Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 1, 09 September 2022
Open
Access | Article
A Systematic Discussion of the Main Epidemic Prediction Models for the Spreading of COVID-19
* Author to whom correspondence should be addressed.
Abstract
Currently, many countries around the world are facing the crisis of COVID-19. Most of them cannot solve the problem of the spread of COVID-19. This study aims to analyze the four main pandemics’ prediction models through their advantages, shortages, the basic structures, appropriate scene as well as the improvement methods to ameliorate the whole statistical system of COVID-19. Today, the researchers have widely criticized the means and medium of data collection for the sake of pandemic spreading. Although the government has undertaken steps on the control of COVID-19, there were always many positive cases appeared at one time so that the viruses spread widely. In summary, this study offers new in sight for arranging the pandemic’s prediction method against the potential of the outbreak of all the epidemics.
Keywords
COVID-19, Prediction of pandemics, Statistics model
References
1. J., Rocklöv, H., Sjödin, (2020), High population densities catalyse the spread of COVID-19, Journal of Travel Medicine, https://doi.org/10.1093/jtm/taaa038.
2. A., Gryparis, C., J., Paciorek, (2009), Measurement error caused by spatial misalignment in environmental epidemiology, Biostatistics, 10.1093/biostatistics/kxn033.
3. National Health Commission of the People’s Republic of China (NHC PRC), Daily briefing on novel coronavirus cases in China. Beijing: NHC PRC, (2020), http:// en.nhc.gov.cn/2020-02/07/c_76323.htm
4. Adam J Kucharski, etc., (2020), Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study, National Library of Medicine, 10.1016/S1473-3099(20)30457-6.
5. N., S., Best, Richardson, A., Thomson, (2005), A comparison of Bayesian spatial models for disease mapping, Edward Arnold, 10.1191/0962280205sm388oaw.
6. Daniel Zeng, Zhidong Cao and Daniel B. Neill, (2021), Artificial intelligence-enabled public health surveillance-from local detection to global epidemic monitoring and control, Elsevier, https://doi.org/10.1016/B978-0-12-821259-2.00022-3.
7. R., Benedici, E., Bilotta & P., Pantano, (2021), A unifying nonlinear probabilistic epidemic model in space and time, Scientific Reports.
8. H., W., Hethcote, (1978), Modeling heterogeneity (mixing), V., Isham & G., Medley, Models for Infectious Human Diseases, 215.239, Cambridge, Issac Newton Institute for Mathmatical Science.
9. World Health Organization (WHO). Coronavirus disease (COVID-19) outbreak situation, (2020), https://www.who.int/emergencies/ diseases/novel-coronavirus-2019.
10. A., Cliff & P., Hagget, (2005), Viral Outbreak and Blood Clots, E., Beltrami, Mathmatical Models for Society and Biology, Academic Press, https://doi.org/10.1016/B978-0-12-404624-5.05001-9.
11. A., Cliff & P., Hagget, (2005), Red Tides and What Happened to the Red Squirrel, E., Beltrami, Mathmatical Models forSociety and Biology, 187, Academic Press, https://doi.org/10.1016/B978-0-12-404624-5.05001-9.
12. M., F., Carfora, 2018, Stochastic Processes, 1.2, Elsevier, 10.1016/B978-0-12-809633-8.20362-2.
13. W., J., Ewens, G., Grant, (2005). Statistical Methods in Bioinformatics: An Introduction, second ed. Springer.
14. D., R., Cox, H.D., Miller, (2001). The Theory of Stochastic Processes. Boca Raton, FL: Chapman and Hall/CRC.
15. M., Rendana, W.M.R., Idris, S.A., Rahim, (2021), Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves, Elsevier.
16. National Health Commission of the People’s Republic of China (NHC PRC). Timeline of China’s fight against the novel coronavirus. Beijing: NHC PRC; (2020), http://en.nhc.gov.cn/2020-03/20/c_78021.htm
17. World Health Organization (WHO). Infection prevention and control during health care when novel coronavirus (nCoV) infection is suspected. Interim guidance. Geneva: WHO. (2020), https://www.who.int/ publications-detail/infection-prevention-and-control-during- health-care-when-novel-coronavirus-(ncov)-infection-is- suspected-20200125.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- ISBN (Print)
- ISBN (Online)
- Published Date
- 09 September 2022
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/1/2022009
- Copyright
- © 2023 The Author(s)
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited