Evidence map›Paper›PMID 39857491›Full record

ArticleInternational journal of environmental research and public health2024

Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling.

Yuan Tian, Jenny Basran, Wade McDonald, Nathaniel D Osgood

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuan TianDepartment of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada.ORCID 0000-0002-7717-2949
Jenny BasranSaskatchewan Health Authority, Saskatoon, SK S7K 0M7, Canada.
Wade McDonaldDepartment of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada.ORCID 0000-0003-4763-3414
Nathaniel D OsgoodDepartment of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada.ORCID 0000-0002-3994-3298

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We engaged with health sector stakeholders and public health professionals within the health system through a participatory modeling approach to support policy-making in the early COVID-19 pandemic in Saskatchewan, Canada. The objective was to use simulation modeling to guide the implementation of public health measures and short-term hospital capacity planning to mitigate the disease burden from March to June 2020. We developed a hybrid simulation model combining System Dynamics (SD), discrete-event simulation (DES), and agent-based modeling (ABM). SD models the population-level transmission of COVID-19, ABM simulates individual-level disease progression and contact tracing intervention, and DES captures COVID-19-related hospital patient flow. We examined the impact of mixed mitigation strategies-physical distancing, testing, conventional and digital contact tracing-on COVID-19 transmission and hospital capacity for a worst-case scenario. Modeling results showed that enhanced contact tracing with mass testing in the early pandemic could significantly reduce transmission, mortality, and the peak census of hospital beds and intensive care beds. Using a participatory modeling approach, we not only directly informed policy-making on contact tracing interventions and hospital surge capacity planning for COVID-19 but also helped validate the effectiveness of the interventions adopted by the provincial government. We conclude with a discussion on lessons learned and the novelty of our hybrid approach.

Indexed as

COVID-19HospitalsPandemicsPublic HealthComputer SimulationContact TracingHumansModels, TheoreticalPandemic PreparednessSARS-CoV-2SaskatchewanSurge Capacityagent-based modelingcontact tracingCOVID-19discrete-event simulationhospital capacity planninghybrid simulationpandemic preparednessparticipatory modelingsystem dynamics

Identifiers

PMID39857491
PMCPMC11764793

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.