How simulation modelling can help reduce the impact of COVID-19

Currie, Christine S. M.; Fowler, John W.; Kotiadis, Kathy; Monks, Thomas; Onggo, Bhakti Stephan; Robertson, Douglas R.; Tako, Antuela A.

doi:10.1080/17477778.2020.1751570

Cited by 365 publications

(310 citation statements)

References 69 publications

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“…Currie et al [12] provide a detailed review of how models and simulations can help reduce the impact of COVID-19. For example, accurate epidemiological models are indispensable for planning and decision making.…”

Section: Simulation and Modellingmentioning

confidence: 99%

“…2) Simulation Models: Simulation models have broad applicability and can be used in a variety of settings [12], including decisions that affect disease transmission-e.g., decisions related to quarantine and social distancing strategies; decisions regarding resource management-e.g., decisions related to capacity of in-patient hospital beds, critical care units, staffing, and resource allocation within and across regions; and decisions about care-e.g., deciding thresholds for admission and discharge of patients and minimising the impact on other patients. In particular, pandemics generate a large number of questions all of which cannot be answered by epidemiological models alone.…”

Section: Simulation and Modellingmentioning

confidence: 99%

“…By considering the range of model outputs, an additional benefit is that an estimate of uncertainty can be produced, which may help policy makers gauge expected benefits against risks. For interested readers, the review paper [12] provides an overview of the use of various simulation modelling methods-including those based on system dynamics [23], agent based models, discrete event simulations, and hybrid simulations-to reduce the impact of COVID-19.…”

Section: Simulation and Modellingmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Latif¹,

Usman²,

Manzoor³

et al. 2020

Preprint

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<div>COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies.</div><div>As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works.</div>

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Section: Simulation and Modellingmentioning

confidence: 99%

Section: Simulation and Modellingmentioning

confidence: 99%

Section: Simulation and Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Latif¹,

Usman²,

Manzoor³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…The literature seems to suggest that using the number of new cases and the level of social distancing are the key variables to analyze the COVID-19 in various ways. In what follows, we provide a background information about the four main COVID-19 modeling techniques: system dynamics, agent-based modeling, discrete event simulation, and hybrid simulation [8]. System dynamics uses differential equations to model resources, knowledge, people, and money, and the flows between these parameters explains the simulation behavior.…”

Section: The Modelmentioning

confidence: 99%

How Can We Be Ahead of COVID-19 Curve? A Hybrid Knowledge-Based and Modified Regression Analysis Approach for COVID-19 Tracking in USA

Hussein¹

2021

Some RNA Viruses

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Since its appearance in 2019, the COVID-19 virus deluged the world with unprecedented data in short time. Despite the countless worldwide pertinent studies and advanced technologies, the spread has been neither contained nor defeated. In fact, there is a recent record surge in the number of confirmed new cases. The rational question is thus: why has it taken so long to date to forecast the trajectory of the spread? To this end, this chapter presents a new predictive Knowledge-based (KB) toolkit named CORVITT (Corona Virus Tracking Toolkit) and a modified linear regression model. This logical step assists the officials, organizations, and users to forecast the spread trajectory and accordingly make proactive rather than retroactive intervention decisions. This hybrid approach uses the confirmed new cases and demographic data, implemented. CORVITT is not an epidemiological model, in the sense that it does not model disease transmission, nor does it use underlying epidemiological parameters or data including the reproductive rate, disease methods, real time polymerase chain reaction cycle threshold, the virus structure and pathogenesis, etc. The chapter is a seed in an in-progress study that will broaden its scope by including additional parameters.

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“…Emerging applications of DES addressing the COVID-19 pandemic hospital planning problems can be found in [8][9][10]. The review of how simulation modeling can help reduce the impact of COVID-19 was presented in [11]. However, most of the DES work on COVID-19 did not account for the highly dynamic and regional-speci c disease spread nature and the impact of interventions (e.g., social distancing orders) on infection control.…”

Section: Introductionmentioning

confidence: 99%

Hospital beds and personal protective equipment planning for COVID-19 pandemic: A computer simulation approach

Guan

Fishe

et al. 2020

Preprint

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Health care systems are at the frontline to fight the COVID-19 pandemic. An emergent question for each hospital is how many general ward and intensive care unit beds are needed and how much personal protective equipment to be purchased. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a computer simulation approach to evaluating bed utilizations and the corresponding supply needs based on the operational considerations and constraints in individual hospitals. We built a data-driven SEIR model which is adaptive to control policies and can be utilized for regional forecast targeting a specific hospital’s catchment area. The forecast model was integrated into a discrete-event simulation which modeled the patient flow and the interaction with hospital resources. We tested the simulation model outputs against patient census data from UF Health Jacksonville, Jacksonville, FL. Simulation results were consistent with the observation that the hospital has ample bed resources to accommodate the regional COVID patients. After validation, the model was used to predict future bed utilizations given a spectrum of possible scenarios to advise bed planning and stockpiling decisions. Lastly, how to optimally allocate hospital resources to achieve the goal of reducing the case fatality rate while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge.

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How simulation modelling can help reduce the impact of COVID-19

Cited by 365 publications

References 69 publications

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

How Can We Be Ahead of COVID-19 Curve? A Hybrid Knowledge-Based and Modified Regression Analysis Approach for COVID-19 Tracking in USA

Hospital beds and personal protective equipment planning for COVID-19 pandemic: A computer simulation approach

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