TY - JOUR
T1 - Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward
AU - Aylett-Bullock, Joseph
AU - Gilman, Robert Tucker
AU - Hall, Ian
AU - Kennedy, David
AU - Evers, Egmond Samir
AU - Katta, Anjali
AU - Ahmed, Hussien
AU - Fong, Kevin
AU - Comes, Tina
AU - Gaanderse, Mariken
AU - More Authors, null
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - epidemiology
KW - mathematical modelling
KW - refugee
KW - COVID-19
KW - Informal settlement
KW - Disaster Management
KW - Uncertainty
KW - Policy
UR - http://www.scopus.com/inward/record.url?scp=85127021630&partnerID=8YFLogxK
U2 - 10.1136/bmjgh-2021-007822
DO - 10.1136/bmjgh-2021-007822
M3 - Article
SN - 2059-7908
VL - 7
JO - BMJ Global Health
JF - BMJ Global Health
IS - 3
M1 - e007822
ER -