Evaluating intervention strategies in controlling coronavirus disease 2019 (COVID-19) spread in care homes: An agent-based model

Nguyen, Le Khanh Ngan; Howick, Susan; McLafferty, Dennis; Anderson, Gillian; Pravinkumar, Josephine; Meer, R.B. Van Der; Megiddo, Itamar

doi:10.1017/ice.2020.1369

Cited by 42 publications

(54 citation statements)

References 42 publications

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“…A report by Nyguen et al [7] uses an ABM to investigate the impact on care home residents of various vaccine coverage, and reducing the weekly testing of staff. However, the models in [6,7] do have an external force of infection (FOI) from the community, based on prevalence data, representing August 24, 2021 2/23…”

Section: Introductionmentioning

confidence: 99%

“…These models [5][6][7] do not assess the relative impact of the different COVID-19 pathways into care homes. Nguyen et al [8] extend [6,7], using a hybrid ABM-System Dynamics model, to explore the conditions under which visitation, heterogeneous care homes sizes, and the cohorting of residents impacts COVID-19 outbreak severity. Rosello et al [9] model an individual care home with a stochastic compartmental model, using multiple FOI's to capture COVID-19 pathways, including visitors, hospital discharges, staff working at other homes, and staff infections from the community.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 in Scottish care homes: A metapopulation model of spread among residents and staff

Baister

McTaggart

McMenemy

et al. 2021

Preprint

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Care homes in the UK were disproportionately affected by the first wave of the COVID–19 pandemic, accounting for almost half of COVID–19 deaths over the course of the period from 6th March – 15th June 2020. Understanding how infectious diseases establish themselves throughout vulnerable communities is crucial for minimising deaths and lowering the total stress on the National Health Service (NHS Scotland). We model the spread of COVID–19 in the health–of NHS Lothian, Scotland over the course of the first wave of the pandemic with a compartmental Susceptible – Exposed – Infected reported – Infected unreported – Recovered – Dead (SEIARD), metapopulation model. Care home residents, care home workers and the rest of the population are modelled as subpopulations, interacting on a network describing their mixing habits. We explicitly model the outbreak′s reproduction rate and care home visitation level over time for each subpopulation, and execute a data fit and sensitivity analysis, focusing on parameters responsible for intra–subpopulation mixing: staff sharing, staff shift patterns and visitation. The results suggest that hospital discharges were not predominantly responsible for the early outbreak in care homes, and that only a few such cases led to infection seeding in care homes by the 6th of March Sensitivity analysis show the main mode of entry into care homes are infections by staff interacting with the general population. Visitation (before cancellation) and staff sharing were less significant in affecting outbreak size. Focusing on the protection and monitoring of staff, followed by reductions in staff sharing and quick cancellations of visitation can significantly reduce future infection attack rates of COVID–19 in care homes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

COVID-19 in Scottish care homes: A metapopulation model of spread among residents and staff

Baister

McTaggart

McMenemy

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…But because the number of infections is unknown in the real world, the usefulness for policymakers of these studies is limited. Five studies [28][29][30][31][32] were simulations of specific environments (a university campus, care homes, and Dane County in the USA, and the USA); thus, the generalisability of their findings is limited. Three studies 16 26 33 focused on policies of testing and tracing.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

“…In contrast to models that are suitable only for specific regions and conditions, [28][29][30][31][32] our tool has potential to be used for various population sizes and is generalisable to different types of communities. The novelty of this method is reflected in the model's structure and scenario design.…”

Section: Principal Findingsmentioning

confidence: 99%

Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study

Hu¹,

Jian-ying²,

et al. 2021

BMJ Open

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ObjectivesThis study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated.SettingWe developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a ‘T’ compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed.Primary and secondary outcome measuresSimulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people.Results(1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate.ConclusionsReducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies.

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“…While most modeling endeavors have not necessarily discussed artificial intelligence applications to school testing, a few have conversed with pertaining concepts such as optimization towards desired performance means [73] and rule-based models [79][80][81][82].…”

Section: Ai In Simulation Modeling For School Testing Scenariosmentioning

confidence: 99%

Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

et al. 2021

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Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.

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Evaluating intervention strategies in controlling coronavirus disease 2019 (COVID-19) spread in care homes: An agent-based model

Cited by 42 publications

References 42 publications

COVID-19 in Scottish care homes: A metapopulation model of spread among residents and staff

COVID-19 in Scottish care homes: A metapopulation model of spread among residents and staff

Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study

Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

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