Predicción de infectados por Covid-19 en el Perú por el modelo de media móvil integrada autorregresiva

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Published: Aug 1, 2022
DOI: https://doi.org/10.33326/26176033.2022.1.1237
Keywords:
Infectados, Muertes, Modelo ARIMA, Predicción

Main Article Content

Alex Youn Aro Huanacuni
Universidad Nacional del Altiplano Puno
https://orcid.org/0000-0002-8295-1816

Abstract

During the outbreak of the Covid-19 virus, several researchers have studied various mathematical models for predicting infections and deaths, as well as the rate of virus transmission. At present, the virus is still active with some variants and it is very important to know its behavior in order to develop effective actions to control the current and future situation. In the research, we obtained predictions of cumulative Covid-19 infections for 38 days from December 23, 2021, using data recorded in the World Health Organization (WHO) for Peru and training the autoregressive integrated moving average (ARIMA) model in the software Python 3. The most optimal models obtained with real data test and according to EMPA and R2 are ARIMA(3,0,1) in the prediction of infected with EMPA=0.178 and R2=0.804 and ARIMA(3,1,1), with EMPA= 0.243 and R2=0.579, for prediction of deaths.

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How to Cite
Aro Huanacuni, A. Y. (2022). Predicción de infectados por Covid-19 en el Perú por el modelo de media móvil integrada autorregresiva. Science and Development, 21(1), 1–9. https://doi.org/10.33326/26176033.2022.1.1237
Issue
Vol. 21 No. 1 (2022): Ciencia y Desarrollo
Section
Artículo original

References

Adhikari, S. P., Meng, S., Wu, Y., Mao, Y., Ye, R., Wang, Q., Sun, C., Sylvia, S., Rozelle, S., Raat, H., & Zhou, H. (2020). A scoping review of 2019 Novel Coronavirus during the early outbreak period: Epidemiology, causes, clinical manifestation and diagnosis, prevention and control. Infectious Diseases of Poverty, 1–12. https://doi.org/10.21203/rs.2.24474/v1

Agrawal, U., Katikireddi, S. V., McCowan, C., Mulholland, R. H., Azcoaga-Lorenzo, A., Amele, S., Fagbamigbe, A. F., Vasileiou, E., Grange, Z., Shi, T., Kerr, S., Moore, E., Murray, J. L. K., Shah, S. A., Ritchie, L., O'Reilly, D., Stock, S. J., Beggs, J., Chuter, A., … Sheikh, A. (2021). COVID-19 hospital admissions and deaths after BNT162b2 and ChAdOx1 nCoV-19 vaccinations in 2·57 million people in Scotland (EAVE II): a prospective cohort study. The Lancet Respiratory Medicine, 9(12), 1439–1449. https://doi.org/10.1016/s2213-2600(21)00380-5

Awan, T. M., & Aslam, F. (2020). Prediction of daily COVID-19 cases in European countries using automatic ARIMA model. Journal of Public Health Research, 9(3), 227–233. https://doi.org/10.4081/jphr.2020.1765

Ayele, A. W., Zewdie, M. A., & Bayko, T. (2020). Modeling and Forecasting the Global Daily Incidence of Novel Coronavirus Disease ( COVID-19 ): An Application of Autoregressive Moving Average ( ARMA ) Model. International Journal of Public Health and Safety, 5(April).

Barandalla, I., Alvarez, C., Barreiro, P., de Mendoza, C., González-Crespo, R., & Soriano, V. (2021). Impact of scaling up SARS-CoV-2 vaccination on COVID-19 hospitalizations in Spain. International Journal of Infectious Diseases, 112, 81–88. https://doi.org/10.1016/j.ijid.2021.09.022

Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association, 70(349), 70–79. https://doi.org/10.2307/2285379

Box, George E. P., & Jenkins, G. M. (1078). Time Series Analysis forecasting and Control.

