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Original research
Modelling interventions to control COVID-19 outbreaks in a refugee camp
  1. Robert Tucker Gilman1,2,
  2. Siyana Mahroof-Shaffi3,
  3. Christian Harkensee4,
  4. Andrew T Chamberlain2
  1. 1Centre for Crisis Studies and Mitigation, The University of Manchester, Manchester, UK
  2. 2Department of Earth and Environmental Sciences, The University of Manchester, Manchester, UK
  3. 3Kitrinos Healthcare, Lesbos, Greece
  4. 4Department of Paediatrics, Queen Elizabeth Hospital Gateshead, Gateshead, UK
  1. Correspondence to Dr Robert Tucker Gilman; tucker.gilman{at}manchester.ac.uk

Abstract

Background In the absence of effective treatments or vaccines, non-pharmaceutical interventions are the mainstay of control in the COVID-19 pandemic. Refugee populations in displacement camps live under adverse conditions that are likely to favour the spread of disease. To date, only a few cases of COVID-19 have appeared in refugee camps, and whether feasible non-pharmaceutical interventions can prevent the spread of the SARS-CoV-2 virus in such settings remains untested.

Methods We constructed the first spatially explicit agent-based model of a COVID-19 outbreak in a refugee camp, and applied it to evaluate feasible non-pharmaceutical interventions. We parameterised the model using published data on the transmission rates and progression dynamics of COVID-19, and demographic and spatial data from Europe’s largest refugee camp, the Moria displacement camp on Lesbos, Greece. We simulated COVID-19 epidemics with and without four feasible interventions.

Results Spatial subdivision of the camp (‘sectoring’) was able to ‘flatten the curve’, reducing peak infection by up to 70% and delaying peak infection by up to several months. The use of face masks coupled with the efficient isolation of infected individuals reduced the overall incidence of infection, and sometimes averted epidemics altogether. These interventions must be implemented quickly in order to be maximally effective. Lockdowns had only small effects on COVID-19 dynamics.

Conclusions Agent-based models are powerful tools for forecasting the spread of disease in spatially structured and heterogeneous populations. Our findings suggest that feasible interventions can slow the spread of COVID-19 in a refugee camp setting, and provide an evidence base for camp managers planning intervention strategies. Our model can be modified to study other closed populations at risk from COVID-19 or future epidemics.

  • public health
  • epidemiology
  • respiratory infections
  • other study design
  • control strategies
https://creativecommons.org/licenses/by/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

https://doi.org/10.1136/bmjgh-2020-003727

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    Footnotes

    • Handling editor Seye Abimbola

    • Twitter @GilmanTucker

    • Contributors All authors conceptualised the study. RTG built the model and analysed the data, with advice from all authors. SM-S and CH provided information about the Moria refugee camp. All authors wrote the paper.

    • Funding RTG was partially supported by Engineering and Physical Sciences Research Council grant number EP/M506436.

    • Competing interests RTG reports grants from the European Union’s Horizon 2020 research and innovation programme outside the submitted work. ATC reports grants from NERC and AHRC outside the submitted work. SM-S is the director and CH is a board member (both without financial interest) of the NGO Kitrinos Healthcare (a UK registered charity providing free healthcare inside the Moria camp).

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available in a public, open access repository. Codes used in this study are available at: https://github.com/TuckerGilman/Moria.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.