Time Series Analysis of Tuberculosis in the State of Veracruz from 2000 to 2018

Keywords: Time Series Analysis, Stationarity, Pulmonary Tuberculosis Disease, ARIMA Models, Forecast

Abstract

This paper reviews the information from the General Direction of Epidemiology, particularly the registered cases of pulmonary Tuberculosis in the State of Veracruz, from 2000 to 2018.
This information, viewed as a time series, is analyzed using one of the most employed approaches for the study of time series. The behavior of the series is reviewed to find if it has any kind of constant or seasonal trend, seasonality tests are performed and finally integrated autoregressive moving average models are obtained, in order to make forecasts. The parameters of the best ARIMA models obtained are shown, as well as the values of the Akaike and Bayesian information criteria for the series reported by month and by epidemiological week.

Downloads

Download data is not yet available.

References

Aryee, G., E., K., Essuman, R., Agyei, A. N., Kudzawu, S., Djagbletey, R., Darkwa, E. O., and Forson, A. (2018). Estimating the incidence of tuberculosis cases reported at a tertiary hospital in ghana: a time series model approach. BMC Public Health, 18:1292.

Cavanaugh, J. E. and Neath, A. A. (2019). The akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. WIREs Computational Statatistics, 11:e1460.

Chen, Y., Wu, A., Wang, C., Zhou, H., and Feng, S. (2013). Time series analysis of pulmonary tuberculosis incidence: Forecasting by applying the time series model. Advanced Materials Research, 709:819–822.

Grolemund, G. and Wickham, H. (2011). Dates and times made easy with lubridate. Journal of Statistical Software, 40(3):1–25.

H., K. E. and Bae, J.-M. (2018). Seasonality of tuberculosis in the republic of korea, 2006-2016. Epidemiology and Health, 40:e2018051.

INSP (2019). Día mundial de la tuberculosis 2019. último acceso 22 de febrero de 2022.

INSP (2021). Tuberculosis: una epidemia que debería quedar en el pasado. último acceso 22 de febrero de 2022.

Khaliq, A., Batool, S. A., and Chaudhry, M. N. (2015). Seasonality and trend analysis of tuberculosis in lahore, pakistan from 2006 to 2013. Journal of Epidemiology and Global Health, 5(4):397–403.

Kohei, Y., Sumi, A., and Kobayashi, N. (2016). Time-series analysis of monthly age-specific numbers of newly registered cases of active tuberculosis in ja- pan from 1998 to 2013. Epidemiol. Infect., 144:2401–2414.

Kumar, V., Singh, A., Adhikary, M., Daral, S., Khokhar, A., and Singh, S. (2014). Seasonality of tuberculosis in delhi, india: A time series analysis. Tuberculosis Research and Treatment, 2014:1–5.

Li, Y., Zhu, L., Lu, W., Chen, C., and Yang, H. (2020). Seasonal variation in notified tuberculosis cases from 2014 to 2018 in eastern china. Journal of International Medical Research, 48(8):1–11.

Moosazadeh, M., Khanjani, N., Nasehi, M., and Bahrampour, A. (2015). Pre- dicting the incidence of smear positive tuberculosis cases in iran using time series analysis. Irani Journal of Public Health, 44(11):1526–1534.

R Core Team (2020). foreign: Read Data Stored by ’Minitab’, ’S’, ’SAS’, ’SPSS’, ’Stata’, ’Systat’, ’Weka’, ’dBase’, ... R package version 0.8-81.

R Core Team (2021a). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

R Core Team (2021b). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

RJ, H. and Y., K. (2008). Automatic time series forecasting: the forecast package for r. Journal of Statistical Software, 26(3):1–22.

Ryan, J. A. and Ulrich, J. M. (2020). quantmod: Quantitative Financial Modelling Framework. R package version 0.4.18.

SSA (2008). Estándares para la atención de la tuberculosis en México. último acceso 22 de febrero de 2022.

SSA (2014). Prevencio ́n y control de la tuberculosis 2013-2018. último acceso 22 de febrero de 2022.

Stoffer, D. (2021). astsa: Applied Statistical Time Series Analysis. R package version 1.13.

Trapletti, A. and Hornik, K. (2020). tseries: Time Series Analysis and Computational Finance. R package version 0.10-48.

Wang, H., Tian, C. W., Wang, W. M., and Luo, X. M. (2018). Time-series analysis of tuberculosis from 2005 to 2017 in china. Epidemiology and Infection, 146(8):935–939.

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., Francois, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Mu ̈ller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K., and Yuta- ni, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43):1686.

Willis, M. D., Winston, C. A., Heilig, C. M., Cain, K. P., Walter, N. D., and Mac Kenzie, W. R. (2012). Seasonality of tuberculosis in the united states, 1993-2008. Clinical Infectious Diseases, 54(11):1553–1560.

Published
2022-04-22
How to Cite
Vázquez-Chena, S. I., Tapia-Santos, B., & Ávila-Pozos, R. (2022). Time Series Analysis of Tuberculosis in the State of Veracruz from 2000 to 2018. Pädi Boletín Científico De Ciencias Básicas E Ingenierías Del ICBI, 10(Especial), 86-92. https://doi.org/10.29057/icbi.v10iEspecial.8425

Most read articles by the same author(s)

<< < 1 2