DIMMS Lab
Disease-Informed Modelling, Methods & Systems
Advancing mathematical biology through innovative computational frameworks to understand disease dynamics and develop effective intervention strategies.
York University • Department of Mathematics & Statistics
What We Do
Our interdisciplinary research integrates mathematical modeling, machine learning, and statistical analysis to tackle complex challenges in disease dynamics and public health.
Disease Modeling
Developing mathematical frameworks to understand and predict disease transmission dynamics across populations.
Machine Learning
Pioneering disease-informed neural networks that combine epidemiological principles with deep learning.
Statistical Analysis
Employing Bayesian inference, MCMC methods, and spatial statistics for complex data analysis.
Multi-scale Analysis
Analyzing disease processes from molecular interactions to population-level dynamics.
Spatial Epidemiology
Examining geographical and socioeconomic factors in disease transmission patterns.
Immunity Modeling
Modeling immune response dynamics and host-pathogen interactions.
Latest News
Upcoming Seminar: Sherif Shuaib
Join us October 17 for a research presentation on stage-structured models for fisheries management. Ross Building, Room N638.
Learn more →SIAM/CAIMS Conference
Dr. Andrew Omame and Dr. Qi Deng represented DIMMS Lab at the Joint SIAM/CAIMS Annual Meetings in Montreal.
Read more →New Publication in PLoS Computational Biology
Dr. Qi Deng published significant research advancing computational biology methodologies.
Read more →Recent Publications
Deep neural networks with application in predicting the spread of avian influenza
Big Data and Information Analytics
Geographical distribution and socio-environmental indicators of Mpox in Ontario
PLOS ONE
Risk factors associated with human Mpox infection: systematic review
BMJ Global Health
Lab Director
Prof. Woldegeriel Assefa Woldegerima
Assistant Professor • Department of Mathematics and Statistics
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.