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BEGIN:VEVENT
SUMMARY:Ping Yan (Public Health Agency of Canada)
DTSTART:20200519T143000Z
DTEND:20200519T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/1
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/1/">Some preliminary trend analysis of COVID-19
  in five selected provinces based on data reported</a>\nby Ping Yan (Publi
 c Health Agency of Canada) as part of COVID-19 Math Modelling Seminar\n\n\
 nAbstract\nThe most recognized trends of transmission of COVID-19 are that
  based on publicly reported data. They are updated and disseminated by pro
 vinces and territories via public media\, available from various internet 
 sources\, such as Canada COVID-19 Situation Dashboard produced by ArcGIS\,
  https://www.covid-19canada.com/\, or https://www.worldometers.info/corona
 virus/country/canada/. These trends are typically presented by date of rep
 ort. They are strongly affected by confounding factors such as testing pat
 terns\, reporting patterns\, computer glitches\, weekdays vs weekends\, et
 c. A different trend presentation is the “Epidemic curve” based on dat
 e of illness onset*\, such as that published by Government of Canada on Ca
 nada.ca. This is more relevant to the disease transmission. However\, repo
 rting delays make the numbers of cases with date of onset in recent days t
 end to be more incomplete compared to cases with dates of onset quite a lo
 ng time ago. This causes a time-bias with an artificial decline for recent
  cases. It is more informative to use statistical models to adjust for thi
 s reporting delay in order to illustrate the trend in the recent past\, as
  a form of now-casting. Reporting delay adjustments are performed by using
  survival analysis techniques for right-truncated data to calculate adjust
  weights. The mot relevant epidemic trends should be presented by dates at
  transmission\, but they are not directly observable from data. They are r
 evealed through statistical models that take the incubation time distribut
 ion into account. If we call the reporting delay adjustment “now-casting
 ”\, then the estimated trends by dates of transmission is “back-castin
 g”. The algorithm is applied to the reporting delay adjusted trend by da
 te of onset. We demonstrate our results based on two-step analysis of repo
 rted data in five selected provinces with Step 1: now-casting trends by da
 te of onset through reporting delay analysis\, and Step 2: back-casting tr
 ends by date of transmission. For each province\, trend representations ar
 e plotted on the same chart according to three different event markers: by
  time at transmission\, by time of onset and by time of reporting. The tre
 nd by date of transmission and the trend by date of onset share close rese
 mblance\, separated by approximately 5 days of the average incubation peri
 ods. The trend by date of transmission and the trend by date of report are
  far apart by 10 days or more. They still have some resemblance. The trend
  by date of report\, as the most visible trend that the public sees\, not 
 only reflects the past transmission taking place 10 to 15 days prior\, but
  also influenced by other confounding factors. The importance messages of 
 these results are: (i) recognition of different transmission patterns and 
 timing in different provinces\; (ii) recognition of the time-delay between
  what we see based on reported data and what might have happened in the pa
 st\; (iii) challenges to mathematical modelling in general while discussin
 g fitting models to data\; (iv) future directions for both modelling and f
 or improvement of data collection.\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Connell McCluskey (Wilfrid Laurier University)
DTSTART:20200526T143000Z
DTEND:20200526T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/2
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/2/">Accounting for heterogeneity in social dist
 ancing</a>\nby Connell McCluskey (Wilfrid Laurier University) as part of C
 OVID-19 Math Modelling Seminar\n\n\nAbstract\nI will present a determinist
 ic compartmental model for COVID-19 with 2 main subgroups: one group that 
 does social distancing and one that doesn't. We vary the number of contact
 s for the social distancing group\, while keeping the basic reproduction n
 umber R0 fixed (by changing the relative sizes of the two groups). We see 
 that the peak number of infections changes dramatically\, dropping by as m
 uch as 70%\, while the initial growth rate and timing of the peak remain c
 onstant. This suggests that heterogeneity in social distancing is fundamen
 tally important.\n\nAs economies open up\, heterogeneity will continue to 
 be important.\n\nBio: \n\nConnell McCluskey received his PhD in 2002 from 
 the University of Alberta\, receiving the CAIMS Doctoral Dissertation Awar
 d. After postdoctoral research at the University of Victoria and McMaster 
 University\, he moved to Wilfrid Laurier University in Waterloo\, Ontario.
