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1.
International Journal of Organizational Analysis ; : 33, 2022.
Article in English | Web of Science | ID: covidwho-1822011

ABSTRACT

Purpose Using total interpretive structural modelling (TISM), this paper aims to "identify", "analyse" and "categorise" the sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic. Design/methodology/approach To obtain the data, a closed-ended questionnaire was used in addition to a scheduled interview with each respondent. To identify how the factors interact, the TISM approach was employed and the cross-impact matrix multiplication applied to a classification method was used to rank and categorise the sustainable-resilience readiness factors. Findings This study identified ten sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic. The study states that the major factors are environmental scanning, awareness and preparedness, team empowerment and working, transparent communication system, learning culture, ability to respond and monitor, organisational culture, resilience engineering, personal and professional resources and technology capability. Research limitations/implications The study focused primarily on sustainable-resilience readiness characteristics for the healthcare sector. Practical implications This research will aid key stakeholders and academics in better understanding the factors that contribute to sustainable-resilience in healthcare. Originality/value This study proposes the TISM technique for healthcare, which is a novel attempt in the subject of readiness for sustainable-resilience in this sector. The paper proposes a framework including a mixture of factors for sustainability and resilience in the healthcare sector for operations.

2.
Ocean Engineering ; : 111486, 2022.
Article in English | ScienceDirect | ID: covidwho-1821432

ABSTRACT

The global shipping industry has been severely influenced by the COVID-19 pandemic;in particular, a significant amount of passenger transportation has been suspended due to the concern of COVID-19 outbreak, as such voyages confine a dense crowd in a compact space. In order to accelerate the recovery of the maritime business and minimise passengers' risk of being infected, this work has developed a computational model to study the airborne transmission of COVID-19 viruses in the superstructure of a full-scale passenger vessel. Considering the vessel advancing in open water, simulations were conducted to study the particulate flow due to an infected person coughing and speaking, with the boat's forward door open and closed. The results suggest that keeping the forward door closed will help prevent the external wind flow spreading the virus. When the forward door is closed, virus particles' coverage is shown to be limited to a radius of half a metre, less than a seat's width. Thus, an alternate seat arrangement is suggested. Furthermore, investigations were conducted on the influence of wall-mounted Air Conditioner (AC) on the virus transmission, and it was found that controlling the AC outlet direction at less than 15° downward can effectively limit the virus spread. Meanwhile, it was demonstrated that an AC's backflow tends to gather virus particles in a nearby area, thus sitting farther from an opening AC may reduce the risk of being infected. Overall, this work is expected to inform hygienic guidelines for operators to counter COVID-19 and potentially similar viruses in the future.

3.
Mathematical Biosciences ; : 108824, 2022.
Article in English | ScienceDirect | ID: covidwho-1821409

ABSTRACT

The COVID-19 epidemic continues to rage in many parts of the world. In the UK alone, an array of mathematical models have played a prominent role in guiding policymaking. Whilst considerable pedagogical material exists for understanding the basics of transmission dynamics modelling, there is a substantial gap between the relatively simple models used for exposition of the theory and those used in practice to model the transmission dynamics of COVID-19. Understanding these models requires considerable prerequisite knowledge and presents challenges to those new to the field of epidemiological modelling. In this paper, we introduce an open-source R package, comomodels, which can be used to understand the complexities of modelling the transmission dynamics of COVID-19 through a series of differential equation models. Alongside the base package, we describe a host of learning resources, including detailed tutorials and an interactive web-based interface allowing dynamic investigation of the model properties. We then use comomodels to illustrate three key lessons in the transmission of COVID-19 within R Markdown vignettes.

