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DIMMS Lab

DIMMS Lab · York University, Toronto

Modelling infectious disease, from a single host to a whole population.

The Disease-Informed Modelling, Methods and Systems Laboratory builds mechanistic, statistical and machine-learning models of how infections spread, and uses them to inform vaccination, forecasting and public health decisions.

Our research Meet the team

SIR simulation

Day 0

Epidemic curve of the outbreak in the field The number of people who are susceptible, infectious and recovered on each day of the outbreak, with the peak of infections marked. The counts beside the chart give the same numbers for the current day.
Susceptible
0
Infectious
0
Recovered
0
Vaccinated
0

Fig. 1A simulated outbreak: each dot is a person, infection can pass between people who come close, and recovery brings immunity.

Click or tap the field to start an outbreak there.

Research radar

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Based in the Department of Mathematics and Statistics at York University, the DIMMS Lab works on practical questions about vaccination, forecasting and outbreak control. Current and recent work covers mpox, seasonal and avian influenza, dengue and Zika.

Prof. Woldegebriel Assefa Woldegerima Led by Prof. Woldegebriel Assefa Woldegerima Associate Professor, Department of Mathematics and Statistics
Members
13
Papers since 2025
8
Research areas
6

Lab meeting on Tuesdays

01 Research

What we work on

Most projects pair a mechanistic model of transmission with real data and a practical question: who to vaccinate first, when an outbreak will peak, or how a virus behaves inside the body.

  1. Transmission dynamics

    Mathematical models for public health and policy, including the analysis of nonlinear dynamical systems, differential equation models of disease and the optimization of vaccination strategies.

  2. Disease-informed machine learning

    Disease-informed neural networks that build epidemiological principles into deep learning architectures. These hybrid models aim to make forecasts of outbreaks and of disease progression more accurate.

  3. Statistical inference

    Statistical and machine-learning methods for epidemiological data, including Bayesian inference, time-series analysis, deep neural networks and random forests.

  4. Within-host and immune dynamics

    Models of immune response dynamics that show how pathogens interact with host immunity, covering antibody responses, T-cell dynamics and cytokine signalling networks.

  5. Spatial epidemiology

    How geographical and socio-economic factors shape transmission and outcomes. We combine spatial statistics with epidemiological models to identify risk factors and anticipate where disease will spread.

  6. Multi-timescale analysis

    Disease processes unfold on very different timescales, from rapid molecular interactions to long-term population dynamics. We develop mathematical frameworks, including singular perturbation methods, that connect molecular, cellular, individual and population-level dynamics.

Read about our research and current projects

02 Publications

Recent papers

Peer-reviewed work from the lab, newest first. Lab members are shown in bold.

All publications

03 News

From the lab

All news

04 Team

Lab members

Led by Prof. Woldegebriel Assefa Woldegerima, with members ranging from undergraduate researchers to postdoctoral fellows.

Members of the DIMMS Lab seated around a restaurant table, smiling at the camera
Lab members over lunch, August 2026.