Kai Zhu (School for Environment and Sustainability) and Kerby Shedden (Statistics)
Bayesian modeling of multi-source phenology to forecast airborne allergen concentration
We aim to improve the short-term and long-term predictions of airborne allergens under climate change, an emerging public health concern. To achieve this, we propose to develop novel data science tools to effectively assimilate multiple data sources and integrate various data-driven and process-based models. Beyond innovative methodology, our project also advances the biological understanding of pollen and fungal spores, and ultimately, our work helps alleviate the impacts of airborne allergens on people’s health.
