine learning data-driven regression are utilized to alleviate this burden. This study extends the current global sensitivity analysis practices in hybrid simulation by employing various different surrogate modeling methodologies as well as providing comparative results. In particular, polynomial chaos expansion, Kriging and polynomial chaos Kriging are used. A case study encompassing a virtual hybrid model is employed, and hybrid model response quantities of interest are selected. Their respective surrogates are developed, using all three aforementioned techniques. The Sobol’ indices obtained utilizing each examined surrogate are compared with each other, and the results highlight potential deviations when different surrogates are used.