Predicting COVID-19 Trends with Comparative Analysis of ARIMA and ANN Models Predicting COVID-19 Trends Analysis of ARIMA and ANN Models
Article Sidebar
Main Article Content
Abstract
This study uses Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks to predict COVID-19 in Pakistan. The pandemic epidemic that hit Wuhan, China, in December 2019 and affected millions of people worldwide inspired this study. As of March 1, 2023, Pakistan had 296,149 confirmed cases, 6,298 deaths, and 280,970 recoveries. The predictive algorithms above will predict confirmed cases, deaths, and recoveries for 30 days. The approach collects time-series data on confirmed cases, Deaths, and recoveries. The data is processed and analyzed using ARIMA and ANN models. These models were chosen because they can handle nonlinear and complex time series data, making them excellent for pandemic prediction. The research hypothesis is that ARIMA and ANN models can accurately anticipate Pakistani COVID-19 case trends over the next 30 days. Correlation and MSE are used to compare models. Early results reveal that ARIMA and ANN models accurately estimate COVID-19 prevalence in Pakistan. An in-depth study of the methodology suggests adjustments to improve forecast accuracy. This study has significant ramifications. Accurate projections can help policymakers choose public health initiatives, saving lives and money. Successfully using these machine learning models could lead to their usage in epidemic prediction.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Pakistan Journal Emerging Science and Technologies (PJEST) in collaboration with Govt. Islamia Graduate College Civil Lines Lahore, Pakistan is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
H. Zhu, L. Wei, and P. Niu, “The novel coronavirus outbreak in Wuhan, China,” Global Health Research and Policy, vol. 5, no. 1, Mar. 2020, doi: 10.1186/s41256-020-00135-6.
Y.-C. Wu, C.-S. Chen, and Y.-J. Chan, "The outbreak of COVID-19: An overview," Journal of the Chinese Medical Association, vol. 83, no. 3, pp. 217–220, Mar. 2020, doi: 10.1097/jcma.0000000000000270.
T. Singhal, “A Review of Coronavirus Disease-2019 (COVID-19),” The Indian Journal of Pediatrics, vol. 87, no. 4, pp. 281–286, Mar. 2020, doi: 10.1007/s12098-020-03263-6.
"WHO Coronavirus Disease (COVID-19) Dashboard," Bangladesh Physiotherapy Journal, vol. 10, no. 1, Nov. 2020, doi: 10.46945/bpj.10.1.03.01.
S. Ahmad, D. E. Lucero-Prisno, M. Y. Essar, H. Khan, and A. Ahmadi, "Pakistan and COVID-19: The mystery of the flattened curve," Journal of Global Health, vol. 11, Jan. 2021, doi: 10.7189/jogh.11.03013.
S. Kotcharin, S. Maneenop, and A. Jaroenjitrkam, "The impact of government policy responses on airline stock return during the COVID-19 crisis," Research in Transportation Economics, vol. 99, p. 101298, Jun. 2023, doi: 10.1016/j.retrec.2023.101298.
R. Rasheed, A. Rizwan, H. Javed, F. Sharif, and A. Zaidi, "Socio-economic and environmental impacts of COVID-19 pandemic in Pakistan—an integrated analysis," Environmental Science and Pollution Research, January 6, 2021. https://doi.org/10.1007/s11356-020-12070-7
H. Baalbaki et al., "Fighting against COVID-19: Who Failed and Who Succeeded?," Journal of Computer and Communications, vol. 10, no. 04, pp. 32–50, 2022, doi: 10.4236/jcc.2022.104004.
H. Akhtar et al., "Pakistan's Response to COVID-19: Overcoming National and International Hypes to Fight the Pandemic," JMIR public health and surveillance, May 19, 2021. https://doi.org/10.2196/28517
O. Delardas, K. S. Kechagias, P. N. Pontikos, and P. Giannos, “Socio-Economic Impacts and Challenges of the Coronavirus Pandemic (COVID-19): An Updated Review,” Sustainability, Aug. 06, 2022. https://doi.org/10.3390/su14159699
S. Kotcharin, S. Maneenop, and A. Jaroenjitrkam, "The impact of government policy responses on airline stock return during the COVID-19 crisis," Research in Transportation Economics, vol. 99, p. 101298, Jun. 2023, doi: 10.1016/j.retrec.2023.101298.
H. Jawed, "Pandemic Coronavirus COVID-19 Spread in Pakistan in 2020," Journal of Respiratory Research, vol. 6, no. 1, pp. 148–151, 2020, doi: 10.17554/j.issn.2412-2424.2019.06.50.
N. Ilyas, R. E. Azuine, and A. Tamiz, “COVID-19 Pandemic in Pakistan,” International Journal of Translational Medical Research and Public Health, vol. 4, no. 1, pp. 37–49, May 2020, doi: 10.21106/ijtmrph.139.
H. Jawed, "Pandemic Coronavirus COVID-19 Spread in Pakistan in 2020," Journal of Respiratory Research, vol. 6, no. 1, pp. 148–151, 2020, doi: 10.17554/j.issn.2412-2424.2019.06.50.
F. U. Khan and R. Gupta, "ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India," Journal of safety science and resilience, September 1, 2020. https://doi.org/10.1016/j.jnlssr.2020.06.007
S. Ahmed, S. Khan, and I. Hussain, "Impact of Government Interventions on COVID-19 Outbreak in Different Provinces of Pakistan: Interrupted Time-Series Analysis," SSRN Electronic Journal, 2020, Published, doi: 10.2139/ssrn.3688215.