  He spent 2011 as a visiting professor at the Université Victor Segalen B
 ordeaux 2 in France.\n\nMore talks from this series can be seen here: http
 ://www.fields.utoronto.ca/activities/19-20/covid-19-math-modelling-seminar
 \n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mirjam Kretzschmar (University Medical Center Utrecht)
DTSTART:20200602T143000Z
DTEND:20200602T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/3
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/3/">Time is of the essence: impact of delays on
  effectiveness of contact tracing for COVID-19</a>\nby Mirjam Kretzschmar 
 (University Medical Center Utrecht) as part of COVID-19 Math Modelling Sem
 inar\n\n\nAbstract\nWith confirmed cases of COVID-19 declining in many cou
 ntries\, lockdown measures are gradually being lifted. However\, even if m
 ost social distancing measures are continued\, other public health measure
 s will be needed to control the epidemic. Contact tracing either via conve
 ntional methods or via mobile app technology is central to control strateg
 ies during de-escalation of social distancing. In my talk\, I will present
  results on the impact of timeliness and completeness on the effectiveness
  of contact tracing strategies (CTS). To analyze the impact of various ste
 ps of a CTS\, we developed a stochastic mathematical model with explicit t
 ime delays between time of infection\, symptom onset\, diagnosis by testin
 g\, and isolation. The model also includes tracing of close contacts (e.g.
  household members) and casual contacts with different delays and coverage
 s. We computed effective reproduction numbers of a CTS for a population wi
 th social distancing measures and various scenarios for testing\, isolatio
 n of index cases\, and tracing and quarantine of their contacts. We quanti
 fied the percent onward transmissions per diagnosed index case that can be
  prevented by CTS depending on timeliness and completeness of isolation an
 d tracing.\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA (TBA)
DTSTART:20200609T143000Z
DTEND:20200609T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/4
DESCRIPTION:by TBA (TBA) as part of COVID-19 Math Modelling Seminar\n\nAbs
 tract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Julien Arino (University of Manitoba)
DTSTART:20200616T143000Z
DTEND:20200616T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/5
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/5/">Considerations on the spatial and temporal 
 spread of COVID-19</a>\nby Julien Arino (University of Manitoba) as part o
 f COVID-19 Math Modelling Seminar\n\n\nAbstract\nPlease register here: htt
 ps://zoom.us/meeting/register/tJMrdOCsrz4jH9SXpn1gJLgslrwytZmiaOZv .\n\nWh
 ile the exact origin and date of start of circulation of SARS-CoV-2 is as 
 yet uncertain\, it appears that the disease it causes\, COVID-19\, started
  its international spread after an amplification stage that happened in Wu
 han (China) in late 2019. Roughly six months later\, COVID-19 has been rep
 orted in most countries and territories worldwide. In this talk\, I will p
 resent various models I have studied that aim to understand and sometimes 
 predict this spatial spread. The work revolves around a slight complexific
 ation of an SLIAR model that Brauer\, van den Driessche\, Watmough\, Wu an
 d myself had studied in preparation for the last pandemic (H1N1). I will f
 irst present this base model and briefly detail its properties. I will the
 n show how the model is being used in three different contexts: a) determi
 nation of places most likely to next import the disease\, b) assessment of
  the risk of importation of the disease to an uninfected location and c) e
 valuation of heterogeneity of transmission characteristics in different lo
 cations.\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Farzali Izadi (Urmia University)
DTSTART:20200623T143000Z
DTEND:20200623T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/6
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/6/">Generalized additive models to capture the 
 death rates in Canada COVID-19</a>\nby Farzali Izadi (Urmia University) as
  part of COVID-19 Math Modelling Seminar\n\n\nAbstract\nPlease register he
 re: https://zoom.us/meeting/register/tJMrdOCsrz4jH9SXpn1gJLgslrwytZmiaOZv 
 .\n\nTo capture the death rates and strong weekly pattern in the COVID-19\
 , we utilize the generalized additive model in \n\nthe absence of direct s
 tatistically based measurement of infection rates. By examining the death 
 rates of Canada in general and Quebec\, Ontario and Alberta in particular\
 , one can easily figure out that there is substantial overdispersion relat
 ive to the Poisson so that the negative binomial distribution is an approp
 riate choice for the analysis. Generalized additive models (GAMs) are one 
