FACS-CHARM: A Hybrid Agent-Based and Discrete-Event Simulation Approach for Covid-19 Management at Regional Level

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. Decisionmakers 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.

Discrete‐event simulation is still alive and strong: evidence from bibliometric performance evaluation of research during COVID‐19 global health pandemic

“…On the other hand, DES is better with problems such as medical supply chain disruptions and resilience (Burgos and Ivanov, 2021). Finally, the third category of problems can be solved using a hybrid of DES and ABS, for example, issues involving social interactions, optimizations, and decision‐making (Anagnostou et al., 2022).…”

Proceedings of SW21 The OR Society Simulation Workshop

“…Three simulation models were used in the workshop that model different aspects of the COVID-19 pandemic. These include the dynamiC Hospital wArd Management (CHARM) Discrete-Event Simulation (DES) for hospital bed capacity management (Anagnostou et al, 2022), the Flu and Coronavirus Simulation (FACS) Agent-Based Simulation (ABS) that models the geospatial spread of the disease and the impact of preventive measures (Anagnostou et al, 2022), and the CoronAvirus Lifelong Modelling and Simulation (CALMS) ABS that considers long-term policy intervention effectiveness (Mintram et al, 2022).…”

Improving the usability of open health service delivery simulation models using Python and web apps

“…For example, in health economics and pharmacology, robust methods have been proposed to build user-friendly interfaces for models developed in R using Shiny, an open source framework for building web applications in R 22,23 . There are few examples of simulation studies for health services delivery that have developed free and open source DES models with a focus on use and reuse, using R or Python 14,24,25 . Several well-maintained DES libraries are available in Python, including simpy 26 , salabim 27 , and ciw 28 , while streamlit provides a relatively simple alternative to Shiny for building web apps in Python.…”

“…Free and Open Source Software (FOSS) for simulation offers an alternative to proprietary software without the licensing restrictions that affect uptake, sharing, and reuse of model code 10,11 . In health service delivery research, computational analyses, and simulation studies can be conducted using FOSS tools built in the popular coding languages of Python, R, and Julia [12][13][14][15][16][17] . Use of a language such as Python has further benefits for research as models can now be integrated with the wider data science ecosystem for statistical modelling and machine learning.…”