Nonlinear Time Series Analysis of Pathogenesis of COVID-19 Pandemic Spread in Saudi Arabia
Source abstract
Abstract
This article discusses short–term forecasting of the novel Corona Virus (COVID-19) data for infected and recovered cases using the ARIMA method for Saudi Arabia. The COVID-19 data was obtained from the Worldometer and MOH (Ministry of Health, Saudi Arabia). The data was analyzed for the period from March 2, 2020 (the first case reported) to June 15, 2020. Using ARIMA (2, 1, 0), we obtained the short forecast up to July 02, 2020. Several statistical parameters were tested for the goodness of fit to evaluate the forecasting methods. The results show that ARIMA (2, 1, 0) gave a better forecast for the data system. COVID 19 data followed quadratic behavior, and in the long run, it spreads with a high peak. It is concluded that COVID-19 will follow secondary shock waves, and it is strongly advisable to maintain social distancing with all safety measures as the pandemic situation is not in control.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · SARS-CoV-2 and COVID-19 Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (30)
Show source-provided references
1. World Health Organization, “Coronavirus,” https://www.who.int/health-topics/coronavirus. 2020.
2. Epidemiology of coronavirus disease in Gansu province10.3201/eid2606.200251 ↗
3. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak10.1016/j.jaut.2020.102433 ↗
4. Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: A scoping review10.1186/s40249-020-00646-x ↗
5. Cluster synchronization of two-layer nonlinearly coupled multiplex networks with multi-links and time-delays10.1016/j.neucom.2019.05.077 ↗
6. A robust and fixed-time zeroing neural dynamics for computing time-variant nonlinear equation using a novel nonlinear activation function10.1016/j.neucom.2019.03.053 ↗
7. Neural network-based finite-time control of quantized stochastic nonlinear systems10.1016/j.neucom.2019.06.060 ↗
8. Towards an artificial intelligence framework for data-driven prediction of Coronavirus clinical severity10.32604/cmc.2020.010691 ↗
9. World Health Organization, “Coronavirus disease 2019 (COVID-19) situation report-59,” March 19, 2020.
10. World Health Organization, “Coronavirus disease (COVID-19) pandemic,” 2019. [Online]. Available: https://www.who.int/emergencies/diseases/novel–coronavirus–2019.
11. A novel coronavirus from patients with pneumonia in China, 201910.1056/NEJMoa2001017 ↗
12. COVID-19 disease due to SARS-CoV–2 (Novel Coronavirus)10.1164/rccm.2014P7 ↗
13. Receptor recognition by the novel coronavirus from Wuhan: An analysis based on decade-long structural studies of SARS coronavirus10.1128/JVI.00127-20 ↗
14. Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: A retrospective review of medical records10.1016/S0140-6736(20)30360-3 ↗
15. UpToDate, 2020. [Online]. Available: https://www.uptodate.com/contents/coronavirus-disease–2019-covid–19#H2513094760.
16. Centers for Disease Control and Prevention, “Interim guidelines for collecting, handling, and testing clinical specimens from persons under investigation (PUIs) for Coronavirus Disease 2019 (COVID–19),” 2020. [Online]. Available: https://www.cdc.gov/coronavirus/2019–nCoV/lab/guidelines–clinical–specimens.html.
17. World Health Organization, “Responding to community spread of COVID-19 interim guidance,” 2020. [Online]. Available: https://www.who.int/docs/default-source/coronaviruse/20200307-responding--to-covid--19-communitytransmission–final.pdf.
18. R. Bhardwaj and D. Datta, “Consensus algorithm,” “Decentralised Internet of Things: A blockchain perspective.” M. A. Khan, M. T. Quasim, F. Algarni and A. Alharthi (eds.), vol. 71, Springer, pp. 91–107, 2020.10.1007/978-3-030-38677-1_5 ↗
19. Bangia · Advances in Intelligent Systems and Computing. Numerical Optimization in Engineering and Sciences · 2020 · 979 · 107
20. Bhardwaj · Soft Computing for Problem Solving · 2019 · 85310.1007/978-981-13-1592-3_68 ↗
21. Bhardwaj · Mathematical Modeling on Real World Problems: Interdisciplinary Studies in Applied Mathematics · 2019 · 71
22. Bhardwaj · Mathematical Models, Methods and Applications, Industrial and Applied Mathematics · 2016 · 173
23. Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures10.1007/s11069-014-1136-1 ↗
24. Improving precipitation forecasts skill over India using a multi-model ensemble technique
25. Analysis and very short range forecast of cyclone AILA with radar data assimilation with rapid intermittent cycle using ARPS 3DVAR and cloud analysis techniques10.1007/s00703-014-0307-7 ↗
26. Assimilation of Doppler weather radar data in WRF model for simulation of Tropical Cyclone Aila10.1007/s00024-013-0723-5 ↗
27. Water quality index and fractal dimension analysis of water parameters10.1007/s13762-012-0086-y ↗
28. Evolutionary techniques for optimizing air quality model10.1016/j.procs.2020.03.206 ↗
29. Development of model for sustainable nitrogen dioxide prediction using neuronal networks10.1007/s13762-019-02620-z ↗
30. Variability analysis in pm 2.5 monitoring