Give decision-makers better ways to weigh the risks and benefits of the food people eat.
I aim to develop and apply methods for measuring risks and benefits of foodborne exposure in the Finnish population, supporting more robust, evidence-based food regulation and methodological advancement. This addresses a social and economic challenge: regulation must reduce preventable foodborne illness while preserving food’s health benefits amid uncertainty. To meet this objective, I use probabilistic methods and Bayesian inference to characterize the magnitude and uncertainty of concentrations of foodborne agents, food consumption, and other exposure factors. I develop Bayesian benchmark dose modeling and integrate it with probabilistic exposure assessment. I then quantify and aggregate beneficial and adverse effects of food consumption using Value of Statistical Life and Disability-Adjusted Life Years, enabling comparison across foodborne exposures and prioritization of food regulation.
