Evidence map›Paper›PMID 33643638›Full record

ArticleJournal of global health2021

Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19.

Houssein H Ayoub, Hiam Chemaitelly, Shaheen Seedat, Monia Makhoul, Zaina Al Kanaani, Abdullatif Al Khal, Einas Al Kuwari, Adeel A Butt, Peter Coyle, Andrew Jeremijenko and 10 more

Abstract read
In one paragraph

Article in Journal of global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 1 pooled it
–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

50 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023
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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

20 authors.

Houssein H AyoubDepartment of Mathematics, Statistics, and Physics, Qatar University, Doha, Qatar.
Hiam ChemaitellyInfectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar.
Shaheen SeedatInfectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar.
Monia MakhoulInfectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar.
Zaina Al KanaaniHamad Medical Corporation, Doha, Qatar.
Abdullatif Al KhalHamad Medical Corporation, Doha, Qatar.
Einas Al KuwariHamad Medical Corporation, Doha, Qatar.
Adeel A ButtDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, New York, USA.
Peter CoyleHamad Medical Corporation, Doha, Qatar.
Andrew JeremijenkoHamad Medical Corporation, Doha, Qatar.
Anvar Hassan KaleeckalHamad Medical Corporation, Doha, Qatar.
Ali Nizar LatifHamad Medical Corporation, Doha, Qatar.
Riyazuddin Mohammad ShaikHamad Medical Corporation, Doha, Qatar.
Hanan Abdul RahimCollege of Health Sciences, QU Health, Qatar University, Doha, Qatar.
Hadi M YassineBiomedical Research Center, Qatar University, Doha, Qatar.
Mohamed G Al KuwariPrimary Health Care Corporation, Doha, Qatar.
Hamad Eid Al RomaihiMinistry of Public Health, Doha, Qatar.
Mohamed H Al-ThaniMinistry of Public Health, Doha, Qatar.
Roberto BertolliniMinistry of Public Health, Doha, Qatar.
Laith J Abu RaddadInfectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMathematical modeling constitutes an important tool for planning robust responses to epidemics. This study was conducted to guide the Qatari national response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. The study investigated the epidemic's time-course, forecasted health care needs, predicted the impact of social and physical distancing restrictions, and rationalized and justified easing of restrictions.

methodsAn age-structured deterministic model was constructed to describe SARS-CoV-2 transmission dynamics and disease progression throughout the population.

resultsThe enforced social and physical distancing interventions flattened the epidemic curve, reducing the peaks for incidence, prevalence, acute-care hospitalization, and intensive care unit (ICU) hospitalizations by 87%, 86%, 76%, and 78%, respectively. The daily number of new infections was predicted to peak at 12 750 on May 23, and active-infection prevalence was predicted to peak at 3.2% on May 25. Daily acute-care and ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number

conclusionsUse of modeling and forecasting to guide the national response proved to be a successful strategy, reducing the toll of the epidemic to a manageable level for the health care system.

Indexed as

Models, TheoreticalCOVID-19EpidemicsForecastingHospitalizationHumansIncidenceIntensive Care UnitsPrevalenceQatar

Identifiers

PMID33643638
PMCPMC7897910

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.