 of the main modeling tools for data analysis. GAMs can efficiently combine
  different types of fixed\, random and smooth terms in the linear predicto
 r of a regression model to account for different types of effects. GAMs ar
 e a semi-parametric extension of generalized linear models (GLMs)\, used o
 ften for the case when there is no a priori reason for choosing a particul
 ar response function (such as linear\, quadratic\, etc.) and need the data
  to speak for themselves. GAMs do this via the smoothing functions and tak
 e each predictor variable in the model and separate it into sections (deli
 mited by knots) and then fit polynomial functions to each section separate
 ly\, with the constraint that there are no links at the knots (second deri
 vatives of the separate functions are equal at the knots).\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Troy Day (Queen's University)
DTSTART:20200630T143000Z
DTEND:20200630T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/7
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/7/">The Political Economy of Infectious Disease
  Outbreaks</a>\nby Troy Day (Queen's University) as part of COVID-19 Math 
 Modelling Seminar\n\n\nAbstract\nPeople's incentives during an infectious 
 disease outbreak influence their behaviour\, and this behaviour can impact
  how the outbreak unfolds. Early on during an outbreak\, people are at lit
 tle personal risk of infection and hence may be unwilling to change their 
 lifestyle to slow the spread of disease. As the number of cases grows\, ho
 wever\, people may then voluntarily take extreme measures to limit their e
 xposure. Government leaders also respond to the welfare and changing desir
 es of their constituents\, through public health policies that themselves 
 shape the course of the epidemic and its ultimate health and economic repe
 rcussions. In this talk I will use ideas from the study of differential ga
 mes to model how individuals’ and government leaders’ incentives chang
 e during an outbreak\, and the epidemiological and economic consequences t
 hat ensue when these incentives are acted upon. Motivated by the current C
 OVID-19 pandemic\, I focus on physical distancing behaviour and the imposi
 tion of stay-at-home orders. I show that there is a fundamental difference
  in the economic and health consequences of an infectious disease outbreak
  depending on the degree of asymptomatic transmission. If transmission occ
 urs primarily by asymptomatic carriers\, then government leaders will be i
 ncentivized to impose stay-at-home orders earlier and for longer than indi
 viduals would like. Despite such orders being unpopular\, however\, they u
 ltimately benefit all individuals. On the other hand\, if the disease is t
 ransmitted primarily by symptomatic infections\, then individuals can be i
 ncentivized to stay at home earlier and for longer than government leaders
  would like. In this case\, politicians can be incentivized to impose back
 -to-work orders that\, despite being unpopular\, will again ultimately be 
 to the benefit of all individuals.\n\nThis is joint work with David McAdam
 s\, Fuqua School of Business and Economics Department\, Duke University.\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Beate Sander (University Health Network)
DTSTART:20200714T143000Z
DTEND:20200714T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/8
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/8/">Forecasting PPE demand for Ontario acute ca
 re hospitals during COVID-19</a>\nby Beate Sander (University Health Netwo
 rk) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Niayesh Afshordi (Perimeter Institute for Theoretical Physics)
DTSTART:20200721T143000Z
DTEND:20200721T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/9
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/9/">Diverse local epidemics reveal the effects 
 of geography\, climate\, and susceptible depletion in the first wave of CO
 VID-19 in the US</a>\nby Niayesh Afshordi (Perimeter Institute for Theoret
 ical Physics) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\
 n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Georges Bucyibaryta / Charmaine Dean (University of Waterloo)
DTSTART:20200728T143000Z
DTEND:20200728T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/10
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/10/">A logistic growth model with logistically 
 varying carrying capacity for Covid-19 deaths using data from Ontario\, Ca
 nada</a>\nby Georges Bucyibaryta / Charmaine Dean (University of Waterloo)
  as part of COVID-19 Math Modelling Seminar\n\n\nAbstract\nWe consider a l
 ogistic growth model with its carrying capacity parameter allowed to vary 
 logistically\, for modeling the cumulative number of deaths due to Covid-1
 9 in the province of Ontario. In particular\, the parameter modeling carry
