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


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
10 2021
Historique:
received: 06 03 2021
accepted: 18 08 2021
entrez: 28 10 2021
pubmed: 29 10 2021
medline: 5 11 2021
Statut: epublish

Résumé

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.

Identifiants

pubmed: 34710090
doi: 10.1371/journal.pcbi.1009360
pii: PCOMPBIOL-D-21-00426
pmc: PMC8553081
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1009360

Subventions

Organisme : World Health Organization
ID : 001
Pays : International

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Joseph Aylett-Bullock (J)

United Nations Global Pulse, New York, New York, United States of America.
Institute for Data Science, Durham University, Durham, United Kingdom.

Carolina Cuesta-Lazaro (C)

Institute for Data Science, Durham University, Durham, United Kingdom.

Arnau Quera-Bofarull (A)

Institute for Data Science, Durham University, Durham, United Kingdom.

Anjali Katta (A)

United Nations Global Pulse, New York, New York, United States of America.

Katherine Hoffmann Pham (K)

United Nations Global Pulse, New York, New York, United States of America.
New York University Stern School of Business, New York, New York, United States of America.

Benjamin Hoover (B)

MIT-IBM Watson AI Lab, Cambridge, Massachusetts, United States of America.

Hendrik Strobelt (H)

MIT-IBM Watson AI Lab, Cambridge, Massachusetts, United States of America.

Rebeca Moreno Jimenez (R)

UNHCR Innovation, Geneva, Switzerland.

Aidan Sedgewick (A)

Institute for Data Science, Durham University, Durham, United Kingdom.

Egmond Samir Evers (E)

WHO Emergency Sub-Office, Cox's Bazar, Bangladesh.

David Kennedy (D)

UK Public Health Rapid Support Team, Public Health England/London School of Hygiene and Tropical Medicine, London, United Kingdom.

Sandra Harlass (S)

UNHCR Public Health Unit, Geneva, Switzerland.

Allen Gidraf Kahindo Maina (A)

UNHCR Public Health Unit, Cox's Bazar, Bangladesh.

Ahmad Hussien (A)

UNHCR Information Management Unit, Cox's Bazar, Bangladesh.

Miguel Luengo-Oroz (M)

United Nations Global Pulse, New York, New York, United States of America.

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Classifications MeSH