Reducing measles risk in Turkey through social integration of Syrian refugees

Abstract

Turkey hosts almost 3.5M refugees and has to face a humanitarian emergency of unprecedented levels. We use mobile phone data to map the mobility patterns of both Turkish and Syrian refugees, and use these patterns to build data-driven computational models for quantifying the risk of epidemics spreading for measles -a disease having a satisfactory immunization coverage in Turkey but not in Syria, due to the recent civil war -while accounting for hypothetical policies to integrate the refugees with the Turkish population. Our results provide quantitative evidence that policies to enhance social integration between refugees and the hosting population would reduce the transmission potential of measles by almost 50%, preventing the onset of widespread large epidemics in the country. Our results suggest that social segregation does not hamper but rather boosts potential outbreaks of measles to a greater extent in Syrian refugees but also in Turkish citizens, although to a lesser extent. This is due to the fact that the high immunization coverage of Turkish citizens can shield Syrian

Combining Telecom Data with Heterogeneous Data Sources for Traffic and Emission Assessments—An Agent-Based Approach

“…While numerous studies have demonstrated the value of leveraging telecom data in a variety of applications, reaching operation level is not near [10]. Those studies show the potential of telecom data in various domains, such as urban planning [11], pandemic monitoring of infectious diseases [12][13][14][15][16][17], human dynamics studies [18][19][20][21][22][23][24][25], socio-economic analysis [26][27][28], and disaster risk management [29][30][31]. In the context of traffic, studies have been focused on the assessment of traffic composition [32], travel speed and time duration [33], the number of passengers [34], origin-destination matrices [35][36][37], route choice [38,39], and mode of transport [40,41].…”

Impact of temporal scales and recurrent mobility patterns on the unfolding of epidemics

“…For this reason, epidemic models based on metapopulations have been continuously refined to bridge this gap between theory and agent-based simulations. Thus, early models considering agents as random walkers [23][24][25][26] have been improved to include more realistic situations such as the recurrent nature of human mobility [27][28][29][30][31][32] or the coexistence of dierent mobility patterns [33,34] within a population. In particular, recently we proposed a framework called MIR (movement-interaction-return) model [35] which allows the use of real data about the population distribution as well as their recurrent mobility patterns, enabling the assessment of the impact of daily movements on the spread of diseases across a given city.…”