Abstract This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent‐based model (ABM) and discrete event simulation (DES), and their hybrids in COVID‐19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID‐19 transmission dynamics, 204 evaluated both pharmaceutical and non‐pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID‐19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID‐19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio‐economic systems involved.

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

In this paper, we provide a thorough analysis of wind speed probability distribution functions (PDF) and test the accuracy of the proposed forecasting method and density functions with regards to measured spatio-temporal data at several weather stations in Iran. We present statistical analysis of wind energy where several proposed probability density functions such as Normal, Weibull, Gamma and Wald are verified according to the real-time measured data. The accuracy of density functions is investigated through the goodness of fit procedure and statistical tests. Finally, we present a low-complexity, yet high-accuracy forecasting approach using autoregressive-moving-average (ARMA) model for wind speed prediction.

Wind Speed Forecasting and Probability Distribution Analysis Using Measured Data at Weather Stations in Iran

Picking a career stream profoundly influences people’s abilities in different ways. Nowadays, choosing the correct career stream in advanced education is troublesome, as the number of people wanting to be in specific specializations is growing. Therefore, it is essential to be able to select the appropriate career path. This article proposes a system that can suggest streams in advanced education schools. This system is influenced by agent-based stream proposal systems (ASPS). The proposed system aims to make picking out the correct stream to study at school simpler for an individual. Different streams are evaluated based on seven pre-characterized models. In our system, three unique sorts of tests, learning styles, and coaching were coordinated in a way that caused the system to recognize an individual’s interests and limits to an area of study. A sample of 238 participants was recruited for our questionnaire on accessibility, user-friendliness, accuracy, and satisfaction with the system. The incorporation of learning styles and coaching proved helpful in the study. The reliability and validity were proven in addition to incorporating a thinking-aloud protocol and immediate evaluation in the pre-, during and post-tests. To a large extent, respondents were satisfied with the model, as was revealed in the Likert scale response frequencies and percentages. The proposed system can be applied to undergraduates choosing engineering, medicine, arts and science degrees

A novel intelligent agent-based framework for appropriate stream selection from perceptive of career counseling

We generalize the bulk-synchronous parallel (BSP) processing model to make it better support agent-based simulations. Such simulations frequently exhibit hierarchical structure in their communication patterns which can be exploited to improve performance. We allow for the creation of temporary artificial network partitions during which agents synchronize only locally within their group in a way that does not compromise the correctness of a simulation. We have built a distributed engine, CloudCity, which uses this idea to improve the locality of computation, communication, and synchronization in such simulations. We experimentally evaluate the performance of our system on a benchmark of simulation workloads and compare it against other popular BSP-like systems, obtaining insights into the impact of various system design choices and optimization on simulation engine performance. 

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

An Analysis of the Dynamics of COVID-19 Pandemic in Zimbabwe Using the Extended SEIR Model with Treatment and Quarantine

COVID-19 has spread rapidly around the globe. While there has been a slow down of the spread in some countries, e.g., in China, the African continent is still at the beginning of a potentially wide spread of the virus. Owing to its economic strength and imbalances, South Africa is of particular relevance with regard to the drastic measures to prevent the spread of this novel coronavirus. In March 2020, South Africa imposed one of the most severe lockdowns worldwide and subsequently faced the number of infections slowing down considerably. In May 2020, this lockdown was partially relaxed and further easing of restrictions was envisaged. In July and August 2020, daily new infections peaked and declined subsequently. Lockdown measures were further relaxed. This study aims to assess the recent and upcoming measures from an epidemiological perspective. Agent-based epidemic simulations are used to depict the effects of policy measures on the further course of this epidemic. The results indicate that measures that are either lifted too early or are too lenient have no sufficient mitigating effects on infection rates. Consequently, continuous exponential infection growth rates or a second significant peak of infected people occur. These outcomes are likely to cause higher mortality rates once healthcare capacities are occupied and no longer capable to treat all severely and critically infected COVID-19 patients. In contrast, strict measures appear to be a suitable way to contain the virus. The simulations imply that the initial lockdown of 27 March 2020 was probably sufficient to slow the growth in the number of infections, but relaxing countermeasures might allow for a second severe outbreak of COVID-19 in our investigated simulation region of Nelson Mandela Bay Municipality.

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Jan Chr. Schlüter

Papers

Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation

Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data.