Evidence map›Paper›PMID 34710090›Full record

ArticlePLoS computational biology2021

Operational response simulation tool for epidemics within refugee and IDP settlements: A scenario-based case study of the Cox's Bazar settlement.

Joseph Aylett-Bullock, Carolina Cuesta-Lazaro, Arnau Quera-Bofarull, Anjali Katta, Katherine Hoffmann Pham, Benjamin Hoover, Hendrik Strobelt, Rebeca Moreno Jimenez, Aidan Sedgewick, Egmond Samir Evers and 5 more

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 21% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Bayesian emulation and history matching of JUNE.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  5. Article
  6. 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

15 authors at 5 institutions in 3 countries.

Joseph Aylett-BullockUnited Nations Global Pulse, New York, New York, United States of America.ORCID 0000-0001-7551-3423
Carolina Cuesta-LazaroInstitute for Data Science, Durham University, Durham, United Kingdom.
Arnau Quera-BofarullInstitute for Data Science, Durham University, Durham, United Kingdom.ORCID 0000-0001-5055-9863
Anjali KattaUnited Nations Global Pulse, New York, New York, United States of America.ORCID 0000-0003-2576-8609
Katherine Hoffmann PhamUnited Nations Global Pulse, New York, New York, United States of America.ORCID 0000-0002-1022-6025
Benjamin HooverMIT-IBM Watson AI Lab, Cambridge, Massachusetts, United States of America.ORCID 0000-0001-5218-3185
Hendrik StrobeltMIT-IBM Watson AI Lab, Cambridge, Massachusetts, United States of America.
Rebeca Moreno JimenezUNHCR Innovation, Geneva, Switzerland.ORCID 0000-0003-0864-5594
Aidan SedgewickInstitute for Data Science, Durham University, Durham, United Kingdom.
Egmond Samir EversWHO Emergency Sub-Office, Cox's Bazar, Bangladesh.
David KennedyUK Public Health Rapid Support Team, Public Health England/London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0003-0004-6167
Sandra HarlassUNHCR Public Health Unit, Geneva, Switzerland.
Allen Gidraf Kahindo MainaUNHCR Public Health Unit, Cox's Bazar, Bangladesh.ORCID 0000-0002-7995-5445
Ahmad HussienUNHCR Information Management Unit, Cox's Bazar, Bangladesh.ORCID 0000-0003-4143-3833
Miguel Luengo-OrozUnited Nations Global Pulse, New York, New York, United States of America.ORCID 0000-0002-8694-2001
United Nations · USDurham University · GBIBM (United States) · USOffice of the United Nations High Commissioner for Refugees · CHPublic Health England · GB

Funding

World Health Organization 001
6 · The paper itself

Abstract

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world's most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox's Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable.

Indexed as

EpidemicsRefugeesSARS-CoV-2BangladeshComorbidityComputational BiologyComputer SimulationCOVID-19Data VisualizationDisease ProgressionHumansMasksPhysical DistancingSchoolsSystems Analysis

Identifiers

PMID34710090
PMCPMC8553081
OpenAlexW3208550367

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.