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BEGIN:VEVENT
SUMMARY:Ping Yan (Public Health Agency of Canada)
DTSTART:20200519T143000Z
DTEND:20200519T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/1
DESCRIPTION:Title: Some preliminary trend analysis of COVID-19 in fiv
e selected provinces based on data reported\nby Ping Yan (Public Healt
h Agency of Canada) as part of COVID-19 Math Modelling Seminar\n\n\nAbstra
ct\nThe most recognized trends of transmission of COVID-19 are that based
on publicly reported data. They are updated and disseminated by provinces
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/coronavirus/c
ountry/canada/. These trends are typically presented by date of report. Th
ey are strongly affected by confounding factors such as testing patterns\,
reporting patterns\, computer glitches\, weekdays vs weekends\, etc. A di
fferent trend presentation is the “Epidemic curve” based on date of il
lness onset*\, such as that published by Government of Canada on Canada.ca
. This is more relevant to the disease transmission. However\, reporting d
elays make the numbers of cases with date of onset in recent days tend to
be more incomplete compared to cases with dates of onset quite a long 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 this repor
ting delay in order to illustrate the trend in the recent past\, as a form
of now-casting. Reporting delay adjustments are performed by using surviv
al analysis techniques for right-truncated data to calculate adjust weight
s. The mot relevant epidemic trends should be presented by dates at transm
ission\, but they are not directly observable from data. They are revealed
through statistical models that take the incubation time distribution int
o account. If we call the reporting delay adjustment “now-casting”\, t
hen the estimated trends by dates of transmission is “back-casting”. T
he algorithm is applied to the reporting delay adjusted trend by date of o
nset. We demonstrate our results based on two-step analysis of reported da
ta in five selected provinces with Step 1: now-casting trends by date of o
nset through reporting delay analysis\, and Step 2: back-casting trends by
date of transmission. For each province\, trend representations are plott
ed on the same chart according to three different event markers: by time a
t transmission\, by time of onset and by time of reporting. The trend by d
ate of transmission and the trend by date of onset share close resemblance
\, separated by approximately 5 days of the average incubation periods. Th
e trend by date of transmission and the trend by date of report are far ap
art by 10 days or more. They still have some resemblance. The trend by dat
e of report\, as the most visible trend that the public sees\, not only re
flects the past transmission taking place 10 to 15 days prior\, but also i
nfluenced by other confounding factors. The importance messages of these r
esults are: (i) recognition of different transmission patterns and timing
in different provinces\; (ii) recognition of the time-delay between what w
e see based on reported data and what might have happened in the past\; (i
ii) challenges to mathematical modelling in general while discussing fitti
ng models to data\; (iv) future directions for both modelling and for impr
ovement of data collection.\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Connell McCluskey (Wilfrid Laurier University)
DTSTART:20200526T143000Z
DTEND:20200526T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/2
DESCRIPTION:Title: Accounting for heterogeneity in social distancing<
/a>\nby Connell McCluskey (Wilfrid Laurier University) as part of COVID-19
Math Modelling Seminar\n\n\nAbstract\nI will present a deterministic comp
artmental model for COVID-19 with 2 main subgroups: one group that does so
cial distancing and one that doesn't. We vary the number of contacts for t
he social distancing group\, while keeping the basic reproduction number R
0 fixed (by changing the relative sizes of the two groups). We see that th
e peak number of infections changes dramatically\, dropping by as much as
70%\, while the initial growth rate and timing of the peak remain constant
. This suggests that heterogeneity in social distancing is fundamentally i
mportant.\n\nAs economies open up\, heterogeneity will continue to be impo
rtant.\n\nBio: \n\nConnell McCluskey received his PhD in 2002 from the Uni
versity of Alberta\, receiving the CAIMS Doctoral Dissertation Award. Afte
r postdoctoral research at the University of Victoria and McMaster Univers
ity\, he moved to Wilfrid Laurier University in Waterloo\, Ontario. He spe
nt 2011 as a visiting professor at the Université Victor Segalen Bordeaux
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://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/3
DESCRIPTION:Title: Time is of the essence: impact of delays on effect
iveness of contact tracing for COVID-19\nby Mirjam Kretzschmar (Univer
