About us
This group aims to study 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.
Schedule Guide
Please find below the schedule for our weekely meetings. The meetings have duration of 2 hours and start at 16:00 on Tuesdays.
Focus: introduction about the reading group and the purposes. SIR model.
Group leaders: Thais Fonseca and Viviana Lobo
Date: 16/06/2020
H. H. Weiss. The sir model and the foundations of public health. Materials matematics, pages 1–17, 2013.
Focus: SIR model and other sources of information about epidemics. The State-space model used to model epidemics.
Group leaders: Viviana Lobo and Carlos Tadeu
Date: 23/06/2020
V. Dukic, H. F. Lopes, and N. G. Polson. Tracking epidemics with google
flu trends data and a state-space seir model. Journal of the American
Statistical Association, 107(500):1410–426, 2012.
Focus: The State-space model used to model epidemics.
Group leaders: Kelly Gonçalves and Mariane Branco
Date: 30/06/2020
Dave Osthus, Kyle S Hickmann, Petrut ̧a C Caragea, Dave Higdon, and
Sara Y Del Valle. Forecasting seasonal influenza with a state-space sir
model. The annals of applied statistics, 11(1):202, 2017.
Focus: Exponential grouth models.
Group leaders: Mariane Branco and Kelly Gonçalves
Date: 07/07/2020
Helio S. Migon and Dani Gamerman, Forecasting the Number of AIDS Cases in Brazil, Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 40, No. 4, 1991, pp. 427-442
Focus: Under-reporting in count data.
Group leaders: Thais Fonseca and Carlos Tadeu
Date: 14/07/2020
Oliver Stoner, Theo Economou & Gabriela Drummond Marques da Silva, Hierarchical Framework for Correcting Under-Reporting in Count Data, JASA, 2019.
Focus: Grouth models.
Presenter: Dani Gamerman
Date: 21/07/2020
CovidLP: um aplicativo para prever a evolução da Covid19 baseado nos dados
Esta apresentação será dedicada a um aplicativo para previsão de curto e longo prazos para a pandemia da Covid19. Essas previsões são completamente baseadas nos dados observados diariamente de casos confirmados e mortes, sem utilização de modelagem epidemiológica. Nossas previsões permitem estimar características relevantes da pandemia, como pico e fim dos casos/mortes e número total de casos/mortes. Toda a inferência é sumarizada em preditores pontuais, acompanhados dos respectivos intervalos de credibilidade. Desafios associadas ao desenvolvimento de um sistema de larga escala para vários paises e estados também serão descritos. Esse projeto está sendo desenvolvido por uma equipe de docentes, pesquisadores e alunos de PG do Departamento de Estatística da UFMG.
Focus: Overdispersion and the second wave
Presenter: Guido Moreira
Date: 28/07/2020
Incluindo sobre-dispersão e múltiplas curvas no modelo de previsão de COVID19
O modelo por trás do app COVIDLP é um modelo Poisson com crescimento logístico. Esta palestra discute alguns desenvolvimentos por trás dos panos do app. Um problema intrínseco desse modelo é que a variância é muito maior do que a média, o que quebra a premissa da distribuição Poisson. Diferentes maneiras de modelar essa sobre-dispersão são discutidas. Além disso, a modelagem de uma curva com mais de um pico é discutida.
Focus: Infectious disease statistical modeling
Group leaders: Widemberg and Viviana Lobo
Date: 04/08/2020
Bayesian Disease Mapping - Hierarchical Modeling in Spatial Epidemiology, 3rd Edition By Andrew B. Lawson, Chapter 14: Infectious Disease Modeling.
Focus: Chain ladder approach
Presenter: Leonardo S. Bastos
Date: 11/08/2020
L.S. Bastos et al - A modelling approach for correcting reporting delays in disease surveillance data, Statistics in Medicine. 2019;38:4363–4377.
Focus: State-space SIR
Presenter:
Date: 18/08/2020
T. Zhou and Y. Ji, Semiparametri Bayesian inference for the transmission dynamics of Covid-19 with a state-space model, Technical report
Focus: Stochastic SEIR
Presenter: Thais Fonseca and Kelly Gonçalves
Date: 08/09/2020
Lekone, P. E. and Finkenst ̈adt, B. F. (2006). Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study. Biometrics, 62(4): 1170–1177.
Focus: Partial observations
Presenter: Viviana Lobo and Mariane Branco
Date: 22/09/2020
ONeill, P. D. and Roberts, G. O. (1999). Bayesian inference for partially observed stochastic epidemics. Journal of the Royal Statistical Society: Series A (Statistics in Society), 162(1):121–129.