001444342 000__ 06390cam\a2200529Ia\4500 001444342 001__ 1444342 001444342 003__ OCoLC 001444342 005__ 20230310003705.0 001444342 006__ m\\\\\o\\d\\\\\\\\ 001444342 007__ cr\un\nnnunnun 001444342 008__ 220210s2022\\\\sz\\\\\\o\\\\\100\0\eng\d 001444342 019__ $$a1296266381$$a1296431659$$a1298387925 001444342 020__ $$a9783030850531$$q(electronic bk.) 001444342 020__ $$a3030850536$$q(electronic bk.) 001444342 020__ $$z3030850528 001444342 020__ $$z9783030850524 001444342 0247_ $$a10.1007/978-3-030-85053-1$$2doi 001444342 035__ $$aSP(OCoLC)1296167108 001444342 040__ $$aYDX$$beng$$cYDX$$dN$T$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dOCLCO$$dSFB$$dUKAHL$$dOCLCQ 001444342 049__ $$aISEA 001444342 050_4 $$aRA644.C67 001444342 08204 $$a616.24140015118$$223 001444342 1112_ $$aSeminar on the Mathematical Modelling of COVID-19$$d(2020 :$$cToronto, Ont.) 001444342 24510 $$aMathematics of public health :$$bproceedings of the Seminar on the Mathematical Modelling of COVID-19 /$$cV. Kumar Murty, Jianhong Wu, editors. 001444342 260__ $$aCham, Switzerland :$$bSpringer,$$c2022. 001444342 300__ $$a1 online resource 001444342 4901_ $$aFields Institute communications ;$$vv. 85 001444342 500__ $$aConference proceedings. 001444342 5050_ $$aDiverse local epidemics reveal the distinct effects of population density, demographics, climate, depletion of susceptibles, and intervention in the first wave of COVID-19 in the United States (N. Ashfordi, B. Holder, M. Bahrami, D. Lichtblau) -- Describing, modelling and forecasting the spatial and temporal spread of COVID-19 (J. Arino) -- A logistic growth model with logistically varying carrying capacity for Covid-19 deaths using data from Ontario, Canada (G. Bucyibaruta, C.B. Dean, E.M. Renouf) -- COVID-19 in Ontario (R. Fields, L Humphrey, E.W. Thommes, M.G. Cojocaru) -- Sub-epidemic model forecasts during the first wave of the COVID-19 pandemic in the USA and European hotspots (G. Chowell, R. Rothenberg, K. Roosa, A. Tariq, J.M. Hyman, R. Luo) -- A Model on the Large Scale Use of Convalescent Plasma to Treat Patients with Severe Symptoms (X. Huo) -- Dont wait, re-escalate: delayed action results in longer duration of COVID-19 restrictions (A. Hurford, J. Watmough) -- Generalized Additive Models to Capture the Death Rates in Canada COVID-19 (F. Izadi) -- Real-time prediction of the end of an epidemic wave (Q. Griette, Z. Liu, P. Magal, R.N. Thompson) -- The Effect that Heterogeneity in Social Distancing Has on the Infection Peak (C. McCluskey) -- Forecasting PPE Demand for Ontario Acute Care Hospitals During COVID-19 (B. Sander) -- Learning COVID-19 Mitigation Strategies using Reinforcement Learning (N. Denis, A. El-Hajj, B Drummond, Y. Abiza, K.C. Gopaluni) -- Joint Modeling of Hospitalization and Mortality of Ontario Covid-19 cases (D.Z. Xi) -- Evaluating the risk of reopening the border: a case study of Ontario (Canada) to New York (USA) using mathematical modeling (P. Yuan, E. Aruffo, Q. Li, J. Li, Y. Tan, T. Zheng, J. David, N. Ogden, E. Gatov, E. Gournis, S. Collier, B. Sander, G. Fan, J. Heffernan, J. Li, J.D. Kong, J. Arino, J. Belair, J. Watmough, H. Zhu) -- Optimal staged reopening schedule based on ICU capacity (K. Nah, M. Chen, A. Asgary, Z. McCarthy, F. Scarabel, Y. Xiao, N.L. Bragazzi, J.M. Heffernan, N.H. Ogden, J.Wu) -- Mathematics of the Pandemic (M.R. Murty, V.K. Murty) -- A mathematical model for evaluating the impact of non-pharmaceutical interventions on the COVID-19 epidemic in the United Kingdom (H. Zhu) -- COVID-19 in Japan (H. Nishiura). . 001444342 506__ $$aAccess limited to authorized users. 001444342 520__ $$aCurated by the Fields Institute for Research in Mathematical Sciences from their COVID-19 Math Modelling Seminars, this first in a series of volumes on the mathematics of public health allows readers to access the dominant ideas and techniques being used in this area, while indicating problems for further research. This work brings together experts in mathematical modelling from across Canada and the world, presenting the latest modelling methods as they relate to the COVID-19 pandemic. A primary aim of this book is to make the content accessible so that researchers share the core methods that may be applied elsewhere. The mathematical theories and technologies in this book can be used to support decision makers on critical issues such as projecting outbreak trajectories, evaluating public health interventions for infection prevention and control, developing optimal strategies to return to a new normal, and designing vaccine candidates and informing mass immunization program. Topical coverage includes: basic susceptible-exposed-infectious-recovered (SEIR) modelling framework modified and applied to COVID-19 disease transmission dynamics; nearcasting and forecasting for needs of critical medical resources including personal protective equipment (PPE); predicting COVID-19 mortality; evaluating effectiveness of convalescent plasma treatment and the logistic implementation challenges; estimating impact of delays in contact tracing; quantifying heterogeneity in contact mixing and its evaluation with social distancing; modelling point of care diagnostics of COVID-19; and understanding non-reporting and underestimation. Further, readers will have the opportunity to learn about current modelling methodologies and technologies for emerging infectious disease outbreaks, pandemic mitigation rapid response, and the mathematics behind them. The volume will help the general audience and experts to better understand the important role that mathematics has been playing during this on-going crisis in supporting critical decision-making by governments and public health agencies. 001444342 650_0 $$aCOVID-19 (Disease)$$xMathematical models$$vCongresses. 001444342 650_6 $$aCOVID-19$$xModèles mathématiques$$vCongrès. 001444342 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772 001444342 655_7 $$aCongressos.$$2thub 001444342 655_7 $$aLlibres electrònics.$$2thub 001444342 655_0 $$aElectronic books. 001444342 7001_ $$aMurty, Vijaya Kumar,$$d1956- 001444342 7001_ $$aWu, Jianhong,$$d1964- 001444342 77608 $$iPrint version:$$z3030850528$$z9783030850524$$w(OCoLC)1260664514 001444342 830_0 $$aFields Institute communications ;$$vv. 85. 001444342 852__ $$bebk 001444342 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-85053-1$$zOnline Access$$91397441.1 001444342 909CO $$ooai:library.usi.edu:1444342$$pGLOBAL_SET 001444342 980__ $$aBIB 001444342 980__ $$aEBOOK 001444342 982__ $$aEbook 001444342 983__ $$aOnline 001444342 994__ $$a92$$bISE