Use of socioeconomic neighborhood matrices in STARMA class models applied to epidemiological data

Authors

Keywords:

Socioeconomic neighborhood matrix, STARMA, Tuberculosis

Abstract

In this work the use of socioeconomic neighborhood matrices was studied in space-time models of
the autoregressive and moving averages class (STARMA). The selected data set is composed of nine time series
that quantify the incidence rate of Tuberculosis observed between 2002 and 2017 in the following cities: Belo
Horizonte, Betim, Contagem, Governador Valadares, Juiz de Fora, Lavras, Montes Claros, Pouso Alegre and
Uberlândia. Since most cities are geographically distant, the use of socioeconomic neighborhood matrices
was necessary. The matrices were obtained through two socioeconomic variables: the municipal IDH and the
average annual investment in basic health. STARMA class models were adjusted considering the data set and
the two neighborhood matrices obtained. The model was obtained computationally and consisted of three
stages: Identification, estimation and diagnosis of the model. It was concluded that the socioeconomic
neighborhood matrices in STARMA models applied to the data set chosen were appropriate since these
matrices can be used in space-time series in which the places of interest are geographically distant.

Author Biographies

Matheus Feres Freitas, Universidade Federal de Lavras

Departamento de estatística. Estatística aplicada.

Haiany Aparecida Ferreira, Universidade Federal de Lavras

Departamento de estatística. Estatística aplicada.

Thelma Sáfadi, Universidade Federal de Lavras

Departamento de estatística. Estatística aplicada.

Daniella Feres Freitas, Universidade Federal de Lavras

Departanto de saúde- Saúde coletiva

References

ALMEIDA, E. Econometria espacial aplicada. Campinas–SP. Alínea, 2012. CHEYSSON F. (2016). starma: Modelling Space Time Auto Regressive Moving Average (STARMA). Processes. R package version 1.3. https://CRAN.R-project.org/package=starma GUIMARÃES, R. M. et al. Tuberculose, HIV e pobreza: tendência temporal no Brasil, Américas e mundo. Jornal Brasileiro de Pneumologia, v. 38, n. 4, p. 518-525, 2012. JIN, E. Y. Estrutura de vizinhanças espaciais nos modelos autorregressivos e de médias móveis espaço-temporais STARMA. Dissertação de Mestrado. Universidade de São Paulo, 2017.

PFEIFER, P. E.; DEUTRCH, S. J. A three-stage iterative procedure for space-time modeling phillip. Technometrics, v. 22, n. 1, p. 35-47, 1980.

R CORE TEAM (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Published

29-06-2019

How to Cite

Feres Freitas, M., Aparecida Ferreira, H., Sáfadi, T., & Feres Freitas, D. (2019). Use of socioeconomic neighborhood matrices in STARMA class models applied to epidemiological data. Sigmae, 8(2), 29–35. Retrieved from https://publicacoes.unifal-mg.edu.br/revistas/index.php/sigmae/article/view/928