Evidence map›Paper›PMID 36930340›Full record

ArticleBulletin of mathematical biology2023

COVID-19 Vaccination and Healthcare Demand.

Matthew I Betti, Amira Hassan Abouleish, Victoria Spofford, Cory Peddigrew, Alan Diener, Jane M Heffernan

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024
    Article
  5. 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

6 authors.

Matthew I BettiMathematics and Computer Science, Mount Allison University, Sackville, NB, Canada.
Amira Hassan AbouleishMathematics and Computer Science, Mount Allison University, Sackville, NB, Canada.
Victoria SpoffordHealth Canada, Ottawa, ON, Canada.
Cory PeddigrewHealth Canada, Ottawa, ON, Canada.
Alan DienerHealth Canada, Ottawa, ON, Canada.
Jane M HeffernanModelling Infection and Immunity Lab, Centre for Disease Modelling, Mathematics and Statistics, York University, Toronto, ON, Canada. jmheffer@yorku.ca.ORCID http://orcid.org/0000-0001-9502-1688

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the driving concerns during any epidemic is the strain on the healthcare system. As we have seen many times over the globe with the COVID-19 pandemic, hospitals and ICUs can quickly become overwhelmed by cases. While strict periods of public health mitigation have certainly helped decrease incidence and thus healthcare demand, vaccination is the only clear long-term solution. In this paper, we develop a two-module model to forecast the effects of relaxation of non-pharmaceutical intervention and vaccine uptake on daily incidence, and the cascade effects on healthcare demand. The first module is a simple epidemiological model which incorporates non-pharmaceutical intervention, the relaxation of such measures and vaccination campaigns to predict caseloads into the Fall of 2021. This module is then fed into a healthcare module which can forecast the number of doctor visits, the number of occupied hospital beds, number of occupied ICU beds and any excess demand of these. From this module, we can also estimate the length of stay of individuals in ICU. For model verification and forecasting, we use the four most populous Canadian provinces as a case study.

Indexed as

COVID-19CanadaCOVID-19 VaccinesHealth Services Needs and DemandHumansMathematical ConceptsModels, BiologicalPandemicsSARS-CoV-2VaccinationCOVID-19 VaccinesCOVID-19Healthcare demandPublic health mitigationVaccination

Identifiers

PMID36930340
PMCPMC10021065

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.