Box, George E. P., Jenkins, G. M., & Reinsel, G. C. (2008). Time series analysis. In Water Resources Research. https://doi.org/10.1029/WR003i003p00817

Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of the Total Environment, 729, 138817. https://doi.org/10.1016/j.scitotenv.2020.138817

Chathappady House, N. N., Palissery, S., & Sebastian, H. (2021). Corona Viruses: A Review on SARS, MERS and COVID-19. Microbiology Insights, 14, 1–8. https://doi.org/10.1177/11786361211002481

Conlon, A., Ashur, C., Washer, L., Eagle, K. A., & Hofmann Bowman, M. A. (2021). Impact of the influenza vaccine on COVID-19 infection rates and severity. American Journal of Infection Control, 49(6), 694–700. https://doi.org/10.1016/j.ajic.2021.02.012

Cromer, D., Steain, M., Reynaldi, A., Schlub, T. E., Wheatley, A. K., Juno, J. A., Kent, S. J., Triccas, J. A., Khoury, D. S., & Davenport, M. P. (2021). Neutralising antibody titres as predictors of protection against SARS-CoV-2 variants and the impact of boosting: a meta-analysis. The Lancet Microbe, 5247(21), 1–10. https://doi.org/10.1016/s2666-5247(21)00267-6

Hamid, S., Mir, M. Y., & Rohela, G. K. (2020). Novel coronavirus disease (COVID-19): a pandemic (epidemiology, pathogenesis and potential therapeutics). New Microbes and New Infections, 35, 100679. https://doi.org/10.1016/j.nmni.2020.100679

He, Z., & 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. https://doi.org/10.1016/j.ijid.2018.07.003

Hernandez-Matamoros, A., Fujita, H., Hayashi, T., & Perez-Meana, H. (2020). Forecasting of COVID19 per regions using ARIMA models and polynomial functions. Applied Soft Computing Journal, 96, 106610. https://doi.org/10.1016/j.asoc.2020.106610

Ilie, O. D., Cojocariu, R. O., Ciobica, A., Timofte, S. I., Mavroudis, I., & Doroftei, B. (2020). Forecasting the spreading of COVID-19 across nine countries from Europe, Asia, and the American continents using the arima models. Microorganisms, 8(8), 1–19. https://doi.org/10.3390/microorganisms8081158

Katoch, R., & Sidhu, A. (2021). An Application of ARIMA Model to Forecast the Dynamics of COVID-19 Epidemic in India. Global Business Review, 1–14. https://doi.org/10.1177/0972150920988653

Malki, Z., Atlam, E. S., Ewis, A., Dagnew, G., Alzighaibi, A. R., Elmarhomy, G., Elhosseini, M. A., Hassanien, A. E., & Gad, I. (2021). ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound. Neural Computing and Applications, 33(7), 2929–2948. https://doi.org/10.1007/s00521-020-05434-0

Raman, R., Patel, K. J., & Ranjan, K. (2021). Covid-19: Unmasking emerging sars-cov-2 variants, vaccines and therapeutic strategies. Biomolecules, 11(7). https://doi.org/10.3390/biom11070993

Sabry, I., Mourad, A. H. I., Idrisi, A. H., & Elwakil, M. (2021). Forecasting COVID-19 cases in Egypt using ARIMA-based time-series analysis. Eurasian Journal of Medicine and Oncology, 5(2), 123–131. https://doi.org/10.14744/ejmo.2021.64251

Shereen, M. A., Khan, S., Kazmi, A., Bashir, N., & Siddique, R. (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. Journal of Advanced Research, 24, 91–98. https://doi.org/10.1016/j.jare.2020.03.005

Wang, G., Wu, T., Wei, W., Jiang, J., An, S., Liang, B., Ye, L., & Liang, H. (2021). Comparison of ARIMA, ES, GRNN and ARIMA-GRNN hybrid models to forecast the second wave of COVID-19 in India and the United States. Epidemiology and Infection, 149, 1–9. https://doi.org/10.1017/S0950268821002375

Xu, Y., Cheng, M., Chen, X., & Zhu, J. (2020). Current approaches in laboratory testing for SARS-CoV-2. International Journal of Infectious Diseases, 100, 7–9. https://doi.org/10.1016/j.ijid.2020.08.041

Yang, Q., Wang, J., Ma, H., & Wang, X. (2020). Research on COVID-19 based on ARIMA modelΔ—Taking Hubei, China as an example to see the epidemic in Italy. Journal of Infection and Public Health, 13(10), 1415–1418. https://doi.org/10.1016/j.jiph.2020.06.019