Dr Claudio Fronterre PhD

• PhD in Statistical Science, University of Padua, 2018
• MSc in Finance, University of Trento, 2014
• BSc in Economics and Management, University of Trento, 2012

Dr Claudio Fronterre is a statistical scientist specialising in spatial and spatio-temporal modelling for global health, with a particular focus on neglected tropical diseases and other health challenges in low-resource settings. His research develops geostatistical methods to improve disease surveillance, quantify disease burden and support programmatic decision-making. He has led several internationally funded projects as Principal Investigator and collaborates with global partners to ensure that statistical innovation informs real-world health policy.

Claudio has created and delivered MSc-level modules in statistical modelling and epidemiology, and he has 

developed and taught international training courses in model-based geostatistics for diverse audiences, including health professionals, national programme staff and academic researchers. He adopts a research-led and inclusive approach to teaching, integrating practical applications and reproducible analytical tools to support students from a wide range of academic and professional backgrounds.

He supervises MSc and PhD students as well as postdoctoral researchers working across interdisciplinary areas in spatial statistics and disease mapping.

Claudio’s work reflects a broader commitment to capacity building, equity in global health and the role of statistical science in advancing evidence-based policy and sustainable public health impact. His academic career is grounded in the belief that rigorous statistical modelling, when combined with open and collaborative partnerships, can substantially improve disease control programmes and strengthen decision-making in settings where data and resources are limited.

Claudio welcomes PhD applications from motivated students interested in statistical modelling for global public health, particularly in:

• Spatialand spatio-temporal statistics: including model-based geostatistics and hierarchical modelling;
• Epidemiology of neglected tropical diseases (NTDs): including methods for disease mapping, surveillance, and impact evaluation of control programmes;
• Statistical and computational methods for infectious disease modelling: such as integration of heterogeneous data sources, small-area estimation, and uncertainty quantification;
• Decision-support tools and data-driven approaches for health policy in low-resource settings.

I am particularly keen to supervise projects that combine advanced statistical modelling with real-world applications in global health and epidemiology.

Claudio’s research focuses on the development and application of advanced statistical methods to address global public health challenges. His main areas of interest include:

• Spatial and spatio-temporal statistical modelling for global health, with an emphasis on model-based geostatistics approaches for disease surveillance and burden estimation.
• Mapping and modelling neglected tropical diseases (NTDs) to support control and elimination strategies.
• Survey design and surveillance systems, particularly the optimisation of sampling strategies, impact monitoring and the use of data-driven approaches for decision-making in low-resource settings.
• Statistical and computational methods for infectious disease modelling, including small-area estimation, uncertainty quantification and integration of heterogeneous data sources.
• Open-source statistical software and global capacity building: promoting reproducibility, accessibility and the development of analytical skills within national public health programmes.

Founder and Director of Kuro Neko Analytics, a consultancy providing statistical modelling and decision-support expertise for global health organisations (2024–present).