4.
Journal of Organizational and End User Computing ; 34(3):18, 2022.
Article in English | Web of Science | ID: covidwho-1820460

ABSTRACT

With the increasing extreme weather events and various disasters, people are paying more attention to environmental issues than ever, particularly global warming. Public debate on it has grown on various platforms, including newspapers and social media. This paper examines the topics and sentiments of the discussion of global warming on Twitter over a span of 18 months using two big data analytics techniques: topic modelling and sentiment analysis. There are seven main topics concerning global warming frequently debated on Twitter: factors causing global warming, consequences of global warming, actions necessary to stop global warming, relations between global warming and COVID-19, global warming's relation with politics, global warming as a hoax, and global warming as a reality. The sentiment analysis shows that most people express positive emotions about global warming, though the most evoked emotion found across the data is fear, followed by trust. The study provides a general and critical view of the public's principal concerns and their feelings about global warming on Twitter.

5.
8th International Conference on Modelling and Simulation for Autonomous Systems (MESAS) ; 13207:397-416, 2021.
Article in English | Web of Science | ID: covidwho-1819411

ABSTRACT

Most of present humanitarian crises are protracted in nature and their average duration has increased. Climate change, environmental degradation, armed conflicts, terrorism, and migration are producing exponentially growing needs to whom humanitarian organizations are struggling to respond. Novel infectious diseases such as COVID-19 add complexity to protracted crises. Planning to respond to current and future medical threats should integrate terrorist risk assessment, to safeguard population and reduce risks to aid workers. Technologies such as Artificial Intelligence (AI), and Modelling and Simulation (M&S) can play a crucial role. The present research has included the conduct of the United Nations HNPW 2021 session on AI and Medical Intelligence and an exercise on a real scenario. Focusing on medical and terror threats in North East Nigeria operating environment, authors have successfully deployed and tested the Expert.ai Medical Intelligence Platform (MIP) jointly with the MASA SYNERGY constructive simulation, with the aim to improve situational awareness to support decision-making in the context of a humanitarian operation.

6.
Mining ; 2(1):86, 2022.
Article in English | ProQuest Central | ID: covidwho-1818179

ABSTRACT

Dumping is one of the main unit operations of mining. Notwithstanding a long history of using large rear dump trucks in mining, little knowledge exists on the cascading behavior of the run-of-mine material during and after dumping. In order to better investigate this behavior, a method for generating high fidelity models (HFMs) of dump profiles was devised and investigated. This method involved using unmanned aerial vehicles with mounted cameras to generate photogrammetric models of dumps. Twenty-eight dump profiles were created from twenty-three drone flights. Their characteristics were presented and summarized. Four types of dump profiles were observed to exist. Factors that influence the determination of these profiles include the location of the truck relative to the dump crest, the movement of the underlying dump material during the dumping process and the differences in the dump profile prior to dumping. The HFMs created in this study could possibly be used for calibrating computer simulations of dumps to better match reality.

7.
Computer Journal ; : 10, 2022.
Article in English | Web of Science | ID: covidwho-1816051

ABSTRACT

A novel Corona virus disease 2019 (COVID-19) outbreak is declared as the international public health emergency concern by the World Health Organization in the month of March 2020. This viral disease invented from China in the month of December 2019 has previously caused havoc around the world, including India. In this paper, efficient mathematical models using Gustafson-Kessel fuzzy clustering approach for the transmission of the COVID-19 are developed by considering the actual reported cases in the state of Tamil Nadu, India. The results proved a good concord between the reported data and the estimated data given by the proposed models. Moreover, the developed models are also capable to predict the requirements of beds in hospitals on the month of August 2020 in Tamil Nadu, India. Also, this work suggests strictly implementing/extending the complete lockdown for at least 21 days in the month of August 2020 and immediate separation of infected cases are the positive steps to reduce the spread of novel corona virus in Tamil Nadu state, India.

8.
Journal of Theoretical Biology ; 542:N.PAG-N.PAG, 2022.
Article in English | Academic Search Complete | ID: covidwho-1814837

