Dr. Amy Marie Campbell
Current Research Projects
Dengue forecasting using D-MOSS
Research Fellow at London School of Hygiene and Tropical Health
supported by the Medical Research Council
Developing computational disease forecasting systems for dengue to improve health systems preparedness for and response to outbreaks.
Previous Research Projects
Global Vibrio Dynamics & Climate Change
Post-doctoral Researcher in INSPIRE DTP
supported by the Natural Environmental Research Council [Grant number NE/S007210/1]
Cholera from Space
European Space Agency Climate Office
supported by ESA's Young Graduate Trainee programme and contributing to the UKRI-NERC Pathways Of Dispersal for Cholera and Solution Tools (PODCAST) project, the ESA-Future Earth PODCAST-DEMOnstrator project, the GEO Blue Planet Water-associated Diseases Working Group, the Future Earth Coasts , and Future Earth Health Knowledge-Action Network
Global epidemiology, ecology and evolution of Vibrio bacteria and water-borne diseases associated with climate change.
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Developing a novel workflow that is able to effectively integrate genomic and climate data to overcome methodological limitations that prevent explicit analysis of evolutionary drivers of climate change. Using such frameworks to improve our knowledge of Vibrio disease dynamics and the evolution, emergence, and dispersal of different genetic variants and subsequent epidemiological effects. Exploring the role of climate drivers within these dynamics and forecasting how future climate change might impact the future of Vibrio bacteria.
Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables.
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Working with Marie-Fanny Racault, from the Plymouth Marine Laboratory, we set out to explore whether we could forecast cholera outbreaks on the Indian coast, using the ESA Climate Office's satellite-derived environmental data, cholera outbreak surveillance data and machine learning. We created a model trained on some of the historic data and tested it on the remaining unseen data.
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The model was able to detect 89.5% of outbreaks in the test dataset. We also found that chlorophyll-a concentration – a pigment marker for phytoplankton presence – as well as salinity and temperature, were the strongest predictors of cholera outbreaks in our dataset.