A spatiotemporal mixed model to assess the influence of environmental and socioeconomic factors on the incidence of hand, foot and mouth disease.

BACKGROUND: As a common infectious disease, hand, foot and mouth disease (HFMD) is affected by multiple environmental and socioeconomic factors, and its pathogenesis is complex. Furthermore, the transmission of HFMD is characterized by strong spatial clustering and autocorrelation, and the classical statistical approach may be biased without consideration of spatial autocorrelation. In this paper, we propose to embed spatial characteristics into a spatiotemporal additive model to improve HFMD incidence assessment. METHODS: Using incidence data (6439 samples from 137 monitoring district) for Shandong Province, China, along with meteorological, environmental and socioeconomic spatial and spatiotemporal covariate data, we proposed a spatiotemporal mixed model to estimate HFMD incidence. Geo-additive regression was used to model the non-linear effects of the covariates on the incidence risk of HFMD in univariate and multivariate models. Furthermore, the spatial effect was constructed to capture spatial autocorrelation at the sub-regional scale, and clusters (hotspots of high risk) were generated using spatiotemporal scanning statistics as a predictor. Linear and non-linear effects were compared to illustrate the usefulness of non-linear associations. Patterns of spatial effects and clusters were explored to illustrate the variation of the HFMD incidence across geographical sub-regions. To validate our approach, 10-fold cross-validation was conducted. RESULTS: The results showed that there were significant non-linear associations of the temporal index, spatiotemporal meteorological factors and spatial environmental and socioeconomic factors with HFMD incidence. Furthermore, there were strong spatial autocorrelation and clusters for the HFMD incidence. Spatiotemporal meteorological parameters, the normalized difference vegetation index (NDVI), the temporal index, spatiotemporal clustering and spatial effects played important roles as predictors in the multivariate models. Efron's cross-validation R2 of 0.83 was acquired using our approach. The spatial effect accounted for 23% of the R2, and notable patterns of the posterior spatial effect were captured. CONCLUSIONS: We developed a geo-additive mixed spatiotemporal model to assess the influence of meteorological, environmental and socioeconomic factors on HFMD incidence and explored spatiotemporal patterns of such incidence. Our approach achieved a competitive performance in cross-validation and revealed strong spatial patterns for the HFMD incidence rate, illustrating important implications for the epidemiology of HFMD.

An Ensemble Spatiotemporal Model for Predicting PM2.5 Concentrations.

Although fine particulate matter with a diameter of <2.5 µm (PM2.5) has a greater negative impact on human health than particulate matter with a diameter of <10 µm (PM10), measurements of PM2.5 have only recently been performed, and the spatial coverage of these measurements is limited. Comprehensively assessing PM2.5 pollution levels and the cumulative health effects is difficult because PM2.5 monitoring data for prior time periods and certain regions are not available. In this paper, we propose a promising approach for robustly predicting PM2.5 concentrations. In our approach, a generalized additive model is first used to quantify the non-linear associations between predictors and PM2.5, the bagging method is used to sample the dataset and train different models to reduce the bias in prediction, and the variogram for the daily residuals of the ensemble predictions is then simulated to improve our predictions. Shandong Province, China, is the study region, and data from 96 monitoring stations were included. To train and validate the models, we used PM2.5 measurement data from 2014 with other predictors, including PM10 data, meteorological parameters, remote sensing data, and land-use data. The validation results revealed that the R² value was improved and reached 0.89 when PM10 was used as a predictor and a kriging interpolation was performed for the residuals. However, when PM10 was not used as a predictor, our method still achieved a CV R² value of up to 0.86. The ensemble of spatial characteristics of relevant factors explained approximately 32% of the variance and improved the PM2.5 predictions. The spatiotemporal modeling approach to estimating PM2.5 concentrations presented in this paper has important implications for assessing PM2.5 exposure and its cumulative health effects.