Modelling for informing public health policy on prevention and control of COVID-19 epidemics in Toronto, Canada
COVID-19 has caused varying degrees of pandemic in various countries and regions around the world. The public health department has adopted a series of control policies to mitigate the spread of the disease, such as lockdowns, stay-at-home policies, school closures, travel restrictions, vaccine campaigns, etc. The implementation of these policies has significantly affected the progression of COVID-19. In this talk, I will introduce a series of mathematical modeling studies regarding COVID-19 conducted over the past three years in collaboration with the Public Health Agency of Canada and the Toronto Public Health. The studies focus on mitigation strategies, epidemic prediction, reopening, and vaccination strategies, taking into account the actual epidemic prevention needs of public health sectors, and highlighting the scientific value of mathematical models in aiding public health decision-making.