sity Medical Center Utrecht) as part of COVID-19 Math Modelling Seminar\n\
n\nAbstract\nWith confirmed cases of COVID-19 declining in many countries\
, lockdown measures are gradually being lifted. However\, even if most soc
ial distancing measures are continued\, other public health measures will
be needed to control the epidemic. Contact tracing either via conventional
methods or via mobile app technology is central to control strategies dur
ing de-escalation of social distancing. In my talk\, I will present result
s on the impact of timeliness and completeness on the effectiveness of con
tact tracing strategies (CTS). To analyze the impact of various steps of a
CTS\, we developed a stochastic mathematical model with explicit time del
ays between time of infection\, symptom onset\, diagnosis by testing\, and
isolation. The model also includes tracing of close contacts (e.g. househ
old members) and casual contacts with different delays and coverages. We c
omputed effective reproduction numbers of a CTS for a population with soci
al distancing measures and various scenarios for testing\, isolation of in
dex cases\, and tracing and quarantine of their contacts. We quantified th
e percent onward transmissions per diagnosed index case that can be preven
ted by CTS depending on timeliness and completeness of isolation and traci
ng.\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA (TBA)
DTSTART:20200609T143000Z
DTEND:20200609T153000Z
DTSTAMP:20260422T213051Z
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://researchseminars.org/talk/covid-19-math-modelling-seminar
/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Julien Arino (University of Manitoba)
DTSTART:20200616T143000Z
DTEND:20200616T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/5
DESCRIPTION:Title: Considerations on the spatial and temporal spread
of COVID-19\nby Julien Arino (University of Manitoba) as part of COVID
-19 Math Modelling Seminar\n\n\nAbstract\nPlease register here: https://zo
om.us/meeting/register/tJMrdOCsrz4jH9SXpn1gJLgslrwytZmiaOZv .\n\nWhile the
exact origin and date of start of circulation of SARS-CoV-2 is as yet unc
ertain\, it appears that the disease it causes\, COVID-19\, started its in
ternational spread after an amplification stage that happened in Wuhan (Ch
ina) in late 2019. Roughly six months later\, COVID-19 has been reported i
n most countries and territories worldwide. In this talk\, I will present
various models I have studied that aim to understand and sometimes predict
this spatial spread. The work revolves around a slight complexification o
f an SLIAR model that Brauer\, van den Driessche\, Watmough\, Wu and mysel
f had studied in preparation for the last pandemic (H1N1). I will first pr
esent this base model and briefly detail its properties. I will then show
how the model is being used in three different contexts: a) determination
of places most likely to next import the disease\, b) assessment of the ri
sk of importation of the disease to an uninfected location and c) evaluati
on of heterogeneity of transmission characteristics in different locations
.\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Farzali Izadi (Urmia University)
DTSTART:20200623T143000Z
DTEND:20200623T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/6
DESCRIPTION:Title: Generalized additive models to capture the death r
ates in Canada COVID-19\nby Farzali Izadi (Urmia University) as part o
f COVID-19 Math Modelling Seminar\n\n\nAbstract\nPlease register here: htt
ps://zoom.us/meeting/register/tJMrdOCsrz4jH9SXpn1gJLgslrwytZmiaOZv .\n\nTo
capture the death rates and strong weekly pattern in the COVID-19\, we ut
ilize the generalized additive model in \n\nthe absence of direct statisti
cally based measurement of infection rates. By examining the death rates o
f Canada in general and Quebec\, Ontario and Alberta in particular\, one c
an easily figure out that there is substantial overdispersion relative to
the Poisson so that the negative binomial distribution is an appropriate c
hoice for the analysis. Generalized additive models (GAMs) are one of the
main modeling tools for data analysis. GAMs can efficiently combine differ
ent types of fixed\, random and smooth terms in the linear predictor of a
regression model to account for different types of effects. GAMs are a sem
i-parametric extension of generalized linear models (GLMs)\, used often fo
r the case when there is no a priori reason for choosing a particular resp
onse function (such as linear\, quadratic\, etc.) and need the data to spe
ak for themselves. GAMs do this via the smoothing functions and take each
predictor variable in the model and separate it into sections (delimited b
y knots) and then fit polynomial functions to each section separately\, wi
th the constraint that there are no links at the knots (second derivatives
of the separate functions are equal at the knots).\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Troy Day (Queen's University)
