Abstract
Respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pandemic coronavirus that is spreading quickly throughout the world. Every person experiences fear due to the unexpected pandemic (Covid-19), which is spreading quickly and affecting people. The affected patients' daily growth rate is accelerating. A predictive analysis was done to determine the potential number of deaths brought on by this pandemic. An official dataset of 35 states/UTs of India was investigated with predictive analysis (regression modeling). To predict the patient's deceased, both recovered and active cases have been impacted. This paper estimated future deceased counts based on active and recovered cases individually and jointly. The high positive linear correlation proved that the active and recovered case affected the patient's deceased rate. Regression models explored high aspects of the deceased ahead. Multiple linear regression has predicted a deceased patient based on active and recovered patients with significant R2 = 0.89. Further, temporal dynamics of Covid-19 timing analyzed with Auto-Regressive Integrated Moving Average forecasted confirmed, active, recovered, and deceased cases for the next 40 days. According to the results, a high cure is still required for active and recovered patients, and the government should follow in obligatory footsteps to avoid more deceased predicted with the models.
Similar content being viewed by others
References
Almendros Jimenez JM, Becerra Teron A, Torres M (2021) The retrieval of social network data for points of interest in openstreetmap. Human Centr Comput Inform Sci. https://doi.org/10.22967/HCIS.2021.11.010
Boudry L, Essahib W, Mateizel I, Van de Velde H, De Geyter D, Piérard D, De Brucker M (2022) Undetectable viral RNA in follicular fluid, cumulus cells, and endometrial tissue samples in SARS-CoV-2–positive women. Fertil Steril 117(4):771–780. https://doi.org/10.1016/j.fertnstert.2021.12.032
George EP, Box GM, Jenkins GC, Reinsel Ljung GM (2015) Time series analysis: Forecasting and Control - George E. P. Box, Gwilym M. Jenkins.” Book.
Bungaro M, Passiglia F, Scagliotti GV (2022) COVID-19 and lung cancer: a comprehensive overview from outbreak to recovery. Biomedicines. https://doi.org/10.3390/biomedicines10040776
Chintalapudi N, Battineni G, Amenta F (2020) COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: a data driven model approach. J Microbiol Immunol Infect Wei Mian Yu Gan Ran Za Zhi 53(3):396–403. https://doi.org/10.1016/J.JMII.2020.04.004
Chitra N, Shanmathi R, Rajesh R (2015) Application of arima model using spss software-a case study in supply chain management. Case Study
Gao Y, Zhang Z, Yao W, Ying Qi, Long C, Xinmiao Fu (2020) Forecasting the cumulative number of COVID-19 deaths in China: a boltzmann function-based modeling study. Infect Control Hosp Epidemiol 41(7):1. https://doi.org/10.1017/ICE.2020.101
Garcia-Flores V, Romero R, Xu Y, Theis KR, Arenas-Hernandez M, Miller D, Gomez-Lopez N (2022) Maternal-fetal immune responses in pregnant women infected with SARS-CoV-2. Nat Commun. https://doi.org/10.1038/s41467-021-27745-z
Hao F, Park DS (2021) CoNavigator: a framework of FCA-based novel coronavirus COVID-19 domain knowledge navigation https://doi.org/10.22967/HCIS.2021.11.006
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Cao B (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan China. The Lancet 395(10223):497–506
Izquierdo-Pujol J, Moron-Lopez S, Dalmau J, Gonzalez-Aumatell A, Carreras-Abad C, Mendez M, Martinez-Picado J (2022) Post COVID-19 condition in children and adolescents: an emerging problem. Front Pediatr. https://doi.org/10.3389/fped.2022.894204
Kotlyar AM, Grechukhina O, Chen A et al (2021) Vertical transmission of coronavirus disease 2019: a systematic review and meta-analysis. Am J Obstet Gynecol 224:35–53
Kumar S, Viral R, Deep V, Sharma P, Kumar M, Mahmud M, Stephan T (2023) Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap. Pers Ubiquit Comput 27(3):807–830. https://doi.org/10.1007/s00779-021-01530-7
Li L, Yang Z, Dang Z, Meng C, Huang J, Meng H, Wang D, Chen G, Zhang J, Peng H, Shao Y (2020) Propagation analysis and prediction of the COVID-19. Infect Dis Model 5:282–292. https://doi.org/10.1016/J.IDM.2020.03.002
