Mosquito-borne arboviruses pose a substantial and growing threat to public health, causing significant morbidity and economic burden worldwide. Due to climate change, globalisation, and ecological changes, several of these viruses are expected to trigger new waves of public health emergencies within the European Union (EU). Improving preparedness and response requires robust quantitative tools for risk assessment and decision support.

This PhD project focuses on modelling the transmission dynamics and risk of mosquito-borne arboviruses with the potential to invade the EU. The project has two main methodological components. First, the PhD candidate will develop a novel spatially explicit transmission model that captures geospatial heterogeneity and local transmission dynamics. Second, since the pathogens of interest are not yet established in the EU, the model will rely on proxy and indirect data sources, requiring the development of novel statistical inference methodologies capable of integrating heterogeneous data while appropriately quantifying uncertainty.

This PhD position is part of a larger interdisciplinary research project. The successful candidate will work in tight collaboration with a second PhD researcher at the Vrije Universiteit Brussel (VUB), who will focus on the development of novel multi-criteria reinforcement learning (RL) algorithms, and with the Università Campus Bio-Medico di Roma. Through this collaboration, strong methodological synergies will be created between infectious disease modelling, statistical inference, and decision-making under uncertainty, with the joint objective of deriving and communicating optimal control and mitigation strategies, such as vector control or surveillance interventions, to support evidence-based decision making by public health authorities.

For more information and to apply, please visit:
https://www.uhasselt.be/en/about-hasselt-university/working-at-hasselt-university/vacancies/detail/3475-phd-student-statistics-and-modelling-of-infectious-diseases-100