DTSTART:20200630T143000Z
DTEND:20200630T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/7
DESCRIPTION:Title: The Political Economy of Infectious Disease Outbre
aks\nby Troy Day (Queen's University) as part of COVID-19 Math Modelli
ng Seminar\n\n\nAbstract\nPeople's incentives during an infectious disease
outbreak influence their behaviour\, and this behaviour can impact how th
e outbreak unfolds. Early on during an outbreak\, people are at little per
sonal risk of infection and hence may be unwilling to change their lifesty
le to slow the spread of disease. As the number of cases grows\, however\,
people may then voluntarily take extreme measures to limit their exposure
. Government leaders also respond to the welfare and changing desires of t
heir constituents\, through public health policies that themselves shape t
he course of the epidemic and its ultimate health and economic repercussio
ns. In this talk I will use ideas from the study of differential games to
model how individuals’ and government leaders’ incentives change durin
g an outbreak\, and the epidemiological and economic consequences that ens
ue when these incentives are acted upon. Motivated by the current COVID-19
pandemic\, I focus on physical distancing behaviour and the imposition of
stay-at-home orders. I show that there is a fundamental difference in the
economic and health consequences of an infectious disease outbreak depend
ing on the degree of asymptomatic transmission. If transmission occurs pri
marily by asymptomatic carriers\, then government leaders will be incentiv
ized to impose stay-at-home orders earlier and for longer than individuals
would like. Despite such orders being unpopular\, however\, they ultimate
ly benefit all individuals. On the other hand\, if the disease is transmit
ted primarily by symptomatic infections\, then individuals can be incentiv
ized to stay at home earlier and for longer than government leaders would
like. In this case\, politicians can be incentivized to impose back-to-wor
k orders that\, despite being unpopular\, will again ultimately be to the
benefit of all individuals.\n\nThis is joint work with David McAdams\, Fuq
ua School of Business and Economics Department\, Duke University.\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Beate Sander (University Health Network)
DTSTART:20200714T143000Z
DTEND:20200714T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/8
DESCRIPTION:Title: Forecasting PPE demand for Ontario acute care hosp
itals during COVID-19\nby Beate Sander (University Health Network) as
part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/9
DESCRIPTION:Title: Diverse local epidemics reveal the effects of geog
raphy\, climate\, and susceptible depletion in the first wave of COVID-19
in the US\nby Niayesh Afshordi (Perimeter Institute for Theoretical Ph
ysics) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/10
DESCRIPTION:Title: A logistic growth model with logistically varying
carrying capacity for Covid-19 deaths using data from Ontario\, Canada\nby Georges Bucyibaryta / Charmaine Dean (University of Waterloo) as par
t of COVID-19 Math Modelling Seminar\n\n\nAbstract\nWe consider a logistic
growth model with its carrying capacity parameter allowed to vary logisti
cally\, for modeling the cumulative number of deaths due to Covid-19 in th
e province of Ontario. In particular\, the parameter modeling carrying cap
acity is linked to the number of hospitalizations. Although this is an emp
irical model\, it incorporates conceptual elements that support the framew
ork required for modeling Covid-19 or more generally\, any infectious dise
ase where hospitalizations are required in management of the disease. By n
ature\, the logistic growth model is deterministic\, and so we induce stoc
hasticity through incorporating the variability that is observed in modeli
ng the daily counts and propose a tool that can be used to quantify the be
havior of the disease within a short time period\, for example to predict
the number of deaths based on new hospitalizations six days earlier. The s
tochasticity in the daily number of deaths is modeled using a negative bin
omial distribution. We develop an indicator of a shift in the trend of the
cumulative number of deaths that could be used to monitor resurgence of t
he disease\, and hence serve as a marker for public health intervention.\n
\nCharmaine Dean is Vice-President\, Research and Professor in the Departm
ent of Statistics and Actuarial Science at the University of Waterloo. Her
research interest lies in the development of methodology for disease mapp
ing\, longitudinal studies\, the design of clinical trials\, and spatio-te
mporal analyses. Much of this work has been motivated by direct applicatio
ns to important practical problems in biostatistics and ecology.\n\nGeorge
s Bucyibaruta is a postdoctoral fellow in the Department of Statistics and
