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Paul Tupper Person1 #679862 Professor in the Department of Mathematics at Simon Fraser University. | 
Research InterestsI am interested in three different areas: Stochastic Differential Equations, Diversities, and Cognitive Science. In the field of Stochastic Differential Equations I work on issues both in the modeling of systems with stochastic differential equations and the numerical analysis of stochastic differential equations. Diversities are a generalization of metric spaces developed by David Bryant and me. We introduced diversities in "Hyperconvexity and Tight Span Theory for Diversities." More recently, we discuss the relation between diversities and some network flow problems in "Diversities and the Geometry of Hypergraphs." Cognitive Science is a tremendously broad field, and I currently work in only two small corners of it: phonology and dynamic field theory. |
+Citations (3) - CitationsAdd new citationList by: CiterankMapLink[2] COVID-19 endgame: From pandemic to endemic? Vaccination, reopening and evolution in low- and high-vaccinated populations
Author: Elisha B. Are, Yexuan Song, Jessica E. Stockdale, Paul Tupper, Caroline Colijn Publication date: 20 December 2022 Publication info: Journal of Theoretical Biology, Volume 559, 2023, 111368, ISSN 0022-5193, Cited by: David Price 9:07 PM 26 November 2023 GMT Citerank: (3) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 704041Vaccination859FDEF6 URL: DOI: https://doi.org/10.1016/j.jtbi.2022.111368
| Excerpt / Summary [Journal of Theoretical Biology, 20 December 2022]
COVID-19 remains a major public health concern, with large resurgences even where there has been widespread uptake of vaccines. Waning immunity and the emergence of new variants will shape the long-term burden and dynamics of COVID-19. We explore the transition to the endemic state, and the endemic incidence in British Columbia (BC), Canada and South Africa (SA), to compare low and high vaccination coverage settings with differing public health policies, using a combination of modelling approaches. We compare reopening (relaxation of public health measures) gradually and rapidly as well as at different vaccination levels. We examine how the eventual endemic state depends on the duration of immunity, the rate of importations, the efficacy of vaccines and the transmissibility. These depend on the evolution of the virus, which continues to undergo selection. Slower reopening leads to a lower peak level of incidence and fewer overall infections in the wave following reopening: as much as a 60% lower peak and a 10% lower total in some illustrative simulations; under realistic parameters, reopening when 70% of the population is vaccinated leads to a large resurgence in cases. The long-term endemic behaviour may stabilize as late as January 2023, with further waves of high incidence occurring depending on the transmissibility of the prevalent variant, duration of immunity, and antigenic drift. We find that long term endemic levels are not necessarily lower than current pandemic levels: in a population of 100,000 with representative parameter settings (Reproduction number 5, 1-year duration of immunity, vaccine efficacy at 80% and importations at 3 cases per 100K per day) there are over 100 daily incident cases in the model. Predicted prevalence at endemicity has increased more than twofold after the emergence and spread of Omicron. The consequent burden on health care systems depends on the severity of infection in immunized or previously infected individuals. |
Link[3] COVID-19 cluster size and transmission rates in schools from crowdsourced case reports
Author: Paul Tupper, Shraddha Pai, COVID Schools Canada, Caroline Colijn Publication date: 30 November 2022 Publication info: eLife, 30 November 2022 Cited by: David Price 10:27 PM 27 November 2023 GMT Citerank: (3) 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 715617Schools859FDEF6 URL: DOI: https://doi.org/10.7554/eLife.76174
| Excerpt / Summary [eLife, 30 November 2022]
The role of schools in the spread of SARS-CoV-2 is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports from four Canadian provinces, providing estimates of mean and dispersion for cluster size, as well as the distribution of the instantaneous transmission parameter β, whilst factoring in imperfect ascertainment. According to our model with parameters estimated from the data, in all four provinces (i) more than 65% of non-index cases occur in the 20% largest clusters, and (ii) reducing instantaneous transmission rate and the number of contacts a student has at any given time are effective in reducing the total number of cases, whereas strict bubbling (keeping contacts consistent over time) does not contribute much to reduce cluster sizes. We predict strict bubbling to be more valuable in scenarios with substantially higher transmission rates. |
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