# Markov Chain Models of Refugee Migration Data

## TL;DR

This paper explores the use of Markov chain models to analyze refugee migration data, demonstrating improved data fit and efficiency over existing agent-based models in the context of the Burundi refugee crisis.

## Contribution

It introduces a Markov chain approach for refugee migration modeling, showing its advantages over agent-based models in accuracy and computational efficiency.

## Key findings

- Markov chain models better match refugee migration data.
- The approach is more computationally efficient.
- Application to Burundi data demonstrates practical utility.

## Abstract

The application of Markov chains to modelling refugee crises is explored, focusing on local migration of individuals at the level of cities and days. As an explicit example we apply the Markov chains migration model developed here to UNHCR data on the Burundi refugee crisis. We compare our method to a state-of-the-art `agent-based' model of Burundi refugee movements, and highlight that Markov chain approaches presented here can improve the match to data while simultaneously being more algorithmically efficient.