Francesco Di Lauro
Postdoctoral Researcher
Infectious Disease Modeller
I am a modeller with a background in Theoretical Physics and a PhD in Applied Maths. During my PhD, I worked on models for epidemic spreading on networks. I have also acquired experience in coding (mainly C, R, Python) and Statistics. You can find a list of my publications here.
I joined the group of Christophe Fraser at the Pandemic Sciences Institute as a Postdoc in 2022.
My research centres on HIV epidemiology in sub-Saharan Africa and its structural difference with the epidemics in Europe and North America, combining large-scale trial and surveillance data with mechanistic models to understand how sexual partnership networks shape transmission and to evaluate interventions. In particular, I work on quantitative analyses of data from HPTN 071 (PopART) trial within the [PANGEA consortium] collaboration, and on the development and use of individual-based modelling tools for HIV.
A recurring theme of my work is connecting rich empirical data to transmission dynamics: identifying the network structures and “hard-to-reach” sub-populations that disproportionately contribute to incidence, and quantifying how targeted strategies compare to broader approaches. Methodologically, my work spans statistical modelling, scalable scientific software (C/R/Python), and integrating phylogenetic information with epidemic models to improve reconstruction of transmission patterns.
Recent publications
Large connected components in sexual networks and their role in HIV transmission in Sub-Saharan Africa: A model-based analysis of HPTN 071(PopART) data
Journal article
Di Lauro F. et al, (2025), Journal of Theoretical Biology, 613, 112218 - 112218
Drivers of epidemic dynamics in real time from daily digital COVID-19 measurements
Journal article
Kendall M. et al, (2024), Science, 385
Biased estimates of phylogenetic branch lengths resulting from the discretised Gamma model of site rate heterogeneity
Preprint
Ferretti L. et al, (2024)
Digital measurement of SARS-CoV-2 transmission risk from 7 million contacts
Journal article
Ferretti L. et al, (2024), Nature, 626, 145 - 150
Enhancing global preparedness during an ongoing pandemic from partial and noisy data.
Journal article
Klamser PP. et al, (2023), PNAS nexus, 2