Please use this identifier to cite or link to this item: https://covid-19.conacyt.mx/jspui/handle/1000/3618
Understanding the CoVID-19 pandemic Curve through statistical approach
Akhtar Ibrar ul Hassan.
Acceso Abierto
Atribución-NoComercial-SinDerivadas
10.1101/2020.04.06.20055426
Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.
www.medrxiv.org
2020
Artículo
https://www.medrxiv.org/content/medrxiv/early/2020/04/08/2020.04.06.20055426.full.pdf
Inglés
VIRUS RESPIRATORIOS
Appears in Collections:Artículos científicos

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