Modelling interventions to control COVID-19 outbreaks in a refugee camp

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• Non-pharmaceutical interventions (NPIs) are critical for COVID-19 control due to limited treatments and vaccines.
• Refugee camps present high-risk environments for infectious disease transmission.
• The effectiveness of NPIs in refugee settings remains largely unquantified.

Purpose of the Study:

• To evaluate the feasibility and impact of NPIs on COVID-19 outbreaks in refugee camps.
• To develop and apply a spatially explicit agent-based model for simulating disease spread in such settings.

Main Methods:

• Construction of a spatially explicit agent-based model for COVID-19.
• Parameterization using published COVID-19 data and data from Moria refugee camp, Greece.
• Simulation of epidemics with and without four distinct NPIs.

Main Results:

• Camp spatial subdivision ('sectoring') reduced peak infections by up to 70% and delayed them by months.
• Face masks combined with isolation of infected individuals significantly reduced incidence and averted some epidemics.
• Lockdowns demonstrated minimal impact on COVID-19 dynamics within the simulated camp.

Conclusions:

• Agent-based models are effective for forecasting disease spread in complex populations.
• Feasible NPIs can effectively mitigate COVID-19 transmission in refugee camps.
• Findings provide an evidence base for public health interventions in similar high-risk, closed populations.