[ABE-L] grupo de leitura DME/UFRJ - Tema: Inferência Bayesiana semi-paramétrica aplicado a COVID-19

[Viviana Lobo]

Caros redistas,

Gostaria de convidar a todos para participarem da próxima sessão do grupo
de leitura promovido pelo Departamento de Métodos Estatísticos da UFRJ
sobre Modelagem de Epidemias. Nosso próximo encontro será na terça feira às
16hs com a apresentação do Professor Tianjian Zhou sobre inferência
Bayesiana semi-paramétrica em modelo de espaço de estados aplicado à COVID.
Tianjian Zhou possui bacharelado em Estatística pela Universidade de
Ciência e Tecnologia da China, PhD em Estatística pela Universidade do
Texas em Austin (2017) e atualmente é Professor Assistente na Universidade
do Estado do Colorado.

O link para a reunião é
https://meet.google.com/fnv-fepa-khj

A sala será aberta 10 minutos antes das 16:00 na terça 18/08.

O resumo da próxima semana segue abaixo.

Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19
with a State-Space Model

Abstract: The outbreak of Coronavirus Disease 2019 (COVID-19) is an ongoing
pandemic affecting over 200 countries and regions. Inference about the
transmission dynamics of COVID-19 can provide important insights into the
speed of disease spread and the effects of mitigation policies. We develop
a novel Bayesian approach to such inference based on a probabilistic
compartmental model using data of daily confirmed COVID-19 cases. In
particular, we consider a probabilistic extension of the classical
susceptible-infectious-recovered model, which takes into account
undocumented infections and allows the epidemiological parameters to vary
over time. We estimate the disease transmission rate via a Gaussian process
prior, which captures nonlinear changes over time without the need of
specific parametric assumptions. Predictions for future observations are
done by sampling from their posterior predictive distributions. Our
approach is applied to COVID-19 data from the United States, and the
analysis results are available at http://covid19.laiyaconsulting.com/baysir.
An R package BaySIR is made available at
https://github.com/tianjianzhou/BaySIR for the public to conduct
independent analysis or reproduce the results in this paper.


As informações sobre o grupo de leitura seguem abaixo:

Our webpage is https://sites.google.com/site/thaisf/Home/reading-group

Group description:

This group aims to study the essential aspects of modeling epidemics and
statistical models used to understand and predict outbreaks in disease
modeling.

Currently, our meetings are held once per week to discuss articles
published in reference journals about statistical epidemic modelling.
The group is organized by Thais C O Fonseca, Mariane Branco Alves, Kelly C
M Gonçalves , Viviana G R Lobo and Carlos Tadeu Zanini (Departament of
Statistics, UFRJ, Brazil).

We meet using Meets platform to allow researchers around all locations to
take part and to share their experiences. If you would like to join this
reading group, please use the google groups ModelingEpidemics_DME_UFRJ to
join the group and contact us. We will be glad to have you as a member of
this reading group if you are interested.

abs
viviana


-- 
Viviana Lobo






<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail>
Livre
de vírus. www.avast.com
<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail>.
<#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
-------------- Próxima Parte ----------
Um anexo em HTML foi limpo...
URL: <https://lists.ime.usp.br/archives/abe/attachments/20200817/aa36158e/attachment.html>