Post-Acute Infectious Syndromes (PAIS) refer to the symptoms persisting months after initial infection. Clinical research studies on this topic often collect rich, multi-modal datasets. Yet, the complexity of the datasets and the lack of a precise clinical case definition pose difficulties in creating comprehensive analyses. Here, we present a generalisable framework for analysing data from longitudinal studies of PAIS using Latent Transition Analysis (LTA). It enables the identification of disease phenotypes and the patient-level analysis of transitions between them, without relying on predefined clinical categorisations. Furthermore, we introduce a method for incorporating covariate information, which enables exploration of how patient characteristics influence disease trajectories. We apply this methodology to the ORCHESTRA dataset, composed of individuals affected by SARS-CoV-2 infection from multiple European centres, for investigation into Post-COVID-19 condition (PCC). 5094 patient assessments were collected at SARS-CoV-2 infection, and at 6, 12, 18, and 24 months of follow-up. Our model identifies distinct PCC phenotypes with patient trajectories impacted by age and sex. Our results highlight how LTA can enhance the interpretability of complex, time-resolved clinical data, support personalized patient monitoring and management, and accelerate therapeutic development for other PAISs, too.