Three Rohingya children overlooking the Cox's Bazar settlement, Bangladesh

Using computer simulations in refugee settlements:

Diving into the epidemic models that can teach us about the spread of disease and intervention planning

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Shelters in Cox's Bazar Settlement, Bangladesh

The spread of COVID-19 has presented many challenges to healthcare systems worldwide. In settlements for refugees and internally-displaced persons (IDPs), which often suffer from overcrowding and insufficient sanitation, the rapid spread of the pandemic presents a significant threat— as will any future health crises.

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For example, the Cox’s Bazar settlement in Bangladesh, the largest of its kind in the world, houses more than 44,000 people per square kilometer. This is one and a half times higher than the density of people in New York City.

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Medical personnel in blue scrubs and masks, standing outside a medical centre in the Cox's Bazar Settlement

To help inform epidemic response, public health officials, researchers, and scientists used mathematical modelling to test out how different public health interventions may affect the spread of the COVID-19 disease.

Our team used the Cox’s Bazar settlement as a case study.

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But how does a mathematical model like this work?

Agent-based modelling in settlementsA scene illustrating how agents move between locations in an epidemic model of a refugee/IDP settlement.Learning CenterShelterDistribution CenterLatrines

Mathematical models can be complex and highly detailed since they use many different data sources and approaches.

In what follows, we’ll describe how the team used agent-based modelling to simulate the spread of COVID-19.

In essence, we created a virtual world that mimics the way people move and what they do throughout the day, in order to understand how a virus can spread.

To do this, we used data to create a ‘digital twin’ of the settlement that reflects its geography and physical layout, as well as the density, age, sex, and family compositions of the people who live in it.

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Now let’s take a closer look at how this works and zoom into one of the shelters in our model's 'digital twin'.

Agent-based modelling in settlementsA scene illustrating how agents move between locations in an epidemic model of a refugee/IDP settlement.Learning CenterShelterDistribution CenterLatrines

This is a shelter where Abdul, a 35-year old man, lives.

Like in a typical shelter, Abdul shares it with his family as well as with another household, totalling 7 people under the same roof.

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These are some of the usual places where Abdul and others can go throughout the day.

In the agent-based model, we simulate their daily routines: how they interact with each other and where they go.

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Every few hours, Abdul goes from one location to another.

What Abdul does when he leaves his shelter is randomly assigned by the model, but he is more likely to go to certain locations based on data about what people of his age might do.

For example, here we see Abdul going to a distribution center to pick up food for his family.

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Many common areas, like distribution centers, latrines and washing facilities, are shared between multiple shelters.

Given the density and living conditions in the settlement, it is easy for one person who becomes infected to spread the virus to others who use the same facilities at the same time.

The likelihood of infecting others depends on the type of location, how infectious someone is, the length of time spent at the location, and a number of other factors

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What happens if Abdul catches COVID-19?

He could remain asymptomatic, or become symptomatic with symptoms that could worsen.

How the disease affects him depends on a number of factors, including his age, sex, and any pre-existing conditions he has.

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In the model, we keep track of what happens to Abdul, and others in this digital settlement, as time passes.

This allows us to ask questions such as...

Which groups of people are at the highest risk of infection?

(e.g. children or people who live in certain areas of the settlement?)

If there is a disease outbreak, which parts of the settlement could be affected the most?

What operational interventions could help curb the spread of the virus?

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To find out more about this agent-based model and how it was used in the Cox's Bazar settlement context, check out our project website, or our academic paper.