[Establishment of response system to emergency parasitic disease affairs in China].

China's prevention and control of parasitic diseases has made remarkable achievements. However, the prevalence and transmission of parasitic diseases is impacted by the complicated natural and social factors of environment, natural disasters, population movements, and so on. Therefore, there are still the risks of the outbreak of emergency parasitic diseases affairs, which may affect the control effectiveness of parasitic diseases and endanger the social stability seriously. In this article, we aim at the analysis of typical cases of emergency parasitic disease affairs and their impacts on public health security in China in recently years, and we also elaborate the disposal characteristics of emergency parasitic disease affairs, and propose the establishment of response system to emergency parasitic disease affairs in China, including the organizational structure and response flow path, and in addition, point out that, in the future, we should strengthen the system construction and measures of the response system to emergency parasitic disease affairs, so as to control the risk and harm of parasitic disease spread as much as possible and to realize the early intervention and proper disposal of emergency parasitic disease affairs.

[Application of ARIMA model to predict number of malaria cases in China].

Objective To study the application of autoregressive integrated moving average (ARIMA) model to predict the monthly reported malaria cases in China, so as to provide a reference for prevention and control of malaria. Methods SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported malaria cases of the time series of 20062015 and 2011-2015, respectively. The data of malaria cases from January to December, 2016 were used as validation data to compare the accuracy of the two ARIMA models. Results The models of the monthly reported cases of malaria in China were ARIMA (2, 1, 1) (1, 1, 0)12 and ARIMA (1, 0, 0) (1, 1, 0)12 respectively. The comparison between the predictions of the two models and actual situation of malaria cases showed that the ARIMA model based on the data of 2011-2015 had a higher accuracy of forecasting than the model based on the data of 2006-2015 had. Conclusion The establishment and prediction of ARIMA model is a dynamic process, which needs to be adjusted unceasingly according to the accumulated data, and in addition, the major changes of epidemic characteristics of infectious diseases must be considered.