ABSTRACT

• Genetic mutations in SARS-CoV-2 emerge, and some of them appears more contagious than historical strains. • The transmission advantage of genetic variants is an innate biological feature that is difficult to be altered by artificial factors. • We demonstrate that NPIs could lead to changes in transmission advantage. • Our findings highlight the important roles of NPIs not only in controlling epidemics but also in slowing the growth of variants. As the COVID-19 pandemic continues, genetic mutations in SARS-CoV-2 emerge, and some of them are found more contagious than the previously identified strains, acting as the major mechanism for many large-scale epidemics. The transmission advantage of mutated variants is widely believed as an innate biological feature that is difficult to be altered by artificial factors. In this study, we explore how non-pharmaceutical interventions (NPI) may affect transmission advantage. A two-strain compartmental epidemic model is proposed and simulated to investigate the biological mechanism of the relationships among different NPIs, the changes in transmissibility of each strain and transmission advantage. Although the NPIs are effective in flattening the epidemic curve, we demonstrate that NPIs probably lead to a decline in transmission advantage, which is likely to occur if the NPIs become intensive. Our findings uncover the mechanistic relationship between NPIs and transmission advantage dynamically, and highlight the important role of NPIs not only in controlling the intensity of epidemics but also in slowing or even containing the growth of the proportion of variants. [ FROM AUTHOR] Copyright of Journal of Theoretical Biology is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

9.
Mathematical Biosciences and Engineering ; 19(6):5998-6012, 2022.
Article in English | Scopus | ID: covidwho-1810396

ABSTRACT

Public health and social measures (PHSMs) targeting the coronavirus disease 2019 (COVID-19) pandemic have potentially affected the epidemiological dynamics of endemic infectious diseases. In this study, we investigated the impact of PHSMs for COVID-19, with a particular focus on varicella dynamics in Japan. We adopted the susceptible-infectious-recovered type of mathematical model to reconstruct the epidemiological dynamics of varicella from Jan. 2010 to Sep. 2021. We analyzed epidemiological and demographic data and estimated the within-year and multi-year component of the force of infection and the biases associated with reporting and ascertainment in three periods: pre-vaccination (Jan. 2010-Dec. 2014), pre-pandemic vaccination (Jan. 2015-Mar. 2020) and during the COVID-19 pandemic (Apr. 2020-Sep. 2021). By using the estimated parameter values, we reconstructed and predicted the varicella dynamics from 2010 to 2027. Although the varicella incidence dropped drastically during the COVID-19 pandemic, the change in susceptible dynamics was minimal;the number of susceptible individuals was almost stable. Our prediction showed that the risk of a major outbreak in the post-pandemic era may be relatively small. However, uncertainties, including age-related susceptibility and travel-related cases, exist and careful monitoring would be required to prepare for future varicella outbreaks. © 2022 the Author(s), licensee AIMS Press

10.
Photonics ; 9(4):238, 2022.
Article in English | ProQuest Central | ID: covidwho-1810086

ABSTRACT

This study aims to highlight the problems with implementing optical techniques (laser tweezers, diffuse light scattering and laser diffractometry) in clinical hemorheological practice. We provide the feasibility of these techniques to assess microrheological effects of various molecular mechanisms affecting RBC aggregation and deformability. In particular, we show that they allow assessment of changes in RBC aggregation in whole blood samples both on the level of single cells and on the level of large ensembles of cells. Application of these methods allows for studying the mechanisms of RBC aggregation because they are sensitive to changes in the medium which surrounds the RBC (i.e., blood plasma, serum or model solutions of blood plasma proteins) and to changes in the cellular properties of RBCs (i.e., effects on the cell membrane due to glycoprotein inhibition).

11.
International Journal of Environmental Research and Public Health ; 19(8):4709, 2022.
Article in English | ProQuest Central | ID: covidwho-1809871

