Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Exploring Covid-19 Progression Patterns


Affiliations
1 Ph.D, Damascus,, Syrian Arab Republic
2 Professor of Biochemistry, Damascus University,, Syrian Arab Republic
3 Department of Toxicology and Pharmacology, Master Degree, Damascus University, Damascus,, Syrian Arab Republic
     

   Subscribe/Renew Journal


Background: A novel coronavirus COVID-19 causing acute illness with severe symptoms, represents the causative agent of a contagious potentially lethal disease. COVID-19 was declared as pandemic by WHO. Aims: This Research aims to study the COVID-19 outbreaks in the fifteen most impacted countries in the world, find the relationship between the precautionary measures of governments and COVID-19 confirmed cases and deaths, and to forecast the pandemic in the following short time. Methods: The global numbers of confirmed cases and deaths of COVID-19 were obtained from the European Union Data. The data of governmentsʹ response actions for COVID-19 were estimated using the Oxford study. Box-Jenkins methodology, ARIMA model, R package were used in data analysis. Results: The rate of COVID-19 confirmed cases is 0.4 per thousand, and the death case rate is 0.03 per thousand of the world population. The rate of death cases was the lowest in Brazil, and the highest in Spain. The usefulness of precautionary measures and its effect on the number of confirmed cases and deaths in the different countries were estimated. A high correlation was established concerning the applied measurements and time of application. The model used for forecasting the expected cases was consistent with our tested result, while the model for forecasting death showed a fair consistently. Conclusion: We conclude that the health system must be reviewed, and these precautionary measures evaluated whether they are beneficial or more stringent conditions should be imposed.

Keywords

COVID-19, Patterns, ARIMA Models, Measures, Forecast, Countries.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Munster, V.J., et al., A novel coronavirus emerging in China-key questions for impact assessment. New England Journal of Medicine, 2020; 382(8): p. 692-694.
  • Bogoch, I.I., et al., Pneumonia of Unknown Etiology in Wuhan, China: Potential for International Spread Via Commercial Air Travel. Journal of Travel Medicine, 2020.
  • Lu, H., C.W. Stratton, and Y.W. Tang, Outbreak of Pneumonia of Unknown Etiology in Wuhan China: the Mystery and the Miracle. Journal of Medical Virology.
  • Wu, Z. and J.M. McGoogan, Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA, 2020; 323(13): p. 1239-1242.
  • Huang, C., et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet, 2020; 395(10223): p. 497-506.
  • Lekhraj Rampal. Neurosurgery, Coronavirus disease (COVID-19) pandemic. Editorial. Med J Malaysia Vol 75 No 2 March 2020.
  • Lu, R., et al., Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet, 2020; 395(10224): p. 565-574.
  • Zhu, N., et al., A novel coronavirus from patients with pneumonia in China, 2019. New England Journal of Medicine, 2020.
  • Menachery, V.D., et al., A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence. Nature Medicine, 2015; 21(12): p. 1508.
  • Menachery, V.D., et al., SARS-like WIV1-CoV poised for human emergence. Proceedings of the National Academy of Sciences, 2016; 113(11): p. 3048-3053.
  • Wang, N., et al., Serological evidence of bat SARS-related coronavirus infection in humans, China. Virologica Sinica, 2018; 33(1): p. 104-107.
  • Cascella, M., et al., Features, evaluation and treatment coronavirus (COVID-19), in Statpearls [internet]. 2020, StatPearls Publishing.
  • Chang, L., Y. Yan, and L. Wang, Coronavirus disease 2019: coronaviruses and blood safety. Transfusion Medicine Reviews, 2020.
  • Onder, G., G. Rezza, and S. Brusaferro, Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. Jama, 2020.
  • COVID, C. and R. Team, Severe outcomes among patients with coronavirus disease 2019 (COVID-19)-United States, February 12–March 16, 2020. MMWR Morb Mortal Wkly Rep, 2020; 69(12): p. 343-346.
  • Garg, S., Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019-COVID-NET, 14 States, March 1-30, 2020. MMWR. Morbidity and Mortality Weekly Report, 2020; 69.
  • Guan, W.-j., et al., Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine, 2020; 382(18): p. 1708-1720.
  • McCullough, P.A., et al., Urgent need for individual mobile phone and institutional reporting of at home, hospitalized, and intensive care unit cases of SARS-CoV-2 (COVID-19) infection. Reviews in Cardiovascular Medicine, 2020; 21(1): p. 1-7.
  • Organization, W.H., Responding to community spread of COVID-19: interim guidance, 7 March 2020, 2020, World Health Organization.
  • Hale, T., et al., Variation in government responses to COVID-19. Blavatnik School of Government Working Paper, 2020; 31.
  • Portal, E.U.O.D., Access to European Union open data. (http:// open-data.europa.eu).
  • Box, G.E., et al., Time series analysis: forecasting and control. 2015: John Wiley and Sons.
  • Nanda, S., Forecasting: Does the Box-Jenkins Method Work Better than Regression? Vikalpa, 1988; 13(1): p. 53-62.
  • Hyndman, R.J., et al., Package ‘forecast’. Online] https://cran. r-project. org/web/packages/forecast/forecast. pdf, 2020.
  • Hyndman, R.J. and Y. Khandakar, Automatic time series for forecasting: the forecast package for R. 2007: Monash University, Department of Econometrics and Business Statistics ….
  • Hyndman, R.J. and G. Athanasopoulos, Forecasting: principles and practice. 2018: OTexts.
  • Organization, W.H., COVID 19 Public Health Emergency of International Concern (PHEIC). Global research and innovation forum: towards a research roadmap. 2020.
  • Siegel, R.L., K.D. Miller, and A. Jemal, Cancer statistics, 2020. CA: A Cancer Journal for Clinicians, 2020; 70(1): p. 7-30. 29. Goals, T.U.N.S.D., Our World in Data. https://ourworldindata.org/ coronavirus-data
  • Yichi Li1, B.W., Ruiyang Peng3,, Mathematical Modeling and Epidemic Prediction Of 2019-Ncov. EC Emergency Medicine and Critical Care, 2020.

