Mathematical and computational approaches for understanding transmission, predicting epidemics, and evaluating public-health interventions.
Deterministic and stochastic models for understanding disease transmission, epidemic dynamics and intervention impact across populations.
Models capturing geographic structure, population movement, age-specific transmission and differences in disease risk.
Coupled models of vectors, hosts and pathogens integrating entomological, environmental and epidemiological data.
Individual-based and network models for heterogeneous contacts, behavioural dynamics and complex transmission pathways.
Combining epidemiological, genomic and surveillance data to estimate transmission dynamics, reconstruct epidemics and quantify uncertainty.
Optimal-control, forecasting, machine-learning and AI approaches to identify effective strategies for epidemic prevention and control.
| Modelling approach | What we do | Applications |
|---|---|---|
| Compartmental models | Deterministic and stochastic models describing populations through epidemiological states | SIR, SEIR and extended models for infectious diseases |
| Metapopulation & spatial models | Model transmission across locations, populations and geographic networks | Spatial spread, regional transmission and geographic risk |
| Age-structured models | Represent differences in susceptibility, exposure and transmission across age groups | Childhood infections, vaccination and age-specific interventions |
| Vector–host models | Couple pathogen transmission between human hosts and vectors | Malaria, arboviruses and other vector-borne diseases |
| Agent-based models | Simulate transmission at the individual level while incorporating heterogeneous behaviours and contacts | Complex transmission dynamics, behavioural interventions and emerging infections |
| Network models | Represent individuals, contacts and transmission pathways as dynamic networks | HIV, sexually transmitted infections, respiratory infections and contact-driven transmission |
| Phylogenetic & phylodynamic models | Combine pathogen genetic data with epidemiological models | Pathogen evolution, transmission reconstruction and genomic surveillance |
| Bayesian inference & uncertainty quantification | Estimate parameters and quantify uncertainty using epidemiological and surveillance data | Model calibration, parameter estimation and decision-making under uncertainty |
| Time-series & statistical models | Analyse temporal patterns and develop predictive models | Outbreak detection, forecasting and epidemic trends |
| Optimal control models | Identify intervention strategies that minimize disease burden and/or intervention costs | Vaccination, vector control, treatment and other public-health interventions |
| Scientific machine learning & AI | Combine mechanistic epidemiological models with machine-learning approaches | Prediction, parameter estimation, model emulation and decision support |
| Economic & cost-effectiveness models | Link transmission dynamics with costs and health outcomes | Evaluation and prioritization of public-health interventions |
| Outbreak & scenario modelling | Simulate alternative epidemic trajectories and intervention scenarios | Preparedness, response planning and policy evaluation |