ABSTRACT

Healthy food environments in early childhood play an important role in establishing health-promoting nutritional behaviours for later life. We surveyed Early Learning Services (ELS) in the Hawke’s Bay region of New Zealand and describe common barriers and facilitators to providing a healthy food environment, through descriptive survey analysis and thematic analysis of open-ended questions. We used a policy analysis tool to assess the strength and comprehensiveness of the individual centre’s nutrition policies and we report on the healthiness of menus provided daily in the centres. Sixty-two centres participated and 96.7% had policies on nutrition compared to 86.7% with policies on drinks. Of the 14 full policies provided for analysis, identified strengths were providing timelines for review and encouraging role modelling by teachers. The main weaknesses were communication with parents and staff, lack of nutrition training for staff and absence of policies for special occasion and fundraising food. With regard to practices in the ELS, food for celebrations was more likely to be healthy when provided by the centre rather than brought from home. Food used in fundraising was more likely to be unhealthy than healthy, though <20% of centres reported using food in fundraising. Only 40% of menus analysed met the national guidelines by not including any ‘red’ (unhealthy) items. Centre Managers considered the biggest barriers to improving food environments to be a lack of parental support and concerns about food-related choking. These results highlight the need for future focus in three areas: policies for water and milk-only, celebration and fundraising food;increased nutrition-focused professional learning and development for teachers;and communication between the centre and parents, as a crucial pathway to improved nutrition for children attending NZ early childhood education and care centres.

12.
Front Pharmacol ; 13:805344, 2022.
Article in English | PubMed | ID: covidwho-1809488

ABSTRACT

SARS-CoV-2 is the virus responsible for causing COVID-19 disease in humans, creating the recent pandemic across the world, where lower production of Type I Interferon (IFN-I) is associated with the deadly form of the disease. Membrane protein or SARS-CoV-2 M proteins are known to be the major reason behind the lower production of human IFN-I by suppressing the expression of IFNβ and Interferon Stimulated Genes. In this study, 7,832 compounds from 32 medicinal plants of India possessing traditional knowledge linkage with pneumonia-like disease treatment, were screened against the Homology-Modelled structure of SARS-CoV-2 M protein with the objective of identifying some active phytochemicals as inhibitors. The entire study was carried out using different modules of Schrodinger Suite 2020-3. During the docking of the phytochemicals against the SARS-CoV-2 M protein, a compound, ZIN1722 from Zingiber officinale showed the best binding affinity with the receptor with a Glide Docking Score of -5.752 and Glide gscore of -5.789. In order to study the binding stability, the complex between the SARS-CoV-2 M protein and ZIN1722 was subjected to 50 ns Molecular Dynamics simulation using Desmond module of Schrodinger suite 2020-3, during which the receptor-ligand complex showed substantial stability after 32 ns of MD Simulation. The molecule ZIN1722 also showed promising results during ADME-Tox analysis performed using Swiss ADME and pkCSM. With all the findings of this extensive computational study, the compound ZIN1722 is proposed as a potential inhibitor to the SARS-CoV-2 M protein, which may subsequently prevent the immunosuppression mechanism in the human body during the SARS-CoV-2 virus infection. Further studies based on this work would pave the way towards the identification of an effective therapeutic regime for the treatment and management of SARS-CoV-2 infection in a precise and sustainable manner.

13.
International Journal of Sustainable Aviation ; 8(2):162-180, 2022.
Article in English | ProQuest Central | ID: covidwho-1808593

ABSTRACT

This paper focuses on aircraft routing and crew rostering problems simultaneously considering the risk of COVID-19 infection. As airports are among high-risk places in COVID-19 pandemic, the crew prefer to spend less sit time in airports and come back to their home base at the end of each duty day. In this research, an integrated model is developed to assign crew and aircraft to flights in order to achieve a fair schedule for the crew. The objective function is minimisation of the difference between crew sit times. Moreover in this model, a framework including flight hours, number of days and number of take-offs is considered for maintenance requirements. Particle swarm optimisation (PSO) is used as the solution approach. To validate the solution approach, 20 test problems were solved using GAMS and PSO. The results show that PSO improved CPU time significantly (98.279% in average) in turn of 1.902% gap with GAMS in optimum solution.

