Predictive modelling of COVID-19 in Canada to inform public health measures
Abstract: This presentation will encompass modelling at the Public Health Agency of Canada (PHAC) to understand possible trajectories of the COVID-19 epidemic in Canada under different scenarios for public health measures (also called Non-Pharmaceutical Interventions). The presentation will introduce the modelling approaches, and have a particular focus on the public health interventions that need to be in place to prevent a resurgence of the epidemic, in what we think is a largely naïve Canadian population, as Canada re-opens. These modelling studies provide policy makers with information they need to develop policies as the lifting of restrictive closures of workplaces, schools, universities and social meeting places begins. In particular, the work to date focuses on the needs for detection and isolation of cases, tracing of contacts with cases and their quarantine, and the likely continued need for members of the public to physical distance at work and leisure. The impact of importation of cases when borders open will also be discussed.
Dr. Nick Ogden is a UK-trained veterinarian (ULiverpool, 1983). After 10 years of mixed clinical practice, he completed a doctorate in disease ecology at UOxford in 1996. During the six years he spent as a professor at the Faculty of Veterinary Science, ULiverpool, he conducted research into the ecology and epidemiology of vector-borne diseases of public health importance in Europe and those of importance to livestock production in Africa. In 2002 he moved to Canada, and continued research on the ecology of Lyme disease and other zoonoses and climate change as a research scientist at the Public Health Agency of Canada (PHAC). He is now Director of Public Health Risk Sciences division within the NML of PHAC, assessing risk by study and modelling of the ecology, epidemiology and genetic diversity of vectors and zoonotic and vector-borne micro-organisms, assessing impacts of climate change on zoonoses and vector-borne diseases, and developing tools for public health adaptation. He is currently leading the PHAC COVID-19 modelling team.
Dr. Nick Ogden
Director of Public Health Risk Sciences division
The NML of PHAC
Date: July 23, 2020
Time: 8:30-9:30 PM (Eastern Time)
Roles of Dynamical Modeling and Optimization in Informing Strategical Decision and Logistic Implementation of Social Distancing Escalation and De-escalation to Mitigate COVID-19 Pandemic
Abstract: Two weeks after confirming the first COVID-19 case in Canada on January 25th 2020, the Fields Institute, in partnership with other Canadian mathematical research institutes and in collaboration with several federal health agencies and national research council, formed the Mathematical Modelling of COVID-19 Task Force to undertake re search relating to COVID-19. Shortly after, both Canadian federal and Ontario provincial governments established their modeling consulting groups, the Public Health Agency of Canada' s External Modelling Expert Group and the Ontario Provincial COVID-19 Modelling Consensus Table. In this talk, I will provide some observations about the role, challenge and opportunity of mathematical modeling in informing public health decision making in real-time during a pandemic. I will focus on the dynamic interactions between the National Task Force/federal and provincial Expert Groups and the Laboratory for Industrial and Applied Mathematics (LIAM) at York University, and I use a few modeling studies to demonstrate the synergy between policy making/implementation and mathematical modeling and analyses.
Dr. Jianhong Wu is Tier 1 Canada research Chair and NSERC Industrial Research Chair. He is the 1st recipient of the Research Prize from the Canadian Applied and Industrial Mathematical Society (CAIMS). He also received the 2019 CAIMS-Fields Industrial Mathematics Prize and the Queen Elizabeth II Diamond Jubilee Medal from the Government of Canada in 2012. Among numerous international awards bestowed to Prof. Wu are the Humboldt Fellowship from the Alexander von Humboldt Foundation; the Paul Erdos Visiting Professorship from the Hungary Academy of Science; the FAPESP Visiting Research Fellow from the Universidade de Sao Paulo, a Canada Research Chair partner of the International Research Chair awarded by IDRC in collaboration with the China CDC. He is also a Fellow of the Fields Institute, and was featured by the Toronto Life as one of the "Top 18 Scientists in Toronto".