Actuarial Science at University of Waterloo\, working jointly with Dr Cha
rmaine Dean and Dr Mahmoud Torabi at the University of Manitoba. His main
research interests are related to spatial analysis and infectious disease
modeling. He completed his PhD in Probability and Statistics at the Centro
de Investigación en Matemáticas (CIMAT) in Guanajuato\, Mexico.\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/11
DESCRIPTION:Title: Analysis and forecasting for COVID-19\nby Pie
ter Houtekamer (Environment and Climate Change Canada) as part of COVID-19
Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Xi Huo (University of Miami)
DTSTART:20200811T143000Z
DTEND:20200811T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/12
DESCRIPTION:Title: Effectiveness and feasibility of the large scale
use of convalescent plasma to treat severe COVID-19 patients\nby Xi Hu
o (University of Miami) as part of COVID-19 Math Modelling Seminar\n\nAbst
ract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kevin Bryan (University of Toronto)
DTSTART:20200818T143000Z
DTEND:20200818T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/13
DESCRIPTION:Title: A Calibrated Equilibrium Model of the Covid Shock
\nby Kevin Bryan (University of Toronto) as part of COVID-19 Math Mode
lling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hiroshi Nishiura (Kyoto University)
DTSTART:20200825T143000Z
DTEND:20200825T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/14
DESCRIPTION:Title: COVID-19 in Japan\nby Hiroshi Nishiura (Kyoto
University) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/15
DESCRIPTION:Title: Modelling the effect of limitation of medical res
ources on COVID-19 infection\nby Yanni Xiao (Xi'an Jiaotong University
) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kumar Murty (Fields Institute)
DTSTART:20200908T143000Z
DTEND:20200908T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/16
DESCRIPTION:Title: Integrating health and economic parameters\nb
y Kumar Murty (Fields Institute) as part of COVID-19 Math Modelling Semina
r\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jacques Belair (Université Montréal)
DTSTART:20200915T143000Z
DTEND:20200915T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/17
DESCRIPTION:Title: Pandemic in films: a mathematical modelling appro
ach\nby Jacques Belair (Université Montréal) as part of COVID-19 Mat
h Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Seyed Moghadas (York University)
DTSTART:20200922T143000Z
DTEND:20200922T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/18
DESCRIPTION:Title: Understanding COVID-19 and its Public Health Chal
lenges\nby Seyed Moghadas (York University) as part of COVID-19 Math M
odelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nicholas Denis (Stat Can)
DTSTART:20200929T143000Z
DTEND:20200929T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/19
DESCRIPTION:Title: Learning optimal mitigation strategies through ag
ent based reinforcement learning\nby Nicholas Denis (Stat Can) as part
of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/20
DESCRIPTION:Title: Joint Modeling of Hospitalization and Mortality o
f Ontario Covid-19 cases\nby Dexen Xi (University of Western Ontario)
as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hauiping Zhu (York University)
DTSTART:20201013T143000Z
DTEND:20201013T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/21
DESCRIPTION:Title: Fangcang shelter hospitals and control of the SAR
S-CoV-2 epidemic in Wuhan\nby Hauiping Zhu (York University) as part o
f COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/22
DESCRIPTION:Title: COVID-19 management in Atlantic Canada\nby JC
Loredo-Osti (Memorial University of Newfoundland) as part of COVID-19 Mat
h Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ali Asgary (York University)
DTSTART:20201103T153000Z
DTEND:20201103T163000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/24
DESCRIPTION:Title: A Simulation Based AI Model for Preventative Test
ing of SARS-CoV-2 in Schools\nby Ali Asgary (York University) as part
of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/24/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chris Budd (University of Bath)
DTSTART:20201027T143000Z
DTEND:20201027T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/25
DESCRIPTION:Title: COVID\, V-KEMs and Modeling Retail and Higher Edu
cation\nby Chris Budd (University of Bath) as part of COVID-19 Math Mo
delling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sharmistha Mishra (University of Toronto)
DTSTART:20201110T153000Z
DTEND:20201110T163000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/26
DESCRIPTION:Title: How to get away with inequity in outbreaks: a SAR
S-CoV-2 modeling story\nby Sharmistha Mishra (University of Toronto) a
s part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jianhong Wu (York University)
DTSTART:20201117T153000Z
