ABSTRACT
In this study, we present a hybrid agent-based model (ABM) and discrete event simulation (DES) framework where ABM captures the spread dynamics of COVID-19 via asymptomatic passengers and DES captures the impacts of environmental variables, such as service process capacity, on the results of different containment measures in a typical high-speed train station in China. The containment and control measures simulated include as-is (nothing changed) passenger flow control, enforcing social distancing, adherence level in face mask-wearing, and adding capacity to current service stations. These measures are evaluated individually and then jointly under a different initial number of asymptomatic passengers. The results show how some measures can consolidate the outcomes for each other, while combinations of certain measures could compromise the outcomes for one or the other due to unbalanced service process configurations. The hybrid ABM and DES models offer a useful multi-function simulation tool to help inform decision/policy makers of intervention designs and implementations for addressing issues like public health emergencies and emergency evacuations. Challenges still exist for the hybrid model due to the limited availability of simulation platforms, extensive consumption of computing resources, and difficulties in validation and optimisation. © 2023 The Operational Research Society.
ABSTRACT
Pandemics have huge impact on all aspect of people's lives. As we have experienced during the Coronavirus pandemic, healthcare, education and the economy have been put under extreme strain. It is important therefore to be able to respond to such events fast in order to limit the damage to the society. Decision-makers typically are advised by experts in order to inform their response strategies. One of the tools that is widely used to support evidence-based decisions is modeling and simulation. In this paper, we present a hybrid agent-based and discrete-event simulation for the Coronavirus pandemic management at regional level. Our model considers disease dynamics, population interactions and dynamic ICU bed capacity management and predicts the impact of various public health preventive measures on the population and the healthcare service. © 2022 IEEE.
ABSTRACT
In this research paper, we propose a hybrid agent-based and discrete-event simulation model coupled with a RFID/IoT infrastructure for improving COVID19 test centers located in Montreal region. This study is important, since defining an optimal capacity for healthcare operations is always a challenge, especially in a pandemic mode. Indeed, in such situations, all managers are more concerned by the effectiveness of daily operations regardless their efficiency. Even though, this can be meaningful and largely acceptable, it could lead to critical situations depending on how the current situation may evolve. To improve the performance of COVID19 test centers, it requires a good understanding of logistics flows and a visibility on daily patient inflows and different resource utilization. We propose a RFID/IoT infrastructure that captures test centers real time data and make them available to be used by our hybrid simulation model. The model uses real time data to continuously adjust test centers capacity. This study is part of a bigger project conducted in Montreal region where we design and develop Digital Twins modules to assist different healthcare units such as emergency departments, COVID19 vaccination centers as well as COVID19 test centers. © 2021 The Authors.