Research
Our Research Approach
The Disease-Informed Modelling, Methods, and Systems (DIMMS) Lab employs a multidisciplinary approach to understand disease dynamics through advanced mathematical modeling, statistical analysis, and machine learning techniques.
Multi-timescale Analysis
Our lab specializes in analyzing disease processes across multiple timescales—from rapid molecular interactions to long-term population dynamics. This integrated approach allows us to understand how phenomena at different temporal scales influence overall disease patterns and outcomes.
Current projects include:
- Modeling the relationship between short-term immune response and long-term disease progression
- Developing mathematical frameworks that bridge molecular, cellular, individual, and population-level dynamics
- Multi-scale analysis of vaccination strategies and their long-term effectiveness
Immunity System Modeling
We develop sophisticated models of immune response dynamics to understand how pathogens interact with host immunity. Our research encompasses antibody responses, T-cell dynamics, and cytokine signaling networks that drive infectious disease outcomes.
Research highlights:
- Modeling dengue virus interactions with the immune system (Tue Dao’s research)
- Investigating vaccine efficacy through mathematical models
- Exploring immunological memory formation and duration
- Theoretical immunology and disease modeling (Dr. Qi Deng’s work)
Spatial Epidemiology
Our spatial analysis research examines how geographical and socioeconomic factors influence disease transmission and outcomes. We integrate spatial statistics with epidemiological models to identify risk factors and predict disease spread patterns across regions.
Recent work includes:
- Mapping the geographical distribution of Mpox in Ontario (Dr. Chigozie Ugwu’s research)
- Analyzing socio-environmental indicators associated with disease risk
- Developing spatial models for targeted public health interventions
Statistical Methods and Machine Learning
We employ advanced statistical frameworks and machine learning techniques to analyze complex disease data. Our approach combines traditional statistical methods with cutting-edge AI to extract meaningful patterns and predictions from epidemiological datasets.
Our toolkit includes:
- Bayesian inference and MCMC methods
- Time series analysis and spatial statistics
- Deep neural networks and random forests (Atiqa Naeem Alam Din’s research)
- Recurrent networks for temporal data analysis
Neural Network Disease Prediction
Our lab pioneers the development of disease-informed neural networks that combine epidemiological principles with deep learning architectures. These hybrid models enhance prediction accuracy by incorporating domain knowledge into machine learning frameworks.
Innovations include:
- Neural networks for predicting avian influenza spread (Nickson Golooba’s work)
- Integration of clinical, genomic, environmental, and demographic data
- Real-time outbreak prediction and progression modeling
Mathematical Biology Applications
Our mathematical biology research focuses on developing robust mathematical models for public health, policy, and industrial applications.
Key areas include:
- Nonlinear dynamical systems analysis (Dr. Andrew Omame’s expertise)
- Disease modeling using ordinary differential equations
- Vaccination strategy optimization (Farah Al Hashimi’s research)
- Mathematical models for infectious disease dynamics
Collaborative Research
Our research benefits from international collaborations and interdisciplinary partnerships:
- International Networks: Collaborations with researchers in Nigeria, Pakistan, Iraq, and Bahrain
- Interdisciplinary Approach: Integration of mathematics, biology, computer science, and public health
- Industry Partnerships: Working with health organizations on real-world applications
- Academic Collaborations: Partnerships with institutions worldwide for comprehensive research projects