Imperial College London


Faculty of MedicineSchool of Public Health

Research Fellow



+44 (0)20 7594 1451i.siveroni




Norfolk PlaceSt Mary's Campus





Development of software for the analysis and interpretation of genetic sequence data from important pathogens such as HIV, Ebola virus and Influenza.

I have developed PhyDyn, a BEAST 2.5 (Bayesian Evolutionary Analysis by Sampling Trees) module/plug-in that implements coalescent models for mulit-deme populations with nonlinear dynamics, based on the theory and methods initially presented in Complex population dynamics and the coalescent under neutrality (Volz EM, 2012, Genetics, Vol:190), and extended in Bayesian Phylodynamic Inference with Complex Models (Volz EM and Siveroni I, 2018, PLOS Computational Biology).

PhyDyn: Epidemiological modelling in BEAST.

PhyDyn is a BEAST2 package for performing Bayesian phylogenetic inference under models that deal with structured populations with complex population dynamics. This package enables simultaneous estimation of epidemiological parameters and pathogen phylogenies.

PhyDyn implements a structured coalescent model for a large class of epidemic processes specified by a deterministic nonlinear dynamical system, and computes the log-likelihood of a gene genealogy conditional on a complex demographic history. Genealogies are specified as timed phylogenetic trees in which lineages are associated with the distinct subpopulation in which they are sampled. Epidemic models are defined by a series of ordinary differential equations (ODEs) specifying the rates that new lineages introduced in the population (birth matrix) and the rates at which migrations, or transition between states occur (migration matrix).

The package's underlying coalescent model (where birth and migration rates are a function of time and the underlying population dynamics) and rich ODE syntax (polynomials, timed conditional expressions, trigonometric functions) enables the specification and implementation of a large class of epidemiological processes. The framework can be applied to models with spatial structure, multiple stages of infections and models of vector-borne diseases and other multi-host pathogens.

Documentation and source available here.

Poster presentation at the MIDAS Annual Networking meeting (2017).

Selected Publications

Journal Articles

Bouckaert R, Vaughan TG, Barido-Sottani J, et al., 2019, BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis, PLOS Computational Biology, Vol:15, ISSN:1553-734X, Pages:1-28

Volz EM, Siveroni I, 2018, Bayesian phylodynamic inference with complex models, PLOS Computational Biology, Vol:14, ISSN:1553-734X

Siveroni I, Zisman A, Spanoudakis G, 2010, A UML-based static verification framework for security, Requirements Engineering, Vol:15, ISSN:0947-3602, Pages:95-118

Di Pierro A, Hankin C, Siveroni I, et al., 2007, Tempus fugit: How to plug it, Journal of Logic and Algebraic Programming, Vol:72, ISSN:1567-8326, Pages:173-190

Siveroni I, 2006, Filling Out the Gaps: A Padding Algorithm for Transforming Out Timing Leaks, Electronic Notes in Theoretical Computer Science, Vol:153, ISSN:1571-0661, Pages:241-257

Siveroni IA, 2004, Operational semantics of the Java Card Virtual Machine, The Journal of Logic and Algebraic Programming. Volume 58, Issues 1–2, January–march 2004, Pages 3-25


Siveroni IA, Zisman A, Spanoudakis G, Property Specification and Static Verification of UML Models, Third International Conference on Availability, Reliability and Security, 2008. ARES 08.

Siveroni IA, Wand M, Constraint Systems for Useless Variable Elimination, Simposium on Principles of Programming Languages


Siveroni IA, Volz EM, 2017, PhyDyn: Epidemiological Modelling in BEAST, v.1.0

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