Gostaríamos 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.

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

Palestrante: Tianjian Zhou

Data: 18/08/2020
Horário: 16h
Acesse o link para transmissão AQUI. 

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.

Descrição do Grupo: 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.

Acesse a página do grupo AQUI.