Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward.

Autor:	Aylett-Bullock J; UN Global Pulse, United Nations, New York, New York, USA joseph@unglobalpulse.org.; Institute for Data Science, Durham University, Durham, UK., Gilman RT; Centre for Crisis Studies and Mitigation, The University of Manchester, Manchester, UK.; Department of Earth and Environmental Sciences, The University of Manchester, Manchester, UK., Hall I; Centre for Crisis Studies and Mitigation, The University of Manchester, Manchester, UK.; Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK.; Department of Mathematics, The University of Manchester, Manchester, UK., Kennedy D; UK Public Health Rapid Support Team, London School of Hygiene & Tropical Medicine/Public Health England, London, UK., Evers ES; WHO Cox's Bazar Emergency Sub-Office, United Nations, Cox's Bazar, Bangladesh., Katta A; UN Global Pulse, United Nations, New York, New York, USA., Ahmed H; UNHCR Cox's Bazar Sub-Office, United Nations, Cox's Bazar, Bangladesh., Fong K; Department of Science, Technology, Engineering and Public Policy, University College London, London, UK., Adib K; WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt., Al Ariqi L; WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt., Ardalan A; WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt., Nabeth P; WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt., von Harbou K; WHO Cox's Bazar Emergency Sub-Office, United Nations, Cox's Bazar, Bangladesh., Hoffmann Pham K; UN Global Pulse, United Nations, New York, New York, USA.; Stern School of Business, New York University, New York City, New York, USA., Cuesta-Lazaro C; Institute for Data Science, Durham University, Durham, UK., Quera-Bofarull A; Institute for Data Science, Durham University, Durham, UK., Gidraf Kahindo Maina A; UNHCR Public Health Unit, United Nations, Cox's Bazar, Bangladesh., Valentijn T; OCHA Centre for Humanitarian Data, United Nations, The Hague, The Netherlands., Harlass S; UNHCR Public Health Unit, United Nations, Geneva, Switzerland., Krauss F; Institute for Data Science, Durham University, Durham, UK., Huang C; UNHCR Global Data Service, United Nations, Copenhagen, New York, USA., Moreno Jimenez R; UNHCR Innovation, United Nations, Geneva, Switzerland., Comes T; Faculty of Technology, Policy, and Management, Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands., Gaanderse M; Faculty of Technology, Policy, and Management, Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands., Milano L; OCHA Centre for Humanitarian Data, United Nations, The Hague, The Netherlands., Luengo-Oroz M; UN Global Pulse, United Nations, New York, New York, USA.
Jazyk:	angličtina
Zdroj:	BMJ global health [BMJ Glob Health] 2022 Mar; Vol. 7 (3).
DOI:	10.1136/bmjgh-2021-007822
Abstrakt:	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 at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks. Competing Interests: Competing interests: No competing interests expressed. (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu