The early identification and prediction of hand-foot-and-mouth disease (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regions due to the differences in geography, social economy factors. We collected data associated with daily reported HFMD cases and weather factors of Zibo city in 2010~2019 and used the generalised additive model (GAM) to evaluate the effects of weather factors on HFMD cases. Then, GAM, support vectors regression (SVR) and random forest regression (RFR) models are used to compare predictive results. The annual average incidence was 129.72/100 000 from 2010 to 2019. Its distribution showed a unimodal trend, with incidence increasing from March, peaking from May to September. Our study revealed the nonlinear relationship between temperature, rainfall and relative humidity and HFMD cases and based on the predictive result, the performances of three models constructed ranked in descending order are: SVR > GAM> RFR, and SVR has the smallest prediction errors. These findings provide quantitative evidence for the prediction of HFMD for special high-risk regions and can help public health agencies implement prevention and control measures in advance.

Excessive iodine can lead to goiters. However, the relationship between the water iodine concentration (WIC) and goiter rate (GR) is unclear. This study aims to explore the factors that influence children’s GR in areas with high WIC and analyse the threshold value of the GR increase associated with the WIC. According to the monitoring of the areas with high WIC in China in 2018–2020, a total of 54 050 children in eight high water iodine provinces were chosen. Drinking water, urine and edible salt samples of children were collected. The thyroid volume (Tvol) was measured. A generalised additive model (GAM) was used to analyse the relationship between the WIC and GR in children. Among the 54 050 children in areas with high WIC, the overall GR was 3·34 %, the median of water iodine concentration was 127·0 µg/l, the median of urinary iodine concentration was 318 µg/l and the non-iodised salt coverage rate (NISCR) was 63·51 %. According to the GAM analysis results, water iodine and urinary iodine are factors that influence the Tvol and GR, while the NISCR affects only the GR. When the WIC was more than 420 µg/l or the urinary iodine concentration was more than 800 µg/l, the GR increased rapidly. When the NISCR reached more than 85 %, the GR was the lowest. Thus, in areas with high WIC, WIC more than 420 µg/l may increase the risk of goiter, and the NISCR should be increased to over 85 % to avoid goiters in children.

This paper considers a wide range of stochastic reserving models for use in general insurance, beginning with stochastic models which reproduce the traditional chain-ladder reserve estimates. The models are extended to consider parametric curves and smoothing models for the shape of the development run-off, which allow extrapolation for the estimation of tail factors. The Bornhuetter-Ferguson technique is also considered, within a Bayesian framework, which allows expert opinion to be used to provide prior estimates of ultimate claims. The primary advantage of stochastic reserving models is the availability of measures of precision of reserve estimates, and in this respect, attention is focused on the root mean squared error of prediction (prediction error). Of greater interest is a full predictive distribution of possible reserve outcomes, and different methods of obtaining that distribution are described. The techniques are illustrated with examples throughout, and the wider issues discussed, in particular, the concept of a ‘best estimate’; reporting the variability of claims reserves; and use in dynamic financial analysis models.