Dr. Jianhong Wu
Professor, Tier 1 Canada Research Chare
Date: July 17, 2020
Time: 8:30-10:00 AM (Toronto)
Real-time epi and acute care need monitoring/forecasting for COVID-19: Sequential Monte Carlo-leveraged transmission models
Abstract: While COVID-19 transmission models have conferred great value in informing public health understanding, planning and response, the ability of public health decision makers to rely purely upon traditional transmission models with pre-set assumptions -- no matter how favourably evidenced when built -- is challenged by numerous factors. The ongoing replanning associated with rolling back and re-instituting measures can strongly benefit from approaches that continuously integrate into rigorous transmission models so that shifts in epidemiology, behaviour, and availability of acute care resources, can be monitored in real time, and scenario-based projections can be made. We describe here the design, implementation and day-to-day use for Saskatchewan public health and clinical support decision making of a particle filtered COVID-19 compartmental model, using a Sequential Monte Carlo algorithm of Particle Filtering that is informed by different data sources.Model outputs include estimates of Rt, and counts of undiagnosed/diagnosed infectives.
Dr. Nathaniel D. Osgood (PhD MIT) is a Professor of Computer Science and Associate Faculty in Community Health & Epidemiology at USaskatchewan. His research is focused on providing cross-linked simulation, ubiquitous sensing, and machine learning tools to inform understanding of population health trends and health policy tradeoffs. His applications work has addressed challenges in the communicable, zoonotic, environmental, and chronic disease areas. Dr. Osgood is a co-creator of two novel mobile sensor-based epidemiological monitoring systems, most recently the Google Android- and iPhone-based iEpi (now Ethica Health) mobile epidemiological monitoring systems.
Dr. Nathaniel Osgood
Professor of Computer Science
Date: July 17, 2020
Time: 8:30-9:30 PM
Point-of-Care Diagnostic of SARS-CoV-2: a Systematic Review and Meta-Analysis of Real-World Data
SARS-CoV-2 is an enveloped, single-stranded, positive-sense RNA virus, responsible for a highly contagious infection, known as COVID-19. SARS-CoV-2 was discovered in late December 2019 and, since then, has spread out from China into neighboring countries, becoming a global pandemic. Timely and accurate COVID-19 laboratory testing is an essential step in the management of the COVID-19 outbreak. To date, both the “European Centre for Disease Prevention and Control” (ECDC) and the “World Health Organization” (WHO) have recommended the use of an assay based on the real-time polymerase chain reaction (RT-PCR) in respiratory samples as gold standard for COVID-19 diagnosis. Unfortunately, RT-PCR has several practical limitations. Consequently, alternative diagnostic methods are urgently required, both for alleviating the pressure on laboratories and healthcare facilities and for expanding testing capacity to enable large-scale screening and ensure a timely therapeutic intervention. To date, few studies have been conducted so far concerning the potential utilization of rapid testing for COVID-19. However, some conflicting results have been reported and there is a need for an updated synthesis of the literature to better inform health policies and guidelines. Therefore, the present systematic review and meta-analysis was undertaken to explore the feasibility of rapid diagnostic tests in the management of the COVID-19 outbreak. Based on four studies, we computed a pooled sensitivity of 70.5% (95%CI 47.5–86.3), and specificity of 99.1% (95%CI 88.8–99.9). We can conclude that: 1) rapid diagnostic tests for COVID-19 are necessary, but should be adequately sensitive and specific; 2) few studies have been carried out to date on commercially available tests; 3) the studies included are characterized by low numbers and low sample power, and 4) in light of these results, the use of available tests is currently questionable for clinical purposes and cannot substitute other more reliable molecular tests, such as assays based on RT-PCR.
Time: 10:30-11:30 AM
Understanding unreported cases in the 2019 n-Cov epidemic outbreak and the importance of major public health interventions
Abstract: We develop a mathematical model to provide epidemic predictions for the 2019-nCov epidemic in China. We use reported case data from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the parameterized model we identify the number of unreported cases. We then use the model to project the epidemic forward with varying level of public health interventions. The model predictions emphasize the importance of major public health interventions in controlling 2019-nCov epidemics. Next we will apply it to the data from South Korea, Italy, France and Germany.
Dr. Pierre Magal
Université de Bordeaux
Date: March 31, 2020
Time: 10:30-11:30 AM