Abstract Views: 90

PDF Views: 0




  • Exploring Covid-19 Progression Patterns

Abstract Views: 90  |  PDF Views: 0

Authors

Zakarya
Ph.D, Damascus,, Syrian Arab Republic
Sahar Alfahoum
Professor of Biochemistry, Damascus University,, Syrian Arab Republic
Razan Zohairee
Department of Toxicology and Pharmacology, Master Degree, Damascus University, Damascus,, Syrian Arab Republic
Al Zalak
Ph.D, Damascus,, Syrian Arab Republic

Abstract


Background: A novel coronavirus COVID-19 causing acute illness with severe symptoms, represents the causative agent of a contagious potentially lethal disease. COVID-19 was declared as pandemic by WHO. Aims: This Research aims to study the COVID-19 outbreaks in the fifteen most impacted countries in the world, find the relationship between the precautionary measures of governments and COVID-19 confirmed cases and deaths, and to forecast the pandemic in the following short time. Methods: The global numbers of confirmed cases and deaths of COVID-19 were obtained from the European Union Data. The data of governmentsʹ response actions for COVID-19 were estimated using the Oxford study. Box-Jenkins methodology, ARIMA model, R package were used in data analysis. Results: The rate of COVID-19 confirmed cases is 0.4 per thousand, and the death case rate is 0.03 per thousand of the world population. The rate of death cases was the lowest in Brazil, and the highest in Spain. The usefulness of precautionary measures and its effect on the number of confirmed cases and deaths in the different countries were estimated. A high correlation was established concerning the applied measurements and time of application. The model used for forecasting the expected cases was consistent with our tested result, while the model for forecasting death showed a fair consistently. Conclusion: We conclude that the health system must be reviewed, and these precautionary measures evaluated whether they are beneficial or more stringent conditions should be imposed.

Keywords


COVID-19, Patterns, ARIMA Models, Measures, Forecast, Countries.

References