DTEND:20201117T163000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/27
DESCRIPTION:Title: Quantifying the shift in social contact patterns
in response to non-pharmaceutical interventions\nby Jianhong Wu (York
University) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/28
DESCRIPTION:Title: Covid-19 in Italy: a provincial modelling using a
n adjusted time-dependent SIRD model and a wavelet and cross-correlation d
ata analysis in Lombardy region.\nby Alessandra Micheletti (Universit
à degli Studi di Milano) as part of COVID-19 Math Modelling Seminar\n\nAb
stract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/29
DESCRIPTION:Title: Epidemics\, Endemicity\, and Herd Immunity\nb
y James Watmough (University of New Brunswick) as part of COVID-19 Math Mo
delling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/31
DESCRIPTION:Title: Vaccine Prioritisation Using Bluetooth Exposure N
otification Apps\nby Mark Penney Yigit Yargic (University of Waterloo)
as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/31/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Francesca Scarabel
DTSTART:20210112T180000Z
DTEND:20210112T190000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/32
DESCRIPTION:Title: A renewal equation model for disease transmission
dynamics with contact tracing\nby Francesca Scarabel as part of COVID
-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/32/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Michael Li (University of Alberta)
DTSTART:20210119T180000Z
DTEND:20210119T190000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/33
DESCRIPTION:Title: Estimation of the Proportion of Population Infect
ed with COVID-19 using SIR Models\nby Michael Li (University of Alber
ta) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/33/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Morgan Craig (Université de Montréal)
DTSTART:20210126T180000Z
DTEND:20210126T190000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/34
DESCRIPTION:Title: Identifying mechanisms of immunopathology in seve
re COVID-19 in a realistic virtual patient cohort\nby Morgan Craig (Un
iversité de Montréal) as part of COVID-19 Math Modelling Seminar\n\nAbst
ract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/34/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Glen Webb (Vanderbilt University)
DTSTART:20210202T180000Z
DTEND:20210202T190000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/35
DESCRIPTION:Title: A Model of COVID-19 Epidemics for Predicting the
Impact of Vaccination\nby Glen Webb (Vanderbilt University) as part of
COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/36
DESCRIPTION:Title: From the discovery of the Higgs boson to Modellin
g the COVID-19 pandemic\nby Bruce Mellado (University of the Witwaters
rand) as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/36/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Swetaprovo Chaudhuri (University of Toronto)
DTSTART:20210216T153000Z
DTEND:20210216T163000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/37
DESCRIPTION:Title: Constructing a Covid-19 disease spread model from
the flow physics of infectious droplets and aerosols\nby Swetaprovo C
haudhuri (University of Toronto) as part of COVID-19 Math Modelling Semina
r\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/38
DESCRIPTION:Title: Modelling the impact of non-pharmaceutical interv
entions on the spread of SARS-CoV-2 in schools\, nursing homes\, Austria\,
and the world\nby Peter Klimek (Medical University of Vienna) as part
of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/39
DESCRIPTION:Title: Contact Tracing & Super-Spreaders in the Branchin
g-Process Model\nby Johannes Müller (Technical University of Münich)
as part of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/39/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Quentin Griette (Université de Bordeaux)
DTSTART:20210316T143000Z
DTEND:20210316T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/40
DESCRIPTION:Title: Phenomenological models and applications to the S
ARS-CoV-2 epidemic\nby Quentin Griette (Université de Bordeaux) as pa
rt of COVID-19 Math Modelling Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/40/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Carolin Colijn (Simon Fraser University)
DTSTART:20210323T143000Z
DTEND:20210323T153000Z
DTSTAMP:20260422T213051Z
UID:covid-19-math-modelling-seminar/41
DESCRIPTION:Title: Modelling and policy in the COVID-19 pandemic
\nby Carolin Colijn (Simon Fraser University) as part of COVID-19 Math Mod
elling Seminar\n\nAbstract: TBA\n
LOCATION:https://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:20260422T213051Z
UID:covid-19-math-modelling-seminar/42
DESCRIPTION:Title: SARS-CoV-2 Evolution\nby Sara Otto (Universit
y of British Columbia) as part of COVID-19 Math Modelling Seminar\n\nAbstr
act: TBA\n
LOCATION:https://researchseminars.org/talk/covid-19-math-modelling-seminar
/42/
END:VEVENT
END:VCALENDAR