14.
PLoS One ; 17(4), 2022.
Article in English | ProQuest Central | ID: covidwho-1808565

ABSTRACT

In this contribution, we present an innovative data-driven model to reconstruct a reliable temporal pattern for time-lagged statistical monetary figures. Our research cuts across several domains regarding the production of robust economic inferences and the bridging of top-down aggregated information from central databases with disaggregated information obtained from local sources or national statistical offices. Our test bed case study is the European Regional Development Fund (ERDF). The application we discuss deals with the reported time lag between the local expenditures of ERDF by beneficiaries in Italian regions and the corresponding payments reported in the European Commission database. Our model reconstructs the timing of these local expenditures by back-dating the observed European Commission reimbursements. The inferred estimates are then validated against the expenditures reported from the Italian National Managing Authorities (NMAs) in terms of cumulative monetary difference. The lower cumulative yearly distance of our modelled expenditures compared to the official European Commission payments confirms the robustness of our model. Using sensitivity analysis, we also analyse the relative importance of the modelling parameters on the cumulative distance between the modelled and reported expenditures. The parameters with the greatest influence on the uncertainty of this distance are the following: first, how the non-clearly regionalised expenditures are attributed to individual regions;and second, the number of backward years that the residuals of the yearly payments are spread onto. In general, the distance between the modelled and reported expenditures can be further reduced by fixing these parameters. However, the gain is only marginal for some regions. The present study paves the way for modelling exercises that are aimed at more reliable estimates of the expenditures on the ground by the ultimate beneficiaries of European funds. Additionally, the output databases can contribute to enhancing the reliability of econometric studies on the effectiveness of European Union (EU) funds.

15.
3rd International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2021 ; : 268-273, 2021.
Article in English | Scopus | ID: covidwho-1806953

ABSTRACT

In this paper, our group evaluates the effect of Covid-19 on the stock prices of the top 10 American airline companies and the NYSE Arca Airline Index using event study methodology. We accomplish this by comparing the Actual Returns and Expected Returns of an airline stock. We derive our Expected Return through a linear regression model between the airline stock returns and the market returns. We then subtract the Expected Return from the Actual Return to find the Abnormal Return. After that, we construct a confidence interval to test the significance of the Abnormal Return. If the Abnormal Return exceeds the confidence interval, we claim that Covid-19 had a significant effect on the stock price of our chosen Airlines. Our group's results showed that Covid-19 had a significant impact on the airline industry. We also looked at the impact of government-issued relief and mass vaccination, and we saw that airline stock prices recovered slowly but steadily. © 2021 IEEE.

16.
Studies in Economics and Finance ; 39(3):458-470, 2022.
Article in English | ProQuest Central | ID: covidwho-1806877

ABSTRACT

Purpose>The purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms.Design/methodology/approach>The data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking.Findings>Among the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns.Originality/value>This study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.

17.
Business Process Management Journal ; 28(1):273-292, 2022.
Article in English | ProQuest Central | ID: covidwho-1806789

ABSTRACT

Purpose>This paper aims to introduce the goal-oriented requirements extraction approach (GOREA). It is an elicitation approach that uses, specifically, healthcare business goals to derive the requirements of e-health system to be developed.Design/methodology/approach>GOREA consists of two major phases: (1) modelling e-health business requirements phase and (2) modelling e-health information technology (IT) and systems requirements phase. The modelling e-health business requirements phase is divided into two main stages: (1) model e-health business strategy stage and (2) model e-health business environment stage. The modelling e-health IT and systems requirements phase illustrates the process of obtaining requirements of e-health system from the organizational goals that are determined in the previous phase. It consists of four main steps that deal with business goals of e-health system: (1) modelling e-health business process (BP) step;(2) modelling e-health business goals step;(3) analysing e-health business goals step;and (4) eliciting e-health system requirements step. A case study based on the basic operations and services in hospital emergency unit for checking patient against COVID-19 virus and taking its diagnostic testing has been set and used to examine the validity of the proposed approach by achieving the conformance of the developed system to the business goals.Findings>The results indicate that (1) the proposed GOREA has a positive influence on the system implementation according to e-health business expectations;and (2) it can successfully fulfil the need of e-health business in order to save the citizens life by checking them against COVID-19 virus.Research limitations/implications>The proposed approach has some limitations. For example, it is only validated using one e-health business goal and thus it has to be authenticated with different e-health business goals in order to address different e-health problems.Originality/value>Many e-health projects and innovations are not established based on robust system requirements engineering phase. In order to ensure the success delivery of e-health services, all characteristics of e-health systems and applications must be understood in terms of technological perspectives as well as the all system requirements.

