We translate biological and behavioural data into agent-based models, contact networks and Bayesian inference pipelines that let us test interventions before they're deployed in the field.
The computational toolkit behind our epidemiological research.
Multiscale, agent-based frameworks for malaria and mpox developed through the Iso Lomso (STIAS) and Wiko fellowships, simulating individual-level transmission and pathogen mutation across networks.
Combining sexual behaviour survey data with phylodynamics and agent-based models — the framework developed for HIV prevention research in our PhD dissertation at Stellenbosch University.
Approximate Bayesian Computation and stochastic model calibration, methods developed during the AIMS MSc and applied to fit models to sparse, noisy surveillance data.
From dynamic-programming optimal control to today's AI4Mpox project (University of Toronto), building scientific AI to learn, predict and control complex disease systems.
Our pipelines run on open, reproducible tooling — from statistical languages to purpose-built simulation and phylogenetics software — version-controlled and shared with our partner institutions for large-scale runs.
Languages & scientific stack
ML & Bayesian inference
Epidemic & ABM simulation
Phylodynamics & genomics
model := AgentBasedModel(pathogen="malaria")
network := ContactNetwork(nodes=12_400)
posterior := infer(model, data, method="MCMC")
> simulating interventions across scenarios …
> effective R estimate: 0.91
Reach out if you'd like to collaborate on modeling, share data, or discuss a joint project.