Disease-Informed Modelling, Methods, and Systems (DIMMS) Lab
The DIMMS Lab at York University encompasses a broad scope of research, including mathematical biology, statistics, machine learning, spatial analysis, disease and immunity modelling, and multi-timescale analysis. Our research integrates diverse methodological approaches to address complex challenges in understanding disease dynamics and developing effective intervention strategies through innovative mathematical and computational frameworks.
Research Areas
Mathematical Biology and Disease Modeling
Our lab specializes in developing mathematical models to understand biological processes and disease dynamics. We focus on creating robust frameworks that can predict and analyze disease behavior across different populations and environments.
Machine Learning and Neural Networks
We pioneer 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.
Statistical Methods and Data Analysis
We employ advanced statistical frameworks including Bayesian inference, MCMC methods, time series analysis, and spatial statistics to analyze complex disease data and extract meaningful patterns from epidemiological datasets.
Multi-timescale Analysis
Our research analyzes disease processes across multiple temporal scales—from rapid molecular interactions to long-term population dynamics. This integrated approach helps us understand how phenomena at different scales influence overall disease patterns.
Spatial Epidemiology
We examine how geographical and socioeconomic factors influence disease transmission and outcomes, integrating spatial statistics with epidemiological models to identify risk factors and predict disease spread patterns.
Immunity Modeling
We develop sophisticated models of immune response dynamics to understand how pathogens interact with host immunity, encompassing antibody responses, T-cell dynamics, and cytokine signaling networks.
Recent Highlights
- Avian Influenza Prediction: Breakthrough neural network model for predicting avian influenza spread
- Mpox Research: Comprehensive analysis of geographical and socioeconomic factors in Mpox transmission in Ontario
- Dengue Immunodynamics: Innovative work on immune system interactions with dengue virus
- Computational Biology: Recent publications in high-impact journals including PLoS Computational Biology
Lab Director
Woldegeriel Assefa Woldegerima
Assistant Professor in the Professional stream
Department of Mathematics and Statistics, York University
Director of the DIMMS Lab at York University with expertise in mathematical biology, disease modeling, and computational epidemiology. Research focuses on developing innovative mathematical frameworks for understanding complex disease dynamics.
Contact: wassefaw@yorku.ca