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 2006-2015 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 ac-curacy 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 addi-tion,the major changes of epidemic characteristics of infectious diseases must be considered.

Objective Sporadic cases occurring in mall geographic unit could lead to extreme value of incidence due to the small population bases,which would influence the analysis of actual incidence.Methods This study introduced a method of hierarchy clustering and partitioning regionalization,which integrates areas with small population into larger areas with enough population by using Geographic Information System (GIS) based on the principles of spatial continuity and geographical similarity (homogeneity test).This method was applied in spatial epidemiology by using a data set of thyroid cancer incidence in Yiwu,Zhejiang province,between 2010 and 2013.Results Thyroid cancer incidence data were more reliable and stable in the new regionalized areas.Hotspot analysis (Getis-Ord) on the incidence in new areas indicated that there was obvious case clustering in the central area of Yiwu.Conclusion This method can effectively solve the problem of small population base in small geographic units in spatial epidemiological analysis of thyroid cancer incidence and can be used for other diseases and in other areas.