R. G. M. Al-Allaff, S. M. Y. Al-Taee, and S. T. D. Baker, "Some Immunological Impacts of Face Mask Usage During the COVID-19 Pandemic," Pakistan Journal of Biological Sciences, August 15, 2021. https://doi.org/10.3923/pjbs.2021.920.927
"Forecasting COVID-19 Transmission in India Using Deep Learning Models," Letters in Applied NanoBioScience, October 17, 2020. https://doi.org/10.33263/lianbs102.20442055
O. H. Al-Hussain, “Complications and Comorbidities in COVID-19 Patients: A Comparative study,” Cureus, Aug. 2022, Published, doi: 10.7759/cureus.28614.
V. Chaurasia and S. Pal, "COVID-19 Pandemic: ARIMA and Regression Model based Worldwide Death Cases Predictions," SSRN Electronic Journal, 2020, Published, doi: 10.2139/ssrn.3663935.
M. Haidari, "Prediction of COVID-19 Cases in Afghanistan Using ARIMA Model," SSRN Electronic Journal, 2021, Published, doi: 10.2139/ssrn.3889679.
V. Chaurasia and S. Pal, "COVID-19 Pandemic: ARIMA and Regression Model based Worldwide Death Cases Predictions," SSRN Electronic Journal, 2020, Published, doi: 10.2139/ssrn.3663935.
S. Arshad, J. H. Kazmi, M. G. Javed, and S. Mohammed, "Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia," European Journal of Agronomy, vol. 147, p. 126837, Jul. 2023, doi: 10.1016/j.eja.2023.126837.
S. G. Paul et al., "Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives," Array, vol. 17, p. 100271, Mar. 2023, doi: 10.1016/j.array.2022.100271.
A. Lahiri and S. Jha, "Machine learning to deep learning: Artificially intelligent approaches toward precision in public health," Journal of Public Health and Primary Care, vol. 2, no. 2, p. 25, 2021, doi: 10.4103/jphpc.jphpc_6_20.
I. Khandelwal, R. Adhikari, and G. Verma, "Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition," Procedia Computer Science, January 1, 2015. https://doi.org/10.1016/j.procs.2015.04.167
C. Siva, M. KG, G. Nalinipriya, and P. M. J, "Dynamic Analytics and Forecasting Model for Covid-19 Using Machine Learning Algorithms," Webology, October 29, 2021. https://doi.org/10.14704/web/v18si05/web18302
S. M. I. Osman and A. Sabit, "Predictors of COVID-19 vaccination rate in USA: A machine learning approach," Machine Learning with Applications, vol. 10, p. 100408, Dec. 2022, doi: 10.1016/j.mlwa.2022.100408.
M. Johnson, "Wuhan 2019 Novel Coronavirus - 2019-nCoV," Materials and Methods, vol. 10, Jan. 2020, doi: 10.13070/mm.en.10.2867.
M. Shakhnazarova, "True death toll of COVID-19 in Georgia: estimates of the number of deaths from COVID-19," European Journal of Public Health, vol. 32, no. Supplement_3, Oct. 2022, doi: 10.1093/eurpub/ckac131.062.
S. Kotcharin, S. Maneenop, and A. Jaroenjitrkam, "The impact of government policy responses on airline stock return during the COVID-19 crisis," Research in Transportation Economics, vol. 99, p. 101298, Jun. 2023, doi: 10.1016/j.retrec.2023.101298.
D. Malouche, "www.Covidradar24.org, a Real-Time COVID-19 Tracker," SSRN Electronic Journal, 2021, Published, doi: 10.2139/ssrn.3900615.
"HPRA safety update on COVID-19 vaccines up to January 2023," Reactions Weekly, vol. 1946, no. 1, pp. 2–2, Mar. 2023, doi: 10.1007/s40278-023-34603-2.
M. Miller, "2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository," Bulletin - Association of Canadian Map Libraries and Archives (ACMLA), no. 164, pp. 47–51, Mar. 2020, doi: 10.15353/acmla.n164.1730.
"Some recent advances in time series modeling," IEEE Journals & Magazine | IEEE Xplore, December 1, 1974. https://ieeexplore.ieee.org/document/1100733
"Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis Forecasting and Control. Revised Edition, Holden Day, San Francisco. - References - Scientific Research Publishing." https://www.scirp.org/(S(czeh2tfqyw2orz553k1w0r45))/reference/ReferencesPapers.aspx?ReferenceID=1969833
G. T. Wilson, "Time Series Analysis: Forecasting and Control, 5th Edition, by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel and Greta M. Ljung, 2015. Published by John Wiley and Sons Inc., Hoboken, New Jersey, pp. 712. ISBN: 978-1-118-67502-1," Journal of Time Series Analysis, March 27, 2016. https://doi.org/10.1111/jtsa.12194
R. J. Hyndman and Y. Khandakar, “Automatic Time Series Forecasting: TheforecastPackage forR,” Journal of Statistical Software, vol. 27, no. 3, 2008, doi: 10.18637/jss.v027.i03.
K. Mehmood et al., "The impact of COVID-19 pandemic on air pollution: a global research framework, challenges, and future perspectives," Environmental Science and Pollution Research, March 9, 2022. https://doi.org/10.1007/s11356-022-19484-5
"An Improved Learning Algorithm Based on The Broyden-Fletcher-Goldfarb-Shanno (BFGS) Method For Back Propagation Neural Networks," IEEE Conference Publication | IEEE Xplore, October 1, 2006. https://ieeexplore.ieee.org/document/4021427
M. Yaghini, M. M. Khoshraftar, and M. Fallahi, "A hybrid algorithm for artificial neural network training," Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 293–301, Jan. 2013, doi: 10.1016/j.engappai.2012.01.023.