This study develops a standardised checklist approach to improve the reporting of discrete-event simulation, system dynamics and agent-based simulation models within the field of Operational Research and Management Science. Incomplete or ambiguous reporting means that many simulation studies are not reproducible, leaving other modellers with an incomplete picture of what has been done and unable to judge the reliability of the results. Crucially, unclear reporting makes it difficult to reproduce or reuse findings. In this paper, we review the evidence on the quality of model reporting and consolidate previous work. We derive general good practice principles and three 20-item checklists aimed at Strengthening The Reporting of Empirical Simulation Studies (STRESS): STRESS-DES, STRESS-ABS and STRESS-SD for discrete-event simulation, agent-based simulation and system dynamics, respectively. Given the variety of simulation projects, we provide usage and troubleshooting advice to cover a wide range of situations.

The publications that relate to the application of simulation to healthcare have steadily increased over the years. These publications are scattered amongst various journals that belong to several subject categories, including operational research, health economics and pharmacokinetics. The simulation techniques that are applied to the study of healthcare problems are also various. The aim of this study, therefore, is to review healthcare simulation literature that have been published between 1970 and 2007 in high-quality journals belonging to various subject categories and that report on the application of four simulation techniques, namely, Monte Carlo simulation, discrete-event simulation, system dynamics and agent-based simulation. Arguably, journal impact factor is fundamental in assessing the quality of publications. Thus, the 201 publications selected for review have been queried from the ISI Web of Science® bibliographic database of high-impact research journals. Through a review of healthcare simulation literature the following three objectives have been realized: (a) papers have been categorized under the different simulation techniques, and the healthcare problems that each technique is employed to investigate are identified; (b) variables such as authors, article citations, etc., within our dataset of healthcare papers have been profiled; (c) turning point (strategically important) papers and authors have been identified through co-citation analysis of references cited by the papers in our dataset. The above objectives have been realized by devising and then employing a methodology for profiling literature. It is expected that this review paper will help the readers gain a broader understanding of research in healthcare simulation.

In an attempt to investigate blood unit ordering policies, researchers have created a discrete-event model of the UK National Blood Service (NBS) supply chain in the Southampton area of the UK. The model has been created using Simul8, a commercial off-the-shelf (COTS) discrete-event simulation package (CSP). However, as more hospitals were added to the model, it was discovered that the length of time needed to perform a single simulation severely increased. It has been claimed that distributed simulation, a technique that uses the resources of many computers to execute a simulation model, can reduce simulation runtime. Further, an emerging standardized approach exists that supports distributed simulation with CSPs. These CSP Interoperability (CSPI) standards are compatible with the IEEE 1516 standard, the High Level Architecture (HLA), the de facto interoperability standard for distributed simulation. To investigate if distributed simulation can reduce the execution time of NBS supply chain simulation, this paper presents experiences of creating a distributed version of the CSP Simul8 according to the CSPI/HLA standards. It shows that the distributed version of the simulation does indeed run faster when the model reaches a certain size. Further, we argue that understanding the relationship of model features is key to performance. This is illustrated by experimentation with two different protocols implementations (using Time Advance Request (TAR) and Next Event Request (NER)). Our contribution is therefore the demonstration that distributed simulation is a useful technique in the timely execution of supply chains of this type and that careful analysis of model features can further increase performance.

The recent Covid-19 outbreak has had a tremendous impact on the world, and many countries are struggling to help incoming patients and at the same time, rapidly enact new public health measures such as lock downs. Many of these decisions are guided by the outcomes of so-called Susceptible-Exposed-Infectious-Recovered (SEIR) models that operate on a national level. Here we introduce the Flu And Coronavirus Simulator (FACS), a simulation tool that models the viral spread at the sub-national level, incorporating geospatial data sources to extract buildings and residential areas in a region. Using FACS, we can model Covid-19 spread at the local level, and provide estimates of the spread of infections and hospital arrivals for different scenarios. We validate the simulation results with the ICU admissions obtained from the local hospitals in the UK. Such validated models can be used to support local decision-making for an effective health care capability response to the epidemic.