The contribution of reduced COVID-19 test time in controlling the spread of the disease: A simulation-based approach

The novel coronavirus COVID-19 has known a large spread over the globe threatening human health. Recommendations from WHO and specialists insist on testing on a mass scale. However, health systems do not have enough resources. The current process requires the isolation of testees in the hospitals’ isolation rooms for several hours until the test results are revealed, limiting hospitals’ capacities to test large numbers of cases. The aim of this paper was to estimate the impact of reducing the COVID-19 test time on controlling the pandemic spread, through increasing hospitals’ capacities to test on a mass scale. First, a discrete-event simulation was used to model and simulate the COVID-19 testing process in Morocco. Second, a mathematical model was developed to demonstrate the effect of accurate identification of infected cases on controlling the disease’s spread. Simulation results showed that hospitals’ testing capacities could be increased six times if the test duration fell from 10 hours to 10 minutes. The reduction of test time would increase testing capacities, which help to identify all the infected cases. In contrast, the simulation results indicated that if the infected population is not accurately identified and no precautionary measures are taken, the virus will continue to spread until it reaches the total population. Reducing test time is a vital component of the response to the COVID-19 pandemic. It is essential for the effective implementation of policies to contain the virus.

Ascertaining Important Features of the JAPROSIM Simulation Library

This paper describes important features of JAPROSIM, a free and open source simulation library implemented in Java programming language. It provides a framework for building discrete event simulation models. The process interaction world view adopted by JAPROSIM is discussed. We present the architecture and major components of the simulation library. In order to ascertain important features of JAPROSIM, examples are given. Further motivations are discussed and suggestions for improving our work are given.

A discrete event simulation-based methodology for building a digital twin of patient pathways in the hospital for near real-time monitoring and predictive simulation

Background: Discrete Event Simulation (DES) is one of the many tools and methods used in the analysis and improvement of healthcare services. Indeed, DES provides perhaps the most powerful and intuitive method for analyzing, evaluating, and improving complex healthcare systems. This paper highlights the process of developing a Digital Twin (DT) framework based on online DES to run the DES model in parallel with the real world in real-time. Methods: This paper suggests a new methodology that uses DES connected to the Internet of Things (IoT) devices to build a DT platform of patient pathways in a hospital for near real-time monitoring and predictive simulation. An experimental platform that mimics the behavior of a hospital has been used to validate this methodology. Results: The application of the proposed methodology allowed us to test the monitoring functionality in the DT. Therefore, we noticed that the DT behaves exactly as the emulator does in near real-time, we also tested the prediction functionality and we noticed that the DT provides us with a proactive overview for the near future of the patient pathways. The predictive functionality of this DT must be improved depending on the various reasons mentioned in this article. Conclusions: This paper presents a new methodology called HospiT'Win that uses DES and IoT devices to develop a DT of patient pathways in hospitals. This DT consists of two real-time models, a DT for Monitoring (DTM) and a DT for Predicting (DTP). An experimental platform with an emulator of a real hospital was used to validate this methodology before connecting to the real hospital. In the DTP, "dynamic" empirical distributions were used to perform a predictive simulation for the near future. In future research, some additional features and machine learning algorithms will be used to improve the proposed DT models.

Simulation optimization of an inventory control model for a reverse logistics system

Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena® was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest® optimization tool.