Modelos de Previsão para Avaliação de Casos de Malária na Amazônia Legal Brasileira
Abstract
Malaria is an infectious disease that affects thousands of people every year around the world. The region known as the Legal Brazilian Amazon, that is composed by nine states in the North, NOrthest and Central-West regions are the most affected in the national territory.This paper performed an analisys of atemporal series of case numbers of malaria occurd in this region, with the goal of identify and train statistical models, capable of forcast the incidence of the desease in the future (1 month), based on historical data. The data that was used were obtained from SIVEP-malaria (Epidemiological Survaillance Information System for malaria) which is responsible for collecting and disclosuring informations about malaria in Brazil and contains records of more than three milliond of notifications between the years of 2006 and 2019. The results of the Work-Foward validation method for ARIMA models have been shown good to predict cases in the reagion as well as a whole and also in each of the states sepaseparately, excep for the state of Tocantins that has a very low incidence of infecitons.
Keywords:
malaria, time-series, arima, legal amazon
References
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Alemu, A., Abebe, G., Tsegaye, W., and Golassa, L. (2011). Climatic variables and malaria transmission dynamics in jimma town, south west ethiopia. Parasites & vectors, 4(1):1-11.
Anokye, R., Acheampong, E., Owusu, I., and Isaac Obeng, E. (2018). Time series analysis of malaria in kumasi: Using arima models to forecast future incidence. Cogent social sciences, 4(1):1461544.
Beluzo, C. E., Arruda, N. M., de Souza Maia, V., and Alves, L. C. (2021). Data visualization for epidemiological and demographic data for malaria surveillance in the brazilian amazon, 2007-2019. medRxiv.
Carlos, B. C., Rona, L. D., Christophides, G. K., and Souza-Neto, J. A. (2019). A comprehensive analysis of malaria transmission in brazil. Pathogens and global health, 113(1):1-13.
Cryer, J. D. (1986). Time series analysis, volume 286. Springer. Ferrão, J. L., Earland, D., Novela, A., Mendes, R., Tungadza, A., and Searle, K. M. (2021). Malaria temporal variation and modelling using time-series in sussundenga district, mozambique. International journal of environmental research and public health, 18(11):5692.
Fisman, D. N. (2007). Seasonality of infectious diseases. Annu. Rev. Public Health, 28:127-143.
He, Z. and Tao, H. (2018). Epidemiology and arima model of positive-rate of influenza viruses among children in wuhan, china: A nine-year retrospective study. International Journal of Infectious Diseases, 74:61-70.
Jere, S. and Moyo, E. (2016). Modelling epidemiological data using box-jenkins procedure. Open Journal of Statistics, 6(2):295-302.
Lapouble, O. M. M., Santelli, A. C. F., Muniz-Junqueira, M. I., et al. (2015). Situação epidemiológica da malária na região amazônica brasileira, 2003 a 2012. Revista Panamericana de Salud Pública, 38:300-306.
Lima, M. V. M. d. and Laporta, G. Z. (2021). Avaliação de modelos de predição para ocorrência de malária no estado do amapá, 1997-2016: um estudo ecológico. Epidemiologia e Serviços de Saúde, 30:e2020080.
MS (2006). Ações de controle da malária: manual para profissionais de saúde na atenção básica. Ministério da Saúde Brasília.
Perone, G. (2020). An arima model to forecast the spread and the final size of covid-2019 epidemic in italy. MedRxiv.
Purwa, T., Nafngiyana, U., and Suhartono, S. (2014). Comparison of arima, transfer function and var models for forecasting cpi, stock prices, and indonesian exchange rate: Accuracy vs. explainability. Statistics, 2020.
Rojat, T., Puget, R., Filliat, D., Del Ser, J., Gelin, R., and Díaz-Rodríguez, N. (2021). Explainable artificial intelligence (xai) on timeseries data: A survey. arXiv preprint arXiv:2104.00950.
Sarpong, S. A. (2013). Modeling and forecasting maternal mortality; an application of arima models. International Journal of Applied, 3(1):19-28.
Tran, T. N., Phuc, D. T., et al. (2021). Grid search of multilayer perceptron based on the walk-forward validation methodology. International Journal of Electrical and Computer Engineering, 11(2):1742.
Wang, W.-C., Chau, K.-W., Cheng, C.-T., and Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of hydrology, 374(3-4):294-306.
WHO (1993). A global strategy for malaria control. World Health Organization.
WHO (2021). World malaria report 2021. World Health Organization.
Wolfarth-Couto, B., Filizola, N., and Durieux, L. (2020). Padrão sazonal dos casos de malária e a relação com a variabilidade hidrológica no estado do amazonas, brasil. Revista Brasileira de Epidemiologia, 23:e200018.
Wolfarth-Couto, B., Silva, R. A. d., and Filizola, N. (2019). Variabilidade dos casos de malária e sua relação com a precipitação e nível d'água dos rios no estado do amazonas, brasil. Cadernos de Saúde Pública, 35.
Published
2022-11-28
How to Cite
SANTOS, Natalia; SILVA, Everton; BELUZO, Carlos; ALVES, Luciana C..
Modelos de Previsão para Avaliação de Casos de Malária na Amazônia Legal Brasileira. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 19. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 670-681.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2022.227370.
