[ABE-L] Fwd: Fw: [CIRS/SWB Webinar Series] July 26, Introduction to Bayesian Modelling of Epidemics: From Population to Individual-level Models

[Kelly Cristina]

Caros colegas,

Segue divulgação de Webinar no dia 26 de julho a pedido da professora
Alexandra Schmidt da McGill University.

Obrigada.

--------------------

Dear Colleagues,


I hope this message finds you well.


I apologize in advance for potential cross-posting.


The seventh edition of the Webinar Series
<https://community.amstat.org/committeeoninternationalrelationsinstatistics/events2/webinars>
of the *Committee on International Relations in Statistics (CIRS) from the
American Statistical Association (ASA)*  and *Statistics Without Borders,*
will be held on July 26th at 12pm EDT, please, help us advertise it.




*July 26, 2023, 12 PM EDT*
*"*
*Introduction to Bayesian Modelling of Epidemics: From Population to
Individual-level Models" *


*Presenter: Professor Rob Deardon, University of Calgary *

*Register here
<https://amstat.zoom.us/webinar/register/WN_bTWlPpxcTtWJ_yV4E3QdkQ>*


*Abstract: *

With the onset of the COVID-19 pandemic, there has been an understandable
increase in the interest in the mathematical and statistical modelling of
infectious disease epidemics. The modelling of infectious disease spread
through a population generally requires the use of non-standard statistical
models. This is primarily because infection events depend upon the
infection status of other members of the population (i.e. we cannot assume
independence of infection events).  Typically, statistical inference for
these models (e.g., parameter estimation) is done in a Bayesian context
using computational techniques such as Markov chain Monte Carlo (MCMC).
This is in part due to the non-standard form of the models, but also in
part due to the fact that we often have missing or uncertain data; for
example, infection times are rarely observed. Bayesian data augmentation
provides a natural framework for allowing for such uncertainty.  Further
complication is added by the fact that there are often complex
heterogeneities in the population which we wish to account for, since, for
example, populations do not tend to mix homogeneously. Sometimes, we may
wish to account for such heterogeneities using spatial mechanisms that
assume that transmission events are more likely to occur between
individuals close together in space than individuals further apart.
Sometimes, it is more natural to model such heterogeneities using contact
networks that represent, for example, the sharing of supplier companies
between farms.

Here, we will examine the main characteristics of both population-level and
individual-level infectious disease models, and how they can be fitted to
data in a Bayesian MCMC framework.

*Bio*: Rob Deardon is a Professor of Biostatistics with a joint position in
the Faculty of Veterinary Medicine and Department of Mathematics &
Statistics at the University of Calgary. Much of his recent work has been
in the area of infectious disease modelling, but he is also interested in
Bayesian & computational statistics, experimental design, disease
surveillance methods, spatio-temporal modelling and statistical learning.
He currently has a research group consisting of 15 trainees, and has
published 75+ papers in peer-reviewed journals. He has served as associate
editor of a number of journals including the Journal of the Royal
Statistical Society (Series C) and the Canadian Journal of Statistics. He
is currently the Graduate Coordinator of the Interdisciplinary
Biostatistics Graduate Program at Calgary, and has recently served a 2-year
term as the Chair of the Statistics Section of the NSERC Discovery Grant
Mathematics & Statistics Evaluation Group.


*For questions, or to suggest topics for future webinars contact Carolina
Franco at **franco-carolina em norc.org <franco-carolina em norc.org>** or
Alexandra Schmidt **alexandra.schmidt em mcgill.ca
<alexandra.schmidt em mcgill.ca>* *or Sloka Iyengar slokaiyengar1 em gmail.com
<slokaiyengar1 em gmail.com>*



We hope to see you there.
Best wishes,

Alexandra, Carolina and Sloka


Alexandra M. Schmidt
Professor - The University Chair
Program Director of Biostatistics
McGill University

*http://alex-schmidt.research.mcgill.ca/
<http://alex-schmidt.research.mcgill.ca/> *

--------------------------------


Atenciosamente,
-- 
*Kelly C. M. Gonçalves*
*Professora Associada I*
*Departamento de Métodos Estatísticos*
*Universidade Federal do Rio de Janeiro*

*https://sites.google.com/dme.ufrj.br/kelly/
<https://sites.google.com/dme.ufrj.br/kelly/>*
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