Application of Seasonal Auto-regressive Integrated Moving Average Model in Forecasting the Incidence of Hand-foot-mouth Disease in Wuhan,China

Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

Impact of Electric Vehicles on Travel and Electricity Demand in Metropolitan Area： A Case Study in Nagoya

In this study, we examine the impacts that EVs （electric vehicles） have on vehicle usage patterns and environmental improvements, using our integrated travel demand forecasting model, which can simulate an individual activity-travel behavior in each time period, as well as consider an induced demand by decreasing travel cost. In order to examine the effects that charging/discharging have on the demand in electricity, we analyze scenarios based on the simulation results of the EVs＇ parking location, parking duration and the battery state of charge. From the simulation, result under the ownership rate of EVs in the Nagoya metropolitan area in 2020 is about 6%, which turns out that the total CO2 emissions have decreased by 4% although the situation of urban transport is not changed. After calculating the electricity demand in each zone using architectural area and basic units of hourly power consumption, we evaluate the effect to decrease the peak load by V2G （vehicle-to-grid）. According to the results, if EV drivers charge at home during the night and discharge at work during the day, the electricity demand in Nagoya city increases by approximately 1%, although changes in each individual zone range from -7% to ＋8%, depending on its characteristics.