Projects / P8

Uni Klinikum Heidelberg
CRSN

P8: Drivers, patterns and health consequences of mobility responses - building a simulation tool for anticipatory climate action

Climate change is increasingly recognized as a significant driver of human migration, particularly in regions where agricultural livelihoods are heavily dependent on weather conditions. In Sub-Saharan Africa, communities face rising temperatures, erratic rainfall, and worsening crop yields, all of which threaten food security and economic stability. In rural Burkina Faso, where populations are already vulnerable due to limited adaptive capacity, declines in crop productivity are leading to increased migration.

While migration can be an adaptive response to environmental change and climate change, migration can also expose these populations to additional health risks, including poor living conditions, infectious diseases, and limited access to healthcare services. To date, few studies have looked longitudinally at mobility patterns in relation to changing environmental patterns and few have included analysis of the distance, duration, and reasons for migration. By leveraging Health and Demographic Surveillance System (HDSS) data, and overlaying climate date, migration data and health data, our research seeks to understand the complex relationship between climate variability and migration patterns, aiming to better predict how climate change influences human mobility.

We seek to identify, measure and articulate exposure-response relationships between climate change variables and migration patterns. Our long-term goal is to implement advanced mathematical models to explore migration patterns linked to climate change. By analyzing HDSS data over time, we can identify the key factors that trigger migration and assess how these may evolve under different climate scenarios. This approach could enable anticipatory action, allowing humanitarian and health organizations to implement timely interventions that reduce the health risks associated with climate-induced migration. For instance, early warning systems based on migration patterns could guide resource allocation to areas most likely to experience movement, thus improving preparedness and resilience in vulnerable communities across Sub-Saharan Africa.

Principal Investigator (PI):
Prof. Joacim Rocklöv
Heidelberg Institute of Global Health (HIGH)
Universitätsklinikum Heidelberg
Heidelberg, Germany
E-mail: joacim.rockloev@uni-heidelberg.de

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