Male V (2022) SARS-CoV-2 infection and COVID-19 vaccination in pregnancy. Nat Rev Immunol 22(5):277–282. https://doi.org/10.1038/s41577-022-00703-6
Mohler RR (1990) Nonlinear time series and control applications. In: Proceedings of the IEEE conference on decision and control 2917–19
Rishabh S, Johri A, Deep V, Sharma P (2019) Heart diseases prediction system using CHC-TSS evolutionary, KNN, and decision tree classification algorithm. Adv Intell Syst Comput 813:809–819. https://doi.org/10.1007/978-981-13-1498-8_71
Rizzo G, Mappa I, Pietrolucci ME, Lu JLA, Makatsarya A, D’Antonio F (2022) Effect of SARS-CoV-2 infection on fetal umbilical vein flow and cardiac function: a prospective study. J Perinat Med 50(4):398–403. https://doi.org/10.1515/jpm-2021-0657
Roosa K, Lee Y, Luo R, Kirpich A, Rothenberg R, Hyman JM, Yan P, Chowell G (2020) Real-time forecasts of the COVID-19 epidemic in China from february 5th to february 24th, 2020. Infect Dis Model 5:256–263. https://doi.org/10.1016/J.IDM.2020.02.002
Rujen J, Sharma P, Keshri R, Sharma P (2023) COVID detection using cough soundhttps://doi.org/10.1007/978-981-19-7346-8_69
Sharma P, Saxena K, Sharma R (2016) Heart disease prediction system evaluation using C4.5 rules and partial tree. Adv Intell Syst Comput. https://doi.org/10.1007/978-81-322-2731-1_26
Sharma P, Alshehri M, Sharma R, Alfarraj O (2021) Self-management of low back pain using neural network. Comput Mater Continua 66(1):885–901. https://doi.org/10.32604/CMC.2020.012251
Sharma P, Alshehri M, Sharma R (2022a) Activities tracking by smartphone and smartwatch biometric sensors using fuzzy set theory. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-13290-4
Sharma P, Sharma P, Shukla VK (2022b) Covid-19 detection using cough sound with neural networks in 10th international conference on reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022b, https://doi.org/10.1109/ICRITO56286.2022.9965099.
Shivam B, Alowaidi M, Bhardwaj R, Sharma SK (2021) Machine learned hybrid Gaussian analysis of COVID-19 pandemic in India. Results Phys 30:104630. https://doi.org/10.1016/J.RINP.2021.104630
Sophia S, Vanessa K, Tobias P et al (2022) Effects of SARS-CoV-2 on prenatal lung growth assessed by fetal MRI. Lancet Respir Med. https://doi.org/10.1016/S2213-2600(22)00060-1
Sun H, Koch M (2001) Case study: analysis and forecasting of salinity in Apalachicola bay, Florida, using box-Jenkins ARIMA models. J Hydraulic Eng 127(9):718–727
Tatura SNN (2022) Case report: Severe COVID-19 with late-onset sepsis-like illness in a neonate. Am J Trop Med Hyg 106(4):1098–1103. https://doi.org/10.4269/ajtmh.21-0743
Tomar A, Gupta N (2020) Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Sci Total Environ 728:138762. https://doi.org/10.1016/J.SCITOTENV.2020.138762
Tran TT, Pham LT, Ngo QX (2020) Forecasting epidemic spread of SARS-CoV-2 using ARIMA model (case study: Iran). Glob J Environ Sci Manag 6(Special Issue (Covid-19)) https://doi.org/10.22034/GJESM.2019.06.SI.01
Wang L, Li J, Guo S, Xie N, Yao L, Cao Y, Day SW, Howard SC, Carolyn Graff J, Tianshu G, Ji J, Weikuan G, Sun D (2020) Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm. Sci Total Environ 727:138394. https://doi.org/10.1016/J.SCITOTENV.2020.138394
Yang Y, Hao F, Park D-S, Peng S, Lee H, Mao M (2021) Modelling prevention and control strategies for COVID-19 propagation with patient contact networks https://doi.org/10.22967/HCIS.2021.11.045
Zhang Z, Jing J, Wang X, Choo KKR, Gupta BB (2020) A crowdsourcing method for online social networks security assessment based on human-centric computing. Human-Centr Comput Inform Sci. https://doi.org/10.1186/s13673-020-00230-0
Funding
The authors did not receive support from any organization for the submitted work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest to report regarding the present study.
Informed consent
The studies are conducted on already available data for which consent not required.
Human or animal participants
This is an observational study. This research includes No involvement of Human and Animals, so no ethical approval is required.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Verma, C., Sharma, P., Singla, S. et al. SARS-CoV-2 forecasting using regression and ARIMA. Int J Syst Assur Eng Manag 14, 2626–2641 (2023). https://doi.org/10.1007/s13198-023-02127-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-023-02127-4