Contact person: Geir Kjetil Ferkingstad Sandve
Keywords: Climate and health, Time series, Deep learning, Global health, Machine learning software
Research groups: Scientific Computing and Machine Learning (SCML)
Department of Informatics
It is well established that climate change will have a range of direct and indirect effects on global health. An example is how precipitation and temperature affects mosquito populations and the incidence of a disease like Malaria. Precise quantitative models could allow early warning of epidemics and predict spatial expansion of disease, facilitating mitigation through improved resource allocation and interventions. The relations between climate and health are, however, highly complex and varying.We are interested in the development of improved machine learning methodology for predicting future disease incidence informed by climate projections, providing modeling-based decision support, or predicting the effects of interventions designed to reduce disease burden. Methodology ranging from classic time series forecasting to tailored deep learning models is of interest, as well as development of open-source platforms to improve transparency, reproducibility and software reuse. The theme is connected to a large, transdisciplinary climate and health collaboration at UiO.
Topics from methodological research:
- Time series forecasting
- Deep Learning for Time Series Analysis
- Geospatial Machine Learning
- Machine learning platform/framework design
Topics from natural sciences or technology:
- Climate science
- Global health
- Climate change preparedness
External partners:
- The Norwegian Computing Center (NR)
- The Norwegian Meteorological Institute (met.no)
- SINTEF