Evidence map›Paper›PMID 33762253›Full record

ReviewBMJ global health2021

A participatory modelling approach for investigating the spread of COVID-19 in countries of the Eastern Mediterranean Region to support public health decision-making.

Keyrellous Adib, Penelope A Hancock, Aysel Rahimli, Bridget Mugisa, Fayez Abdulrazeq, Ricardo Aguas, Lisa J White, Rana Hajjeh, Lubna Al Ariqi, Pierre Nabeth

Open access · goldAbstract readReview
In one paragraph

Review in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 17% of its field
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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

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  16. SARS-CoV-2 transmission in opposition-controlled Northwest Syria: modeling pandemic responses during political conflict.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022
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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

10 authors at 2 institutions in 3 countries.

Keyrellous AdibWorld Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt adibk@who.int.ORCID 0000-0001-9995-8400
Penelope A HancockWorld Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt.
Aysel RahimliWorld Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt.
Bridget MugisaWorld Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt.
Fayez AbdulrazeqWorld Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt.
Ricardo AguasNuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK.ORCID 0000-0002-6507-6597
Lisa J WhiteNuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK.
Rana HajjehWorld Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt.
Lubna Al AriqiWorld Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt.
Pierre NabethWorld Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt.ORCID 0000-0002-1567-6252
World Health Organization Regional Office for the Eastern Mediterranean · EGUniversity of Oxford · GB

Funding

Medical Research Council MC_PC_19012Medical Research Council MR/R015600/1World Health Organization 001
6 · The paper itself

Abstract

Early on in the COVID-19 pandemic, the WHO Eastern Mediterranean Regional Office recognised the importance of epidemiological modelling to forecast the progression of the COVID-19 pandemic to support decisions guiding the implementation of response measures. We established a modelling support team to facilitate the application of epidemiological modelling analyses in the Eastern Mediterranean Region (EMR) countries. Here, we present an innovative, stepwise approach to participatory modelling of the COVID-19 pandemic that engaged decision-makers and public health professionals from countries throughout all stages of the modelling process. Our approach consisted of first identifying the relevant policy questions, collecting country-specific data and interpreting model findings from a decision-maker's perspective, as well as communicating model uncertainty. We used a simple modelling methodology that was adaptable to the shortage of epidemiological data, and the limited modelling capacity, in our region. We discuss the benefits of using models to produce rapid decision-making guidance for COVID-19 control in the WHO EMR, as well as challenges that we have experienced regarding conveying uncertainty associated with model results, synthesising and comparing results across multiple modelling approaches, and modelling fragile and conflict-affected states.

Indexed as

Decision MakingEpidemiologic MethodsPublic HealthCommunicable Disease ControlCOVID-19HumansMediterranean RegionPandemicsSARS-CoV-2COVID-19epidemiologyhealth policymathematical modellingpublic health

Identifiers

PMID33762253
PMCPMC7992384
OpenAlexW3131330833

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.