Banca de QUALIFICAÇÃO: ITALO GIULLIAN CARVALHO DE ALBUQUERQUE

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : ITALO GIULLIAN CARVALHO DE ALBUQUERQUE
DATE: 02/10/2023
TIME: 08:00
LOCAL: meet.google.com/hfa-yosw-shv
TITLE:

Using Machine Learning for Quantification of Uncertainty in Predicting Incidence of Dengue and Chikungunya

PAGES: 40
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUMMARY:

    Dengue and chikungunya are two public health problems and, in order to combat them, it is necessary to know more and more about the factors that favor their development, proliferation and the emergence of new outbreaks. In this context, this study aims to develop a methodology for estimating uncertainty in the time series of dengue and chikungunya cases. The research was conducted in the neighborhoods of Copacabana, Jacarepaguá and Vila Militar in the city of Rio de Janeiro, using data collected by the Rio de Janeiro Health Department and the National Institute of Meteorology (INMET). Climate variables and machine learning techniques were used, including recursive autoregressive forecasting with exogenous variables, recursive autoregressive forecasting with customized predictors, and calculation of forecast intervals. The results showed significant insights into the relationship between climate factors and disease incidence. It was observed that seasonal patterns and fluctuations in climate variables correlate differently with the increased incidence of dengue and chikungunya cases. The effectiveness of the autoregressive model with personalized predictors, capable of capturing trends and seasonal fluctuations in time series, was highlighted. At the conclusion of the work, it is hoped that the methodology developed will contribute to a better understanding of the factors that influence the spread of dengue and chikungunya, and can also be a valuable tool for formulating public health policies. The ability to estimate uncertainty in the time series of these diseases opens the door to a more informed and adaptive approach to combating these public health threats.

COMMITTEE MEMBERS:
Presidente - Interno ao Programa - 1673092 - RONALDO CRISTIANO PRATI
Membro Titular - Examinador(a) Interno ao Programa - 1676329 - RAPHAEL YOKOINGAWA DE CAMARGO
Membro Titular - Examinador(a) Interno ao Programa - 3008222 - PAULO HENRIQUE PISANI
Membro Suplente - Examinador(a) Interno ao Programa - 1722875 - DAVID CORREA MARTINS JUNIOR