 ing capacity is linked to the number of hospitalizations. Although this is
  an empirical model\, it incorporates conceptual elements that support the
  framework required for modeling Covid-19 or more generally\, any infectio
 us disease where hospitalizations are required in management of the diseas
 e. By nature\, the logistic growth model is deterministic\, and so we indu
 ce stochasticity through incorporating the variability that is observed in
  modeling the daily counts and propose a tool that can be used to quantify
  the behavior of the disease within a short time period\, for example to p
 redict the number of deaths based on new hospitalizations six days earlier
 . The stochasticity in the daily number of deaths is modeled using a negat
 ive binomial distribution. We develop an indicator of a shift in the trend
  of the cumulative number of deaths that could be used to monitor resurgen
 ce of the disease\, and hence serve as a marker for public health interven
 tion.\n\nCharmaine Dean is Vice-President\, Research and Professor in the 
 Department of Statistics and Actuarial Science at the University of Waterl
 oo. Her research interest lies in the development of methodology for disea
 se mapping\, longitudinal studies\, the design of clinical trials\, and sp
 atio-temporal analyses. Much of this work has been motivated by direct app
 lications to important practical problems in biostatistics and ecology.\n\
 nGeorges Bucyibaruta is a postdoctoral fellow in the Department of Statist
 ics and Actuarial Science at University of Waterloo\, working jointly with
  Dr Charmaine Dean and Dr Mahmoud Torabi at the University of Manitoba. Hi
 s main research interests are related to spatial analysis and infectious d
 isease modeling. He completed his PhD in Probability and Statistics at the
  Centro de Investigación en Matemáticas (CIMAT) in Guanajuato\, Mexico.\
 n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Pieter Houtekamer (Environment and Climate Change Canada)
DTSTART:20200804T143000Z
DTEND:20200804T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/11
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/11/">Analysis and forecasting for COVID-19</a>\
 nby Pieter Houtekamer (Environment and Climate Change Canada) as part of C
 OVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Xi Huo (University of Miami)
DTSTART:20200811T143000Z
DTEND:20200811T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/12
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/12/">Effectiveness and feasibility of the large
  scale use of convalescent plasma to treat severe COVID-19 patients</a>\nb
 y Xi Huo (University of Miami) as part of COVID-19 Math Modelling Seminar\
 n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kevin Bryan (University of Toronto)
DTSTART:20200818T143000Z
DTEND:20200818T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/13
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/13/">A Calibrated Equilibrium Model of the Covi
 d Shock</a>\nby Kevin Bryan (University of Toronto) as part of COVID-19 Ma
 th Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hiroshi Nishiura (Kyoto University)
DTSTART:20200825T143000Z
DTEND:20200825T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/14
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/14/">COVID-19 in Japan</a>\nby Hiroshi Nishiura
  (Kyoto University) as part of COVID-19 Math Modelling Seminar\n\nAbstract
 : TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yanni Xiao (Xi'an Jiaotong University)
DTSTART:20200901T133000Z
DTEND:20200901T143000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/15
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/15/">Modelling the effect of limitation of medi
 cal resources on COVID-19 infection</a>\nby Yanni Xiao (Xi'an Jiaotong Uni
 versity) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kumar Murty (Fields Institute)
DTSTART:20200908T143000Z
DTEND:20200908T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/16
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/16/">Integrating health and economic parameters
 </a>\nby Kumar Murty (Fields Institute) as part of COVID-19 Math Modelling
  Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jacques Belair (Université Montréal)
DTSTART:20200915T143000Z
DTEND:20200915T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/17
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/17/">Pandemic in films: a mathematical modellin
 g approach</a>\nby Jacques Belair (Université Montréal) as part of COVID
 -19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Seyed Moghadas (York University)
DTSTART:20200922T143000Z
DTEND:20200922T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/18
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/18/">Understanding COVID-19 and its Public Heal
