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

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 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.

Determinants of malaria infections among children in refugee settlements in Uganda during 2018–2019

“…Some studies in non-refugee settlements, however, indicate no association between outdoor cooking practices and malaria infections among children [ 48 ]. Nevertheless, epidemiological modelling [ 13 ] is required to better understand the relationships between cooking practices, cooking fuel emissions, mosquito activity and risk of malaria acquisition among children in refugee settlements. Such modelling can be vital in supporting appropriate malaria prevention messaging and evidence-based decision making in this context.…”

“…The high prevalence of malaria among the displaced populations in Africa constitutes an emerging challenge for humanitarian response [ 12 ]. Vector borne and other infectious diseases present many challenges in refugee settlements due to inequalities, limited access to healthcare services, and crowded environments which enable rapid disease spread [ 13 ]. The risk for malaria infections can increase among refugees especially when immunologically naive individuals with little or no prior malaria exposure move to areas of more intense transmission [ 11 ].…”

A Mixed-Method Approach to Determining Contact Matrices in the Cox’s Bazar Refugee Settlement

“…The venues are filled according to the probabilities described above, Eqs. (3,4) and we "measure" the total raw contacts ti j (see Algorithm 1 in Appendix F) in the simulation. To further insure the correct total expected attendance time at the virtual venues compared with the real world, we proportionally close venues to approximate their possible fractional opening times.…”

“…This work also therefore contributes to the global call to action laid out in prior work, which aims, among others, to develop new methods and mechanisms of data collection for modelling disease spread in refugee and IDP settlements 3 .…”

“…Specifically, we will focus on the use case of the Cox’s Bazar refugee settlement in Bangladesh. Epidemics in refugee and internally displaced person (IDP) settlements are commonplace and tend to spread rapidly 4 , and only very few models have been designed to simulate outbreaks in these unique environments and to inform public health decision-making 3 . Given the application domain, we believe this is not just an important area in which to contribute knowledge about disease spread patterns, but also a challenging test case which demonstrates the strengths of our methodology.…”