About: A novel algorithm to define infection tendencies in H1N1 cases in Mainland China

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About: A novel algorithm to define infection tendencies in H1N1 cases in Mainland China Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A novel algorithm to define infection tendencies in H1N1 cases in Mainland China
Creator	Ren, Xiaofeng Qin, Chengfeng Ding, Fan Zarlenga, Dante
Source	Elsevier; Medline; PMC
abstract	Incidences of H1N1 viral infections in Mainland China are collected by the Ministry of Health, the People's Republic of China. The number of confirmed cases and the timing of these outbreaks from May 13 to July 22, 2009 were obtained and subjected to a novel mathematical model to simulate the infection profile (time vs number). The model was predicated upon the grey prediction theory which allows assignment of future trends using limited numbers of data points. During the period of our analysis, the number of confirmed H1N1 cases in Mainland China increased from 1 to 1772. The efficiency of our model to simulate these data points was evaluated using Sum of squares of error (SSE), Relative standard error (RSE), Mean absolute deviation (MAD) and Average relative error (ARE). Results from these analyses were compared to similar calculations based upon the grey prediction algorithm. Using our equation, defined herein as equation D–R, results showed that SSE = 6742.00, RSE = 10.69, MAD = 7.07, ARE = 2.47% were all consistent with the D–R algorithm performing well in the estimation of future trends of H1N1 cases in Mainland China. Calculations using the grey theory had no predictive value [ARE for GM(1,1) = −104.63%]. To validate this algorithm, we performed a second analysis using new data obtained from cases reported to the WHO and CDC in the US between April 26 and June 8, 2009. In like manner, the model was equally predictive. The success of the D–R mathematical model suggests that it may have broader application to other viral infections among the human population in China and may be modified for application to other regions of the world.
has issue date	2010-10-15(xsd:dateTime)
bibo:doi	10.1016/j.meegid.2010.09.015
bibo:pmid	20951840
has license	no-cc
sha1sum (hex)	f65e7f96829cdeeb15c842d21887c51be3d72bfb
schema:url	https://doi.org/10.1016/j.meegid.2010.09.015
resource representing a document's title	A novel algorithm to define infection tendencies in H1N1 cases in Mainland China
has PubMed Central identifier	PMC7106193
has PubMed identifier	20951840
schema:publication	Infect Genet Evol
resource representing a document's body	covid:f65e7f96829cdeeb15c842d21887c51be3d72bfb#body_text
is schema:about of	named entity 'Mainland China' named entity 'algorithm' named entity 'H1N1' named entity 'infection' named entity 'H1N1' named entity 'Time Series Analysis' named entity 'China' named entity 'SSE' named entity 'prevention and control' named entity 'data points' named entity 'H1N1' named entity 'SARS' named entity 'pandemic alert level' named entity 'World Health Organization' named entity 'MAD' named entity 'D-R' named entity 'Mainland China' named entity 'epidemic' named entity 'H1N1' named entity 'Relative standard error' named entity 'absolute deviation' named entity 'ARIMA' named entity 'ARMA' named entity 'D-R' named entity 'Mainland China' named entity 'SSE' named entity 'WHO' named entity 'Severe Acute Respiratory Syndrome' named entity 'D-R' named entity 'RSE' named entity 'RSE' named entity 'H1N1' named entity '1/2' named entity 'RSE' named entity 'infection' named entity 'trend line' named entity 'H1N1' named entity 'MAD' named entity 'MAD' named entity 'infection' named entity 'influenza' named entity 'H1N1' named entity 'D-R' named entity 'phase 6' named entity 'H1N1' named entity 'H1N1' named entity 'Tracht' named entity 'May 13' named entity 'ARMA' named entity 'vaccine' named entity 'influenza' named entity 'D-R' named entity 'H1N1' named entity 'pandemics' named entity 'exponentially increasing' named entity 'Mainland China' named entity 'Table 1' named entity 'Autoregressive Integrated Moving Average' named entity 'H1N1' named entity 'H1N1' named entity 'human population' named entity 'infection rate' named entity 'relative error' named entity 'epidemic' named entity 'H1N1' named entity 'endemic' named entity 'epidemic' named entity 'SSE' named entity 'uncertainty analysis' named entity 'SSE' named entity 'TSA' named entity 'H1N1' named entity 'infection' named entity 'RSE' named entity 'SSE' named entity 'WHO' named entity 'organism' named entity 'CDC'

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