Granger causalidade e a dinâmica migratória do vírus da gripe

O objetivo desse trabalho é modelar a incidência da gripe no Brasil no mês t a partir dos dados de incidência e de diversidade genética coletados dos meses anteriores no hemisfério norte. Para tal irá se utilizar os métodos de Granger causalidade e regressão com defasagens. Os modelos propostos podem ser utilizados na previsão da incidência da gripe no Brasil, conhecimento que pode ser estratégico para a implementação de políticas de vacinação pelo governo bem como desenvolvimento de estratégias ótimas de controle de epidemias de gripe.The objective of this work is to model the incidence of influenza in Brazil in the month t from the data of incidence and genetic diversity collected from previous months in the northern hemisphere. For this purpose, the Granger-causality and regression methods with lags will be used. The proposed models can be used to predict the incidence of influenza in Brazil, knowledge that can be strategic for the implementation of vaccination policies by the government as well as the development of optimal strategies for controlling influenza epidemics

Bayesian analysis of beta autoregressive moving average models

O presente trabalho propõe uma abordagem Bayesiana para a estimação dos parâmetros do modelo βARMA(p, q), modelos de séries temporais para dados com suporte no intervalo (0, 1). Para tanto, emprega-se a técnica de amostragem Monte Carlo Hamiltoniano, reconhecida por sua eficiência computacional na estimação de parâmetros em modelos mais complexos. O estudo conduz simulações de Monte Carlo considerando modelos βARMA sob diversos cenários, bem como uma análise de sensibilidade com relação à escolha das prioris utilizadas e a detecção de raízes unitárias. Para ilustrar a aplicação da abordagem proposta, são utilizados dados de energia hidrelétrica como exemplo.The present work proposes a Bayesian approach to estimate the parameters of βARMA(p, q) models, which are used for time series data with values in the interval (0, 1). To achieve this goal, the study employs the Hamiltonian Monte Carlo sampling technique, which is known for its effectiveness in estimating parameters in complex models. The study also conducts Monte Carlo simulations to examine the performance of the proposed approach under different scenarios. Additionally, the sensitivity of the results to prior selection and unit roots detection is evaluated. To demonstrate the applicability of the proposed approach, the study provides an empirical analysis using hydroelectric energy data

Granger causality and time series regression for modelling the migratory dynamics of influenza into Brazil

cknowledgments.Aline F. Grande and Guilherme Pumi gratefully acknowledge the support of CNPq and FAPERGS. Gabriela B. Cybis gratefully acknowledges the support of the Serrapilheira Institute (grant number Serra-G1709-18939). The authors are also grateful to Rafaela Gomes de Jesus for helping with the genetic diversity data assembly.In this work we study the problem of modelling and forecasting the dynamics of the influenza virus in Brazil at a given month, from data on reported cases and genetic diversity collected from previous months, in other locations. Granger causality is employed as a tool to assess possible predictive relationships between covariates. For modelling and forecasting purposes, a time series regression approach is applied considering lagged information regarding reported cases and genetic diversity in other regions. Three different models are analysed, including stepwise time series regression and LASSO

Bayesian Analysis of Beta Autoregressive Moving Average Models

This work presents a Bayesian approach for the estimation of Beta Autoregressive Moving Average ($\beta$ARMA) models. We discuss standard choice for the prior distributions and employ a Hamiltonian Monte Carlo algorithm to sample from the posterior. We propose a method to approach the problem of unit roots in the model's systematic component. We then present a series of Monte Carlo simulations to evaluate the performance of this Bayesian approach. In addition to parameter estimation, we evaluate the proposed approach to verify the presence of unit roots in the model's systematic component and study prior sensitivity. An empirical application is presented to exemplify the usefulness of the method. In the application, we compare the fitted Bayesian and frequentist approaches in terms of their out-of-sample forecasting capabilities