Submitted to: Infection, Genetics and Evolution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/1/2010
Publication Date: 10/15/2010
Citation: Ding, F., Zarlenga, D.S., Ren, X. 2010. A novel algorithm to define infection tendencies in H1N1 cases in mainland China. Infection, Genetics and Evolution. 11(1):222-226. Interpretive Summary: Since the World Health Organization (WHO) first identified H1N1 influenza outbreaks in the United States and Mexico in April 2009, the worldwide incidence of this disease has risen dramatically. This infection has had substantial impact on the politics, economies and public health in now endemic regions of the world, so much so that in June 2009, the WHO raised the pandemic alert level to phase 6 (maximum). This action prompted many countries to adopt counter-measures such as strengthening prevention and control, perfecting surveillance, and accelerating vaccine development. Nevertheless, a better model is needed to predict infection trends of H1N1 and other potential viral infections of humans that can pass through animals in order to advance effective prevention and control practices. Here we establish the mathematic model D-R using limited data points, and fit the H1N1 epidemic in mainland China as a test, then compare this model to the predictive capability of other models to show the enhanced predictive value of the new equation. This new enhanced model will assist in designing effective surveillance strategies for this and other viral diseases in order to determine a trend line for future infections, determine when and to what extent the infection rate deviates from the norm, and provide guidance on how well strategies to mitigate transmission are working.
Technical 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%]. 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.