18.
Aircraft Engineering and Aerospace Technology ; 93(9):1488-1501, 2021.
Article in English | ProQuest Central | ID: covidwho-1806782

ABSTRACT

Purpose>The purpose of this study is to provide an alternative graph-based airspace model for more effective free-route flight planning.Design/methodology/approach>Based on graph theory and available data sets describing airspace, as well as weather phenomena, a new FRA model is proposed. The model is applied for near to optimal flight route finding. The software tool developed during the study and complexity analysis proved the applicability and timed effectivity of the flight planning approach.Findings>The sparse bidirectional graph with edges connecting only (geographically) closest neighbours can naturally model local airspace and weather phenomena. It can be naturally applied to effective near to optimal flight route planning.Research limitations/implications>Practical results were acquired for one country airspace model.Practical implications>More efficient and applicable flight planning methodology was introduced.Social implications>Aircraft following the new routes will fly shorter trajectories, which positively influence on the natural environment, flight time and fuel consumption.Originality/value>The airspace model proposed is based on standard mathematical backgrounds. However, it includes the original airspace and weather mapping idea, as well as it enables to shorten flight planning computations.

19.
Proc Math Phys Eng Sci ; 478(2260):20220040, 2022.
Article in English | PubMed | ID: covidwho-1806777

ABSTRACT

COVID-19, the disease caused by the novel coronavirus 2019, has caused grave woes across the globe since it was first reported in the epicentre of Wuhan, Hubei, China, in December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that rendered more than 900 million people housebound for more than two months since the lockdown of Wuhan, and elsewhere, on 23 January 2020. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns across and within provinces, before and during the lockdown period. We calibrate movement flow between provinces with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicentre Hubei. Moreover, we show that synchronous lockdowns and consequent reduced mobility lag a certain time to elicit an actual impact on suppressing the spread. Such highly coordinated nationwide lockdowns, applied via a top-down approach along with high levels of compliance from the bottom up, are central to mitigating and controlling early-stage outbreaks and averting a massive health crisis.

20.
Int J Qual Health Care ; 2022.
Article in English | PubMed | ID: covidwho-1806424

ABSTRACT

BACKGROUND: Managing high levels of acute COVID-19 bed occupancy can affect the quality of care provided to both affected patients and those requiring other hospital services. Mass vaccination has offered a route to reduce societal restrictions while protecting hospitals from being overwhelmed. Yet, early in the mass vaccination effort, the possible impact on future bed pressures remained subject to considerable uncertainty. The aim of this study was to model the effect of vaccination on projections of acute and intensive care bed demand within a one million resident healthcare system located in South West England. METHODS: An age-structured epidemiological model of the Susceptible-Exposed-Infectious-Recovered (SEIR) type was fitted to local data up to the time of the study, in early March 2021. Model parameters and vaccination scenarios were calibrated through a system-wide multi-disciplinary working group, comprising public health intelligence specialists, healthcare planners, epidemiologists, and academics. Scenarios assumed incremental relaxations to societal restrictions according to the envisaged UK Government timeline, with all restrictions to be removed by 21 June 2021. RESULTS: Achieving 95% vaccine uptake in adults by 31 July 2021 would not avert a third wave in autumn 2021 but would produce a median peak bed requirement approximately 6% (IQR: 1% to 24%) of that experienced during the second wave (January 2021). A two-month delay in vaccine rollout would lead to significantly higher peak bed occupancy, at 66% (11% to 146%) of that of the second wave. If only 75% uptake was achieved (the amount typically associated with vaccination campaigns) then the second wave peak for acute and intensive care beds would be exceeded by 4% and 19% respectively, an amount which would seriously pressure hospital capacity. CONCLUSION: Modelling influenced decision making among senior managers in setting COVID-19 bed capacity levels, as well as highlighting the importance of public health in promoting high vaccine uptake among the population. Forecast accuracy has since been supported by actual data collected following the analysis, with observed peak bed occupancy falling comfortably within the inter-quartile range of modelled projections.

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