• Modelling the spatiotemporal dynamics of mobile species under the influence of environmental stressor In collaboration with Malin Pinsky and Sapna Sharma
  • Modelling the dynamics of phytoplankton competing for light and nutrients in stratified lake
  • Modelling greenhouse gases emissions from oil sands tailing and end pit lakes It is a collaborative venture with Hao Wang, Julia Foght, Mark Lewis and Tarq Siddique.
  • Modelling the dynamics of bacteria and bacteriophage in the environment.
  • Modelling indirectly transmitted infectious disease (cholera). This is in collaboration with Zhisheng Shuai
  • Modelling the ransmission dynamics of COVID-19 in a theme park. In collaboration wth Jane Heffernan
  • Modelling the dynamics of COVID-9 in multilayer networks influenced by opinion exchanges on quarantine. In collaboration with Gonzalo Pablo Suarez, Jane Haffernan and Iian Moyles
  • Modelling the spread of communicable diseases among vulnerable groups: nursing homes. This is in collaboration with Jane Heffernan, Jianhong Wu, Peter Psasis and Safia Athar

Modelling Composting

Applying artificial intelligence to develop advanced visualization and analytics tools that will assist policy makers; now-casting and forecasting of a second pandemic wave to inform healthcare planning; determining initial disease spread characteristics within communities prior to non-pharmaceutical interventions (NPI) introduction; employing mathematical models to examine NPI effectiveness, in particular the effect of a home quarantine policy, social distancing interventions, tracing apps, testing and isolation on the epidemic development; determining NPI effects on healthcare demand; determining the effects of vaccination on the NPI uptake needed for post-vaccine waves of infection; developing an overarching economic-epidemiological model for scientists and policy-makers; build spatial disease spread models and simulations for the outbreak of COVID-19 at a localized level to model the effects of informal settlements and varying social distancing practicalities in townships, performance of hotspot analysis, and the identification of vulnerable areas; analyses of outbreak data in specific settings (e.g. households, hospitals). In collaboration with the Laboratory for Industrial and Applied Mathematics (LIAM), the Dahdaleh Institute for Global Health Research (https://dighr.yorku.ca) and the Advanced Disaster (ADERSIM) , Emergency and Rapid Response Program at York University and epidemiologists, modelers, physicists, statisticians, software engineers and data scientists across Africa institutions in Africa