Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

Eupatorium makinoi plants with or without geminivirus infection were grown in shading frames with 70, 15 and 5.5% sunlight. Growth characteristics of these plants in the early vegetative phase were compared by means of growth analysis. We also measured leaf photosynthetic gas exchange rates and examined relationships between leaf photosynthesis and whole-plant growth. Relative growth rate (RGR=(1/W)×(dW/dt), where W is plant dry mass) of virus-infected plants was lower than that of uninfected plants under all three light conditions. The reduction of RGR by infection was increased with irradiance. The net assimilation rate (NAR=(1/A)×(dW/dt), where A is total leaf area of the plant) was also reduced both by infection and shading. NARs that were estimated from light-response curves of leaf photosynthesis, in situ measurements of irradiance, and respiration rates of leaves, stems and below-ground parts, agreed very well with the values obtained by conventional growth analysis techniques. Decreases in the estimated NAR value from infection and shading were mostly explained by the decreases in leaf photosynthesis. These results clearly showed that lowered RGR in virus-infected plants was attributed mainly to impaired photosynthesis in virus-infected leaves.