 th Challenges</a>\nby Seyed Moghadas (York University) as part of COVID-19
  Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nicholas Denis (Stat Can)
DTSTART:20200929T143000Z
DTEND:20200929T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/19
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/19/">Learning optimal mitigation strategies thr
 ough agent based reinforcement learning</a>\nby Nicholas Denis (Stat Can) 
 as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dexen Xi (University of Western Ontario)
DTSTART:20201006T143000Z
DTEND:20201006T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/20
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/20/">Joint Modeling of Hospitalization and Mort
 ality of Ontario Covid-19 cases</a>\nby Dexen Xi (University of Western On
 tario) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hauiping Zhu (York University)
DTSTART:20201013T143000Z
DTEND:20201013T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/21
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/21/">Fangcang shelter hospitals and control of 
 the SARS-CoV-2 epidemic in Wuhan</a>\nby Hauiping Zhu (York University) as
  part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:JC Loredo-Osti (Memorial University of Newfoundland)
DTSTART:20201020T143000Z
DTEND:20201020T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/22
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/22/">COVID-19 management in Atlantic Canada</a>
 \nby JC Loredo-Osti (Memorial University of Newfoundland) as part of COVID
 -19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ali Asgary (York University)
DTSTART:20201103T153000Z
DTEND:20201103T163000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/24
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/24/">A Simulation Based AI Model for Preventati
 ve Testing of SARS-CoV-2 in Schools</a>\nby Ali Asgary (York University) a
 s part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/24/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chris Budd (University of Bath)
DTSTART:20201027T143000Z
DTEND:20201027T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/25
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/25/">COVID\, V-KEMs and Modeling Retail and Hig
 her Education</a>\nby Chris Budd (University of Bath) as part of COVID-19 
 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sharmistha Mishra (University of Toronto)
DTSTART:20201110T153000Z
DTEND:20201110T163000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/26
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/26/">How to get away with inequity in outbreaks
 : a SARS-CoV-2 modeling story</a>\nby Sharmistha Mishra (University of Tor
 onto) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jianhong Wu (York University)
DTSTART:20201117T153000Z
DTEND:20201117T163000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/27
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/27/">Quantifying the shift in social contact pa
 tterns in response to non-pharmaceutical interventions</a>\nby Jianhong Wu
  (York University) as part of COVID-19 Math Modelling Seminar\n\nAbstract:
  TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alessandra Micheletti (Università degli Studi di Milano)
DTSTART:20201201T153000Z
DTEND:20201201T163000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/28
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/28/">Covid-19 in Italy: a provincial modelling 
 using an adjusted time-dependent SIRD model and a wavelet and cross-correl
 ation data analysis in Lombardy region.</a>\nby Alessandra Micheletti (Uni
 versità degli Studi di Milano) as part of COVID-19 Math Modelling Seminar
 \n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/28/
END:VEVENT
BEGIN:VEVENT
SUMMARY:James Watmough (University of New Brunswick)
DTSTART:20201222T153000Z
DTEND:20201222T163000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/29
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/29/">Epidemics\, Endemicity\, and Herd Immunity
 </a>\nby James Watmough (University of New Brunswick) as part of COVID-19 
 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/29/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mark Penney Yigit Yargic (University of Waterloo)
DTSTART:20210105T153000Z
DTEND:20210105T163000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/31
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/31/">Vaccine Prioritisation Using Bluetooth Exp
 osure Notification Apps</a>\nby Mark Penney Yigit Yargic (University of Wa
 terloo) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/31/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Francesca Scarabel
DTSTART:20210112T180000Z
DTEND:20210112T190000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/32
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/32/">A renewal equation model for disease trans
 mission dynamics with contact tracing</a>\nby Francesca Scarabel as part o
 f COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/32/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Michael Li (University of Alberta)
DTSTART:20210119T180000Z
DTEND:20210119T190000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/33
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/33/">Estimation of the Proportion of Population
  Infected with COVID-19 using  SIR Models</a>\nby Michael Li (University o
 f Alberta) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/33/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Morgan Craig (Université de Montréal)
DTSTART:20210126T180000Z
DTEND:20210126T190000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/34
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/34/">Identifying mechanisms of immunopathology 
 in severe COVID-19 in a realistic virtual patient cohort</a>\nby Morgan Cr
 aig (Université de Montréal) as part of COVID-19 Math Modelling Seminar\
 n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/34/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Glen Webb (Vanderbilt University)
DTSTART:20210202T180000Z
DTEND:20210202T190000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/35
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/35/">A Model of COVID-19 Epidemics for Predicti
 ng the Impact of Vaccination</a>\nby Glen Webb (Vanderbilt University) as 
 part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/35/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bruce Mellado (University of the Witwatersrand)
DTSTART:20210209T180000Z
DTEND:20210209T190000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/36
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/36/">From the discovery of the Higgs boson to M
 odelling the COVID-19 pandemic</a>\nby Bruce Mellado (University of the Wi
 twatersrand) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/36/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Swetaprovo Chaudhuri (University of Toronto)
DTSTART:20210216T153000Z
DTEND:20210216T163000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/37
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/37/">Constructing a Covid-19 disease spread mod
 el from the flow physics of infectious droplets and aerosols</a>\nby Sweta
 provo Chaudhuri (University of Toronto) as part of COVID-19 Math Modelling
  Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/37/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Peter Klimek (Medical University of Vienna)
DTSTART:20210309T153000Z
DTEND:20210309T163000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/38
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/38/">Modelling the impact of non-pharmaceutical
  interventions on the spread of SARS-CoV-2 in schools\, nursing homes\, Au
 stria\, and the world</a>\nby Peter Klimek (Medical University of Vienna) 
 as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/38/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Johannes Müller (Technical University of Münich)
DTSTART:20210302T153000Z
DTEND:20210302T163000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/39
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/39/">Contact Tracing & Super-Spreaders in the B
 ranching-Process Model</a>\nby Johannes Müller (Technical University of M
 ünich) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/39/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Quentin Griette (Université de Bordeaux)
DTSTART:20210316T143000Z
DTEND:20210316T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/40
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/40/">Phenomenological models and applications t
 o the SARS-CoV-2 epidemic</a>\nby Quentin Griette (Université de Bordeaux
 ) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/40/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Carolin Colijn (Simon Fraser University)
DTSTART:20210323T143000Z
DTEND:20210323T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/41
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/41/">Modelling and policy in the COVID-19 pande
 mic</a>\nby Carolin Colijn (Simon Fraser University) as part of COVID-19 M
 ath Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/41/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sara Otto (University of British Columbia)
DTSTART:20210330T143000Z
DTEND:20210330T153000Z
DTSTAMP:20260404T095031Z
UID:covid-19-math-modelling-seminar/42
DESCRIPTION:Title: <a href="https://stable.researchseminars.org/talk/covid
 -19-math-modelling-seminar/42/">SARS-CoV-2 Evolution</a>\nby Sara Otto (Un
 iversity of British Columbia) as part of COVID-19 Math Modelling Seminar\n
 \nAbstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/covid-19-math-modelling-
 seminar/42/
END:VEVENT
END:VCALENDAR