BackgroundEnterovirus 71 (EV71) infections are a significant cause of neurological disorder and death in children worldwide. Seasonal variations in EV71 infections have been recognized, but the mechanisms responsible for this phenomenon remain unknown. The purpose of this study was to examine the relationship between meteorological parameters and EV71 infection.Methods and FindingsWe analyzed the number of EV71 infections and daily climate data collected in Taiwan between 1998 and 2008 and used Poisson regression analysis and case-crossover methodology to evaluate the association between weather variability and the incidence of EV71 infection. A total of 1,914 EV71-infected patients were reported between 1998 and 2008. The incidence of EV71 infections reflected significant summertime seasonality (for oscillation, p<0.001). The incidence of EV71 infections began to rise at temperatures above 13°C (r2 = 0.76, p<0.001); at temperatures higher than approximately 26°C (r2 = 0.94, p<0.05), the incidence began to decline, producing an inverted V-shaped relationship. The increase in the incidence with increasing relative humidity was positive and linear (r2 = 0.68, p<0.05). EV71 infection was most highly correlated with temperature and relative humidity in the period that likely preceded the infection.ConclusionOur study provides quantitative evidence that the rate of EV71 infection increased significantly with increasing mean temperature and relative humidity in Taiwan.

Vol. 15, No. 5, December 2004 760 range transport. When this contribution is taken into account, we estimate that the contribution of long-range transport to PM 10 abundance in northern Taiwan during winter and spring to be in the range of 50% to 75%.

These authors equally contributed to this work. Keywords: infectious disease, meteorological parameters, modeling, mumps, weatherMumps is caused by a paramyxovirus. It is an acute, but mild infectious disease. However, approximately 10% of patients with mumps can develop severe meningoencephalitis, disability, and death. Seasonal patterns in mumps vary across countries, but the reasons for this phenomenon remain unclear. The aim of this study was to assess the role of meteorological factors on mumps infection. We investigated the relationships between weather variability and the incidence of mumps in Taiwan using a Poisson regression analysis and case-crossover methodology. Between 2006 and 2011, 6,612 cases of mumps were reported to the Centers for Disease Control, Taiwan (Taiwan CDC). The incidence of mumps showed a significant seasonality in summertime (for oscillation, P < 0.001). The number of mumps started to increase at temperatures of 20 C (r 2 D 0.73, P < 0.001), and the case count of mumps began to decline when the temperatures were higher than approximately 25 C (r 2 D 0.24, p D 0.04), producing an inverted V-shaped relationship. Similarly, the number of mumps began to increase at a vapor pressure of 5-9 hPa (r 2 D 0.87, P < 0.005) and decreased at a vapor pressure higher than 25-29 hPa (r 2 D 0.21, p D 0.05). The number of mumps cases was positively associated with temperature and vapor pressure in the preceding period of the infection. In conclusion, this study showed that the occurrence of mumps is significantly associated with increasing temperature and vapor pressure in Taiwan. Therefore, these factors could be regarded as warning signals indicating the need to implement preventive measures.

Because the nonlinear and time-varying characteristics of the continuously variable transmission system operated using a six-phase copper rotor induction motor are unknown, improving the control performance of the linear control design is time-consuming. To capture the nonlinear and dynamic behaviour of the six-phase copper rotor induction motor servo-driven continuously variable transmission system, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the nonlinear time-varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control, and recompensed control. Moreover, the adaptation law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee colony optimization yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme.