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DTSTAMP;TZID=America/Vancouver:20221205T174500
DTSTART;TZID=America/Vancouver:20221205T174500
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UID:20221205T174500@prima2022.primamath.org
SUMMARY:Infectious disease modelling in the time of COVID
DESCRIPTION:The COVID-19 pandemic has made some ideas in infectious disease modelling
household names: $R_0$, $R_t$ and herd immunity were very much in the public
eye for many months. But the pandemic also raised new challenges for infectious
disease modelling. Much of ""classical"" infectious disease modelling focused
on setting up a model, determining its (usually local asymptotic) behaviour,
and discovering how its basic reproduction number depended on its parameters.
But in the era of the pandemic, we need models that can help us interpret data
in real time, that can cope with heterogeneity, that are suitable for modelling
feasible actions at a range of scales, and we need models that can incorporate
data describing viral diversity.  Some of the challenges call for
juxtapositions of mechanistic models and statistical models, describing the
distributions of variables we can observe. In this talk, I will describe new
modelling and estimation approaches that we have developed in this context.
First, I will introduce ""eventR"", which is like a basic reproduction number
for a specific event, and outline how it can be used to help compare strategies
for preventing transmission. Next, I will describe a mechanistic-statistical
model with which we can estimate a key parameter for any infectious disease
simulation: the per unit time, per contact transmission rate, as long as we
know enough about the transmission setting. I will link these two through an
analysis of the cluster size distributions in schools in four Canadian
provinces in 2021, and will describe and interpret our findings about the
transmission rates. Finally, I will discuss the broader challenges for
infectious disease modelling in this pandemic and beyond. 

STATUS:CONFIRMED
LOCATION:Grand Ballroom
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