Data i lloc: Dimarts 02 de juny de 2026 · 12:00 h · Seminari del Departament d'Estadística i Investigació Operativa de la Facultat de Ciències Matemàtiques.

Resum: This research proposes a two-step statistical approach to better understand disease recurrence, such as reinfection or relapse. In the first step we use multistate models (MSM) to capture the full, complex trajectory of a primary infection, which accounts for all significant health factors and transitions an individual experiences before a potential recurrence occurs.

The second part of the work focuses on the specific risk of recurrence after a fixed point in time, known as a landmark time. It treats death as a competing risk, considering that some patients are no longer at risk of reinfection. Several approaches are proposed and compared, all assuming a Cox model for cause-specific hazards and introducing all baseline and time-dependent covariates at the landmark time.

The proposal is applied to a massive dataset of 400,000 COVID-19 cases in the Basque Country. While the study focused on reinfection, the methodology is designed to be a versatile tool for other acute or chronic illnesses.

This is a joint work with G. Gómez-Mellis and K. Langohr from the Universitat Politécnica de Catalunya.