Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China.

BACKGROUND: Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people's health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics. METHODS: A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed. RESULTS: DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics. CONCLUSIONS: The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics.

[Study on canopy spectral characteristics of paddy polluted by heavy metals].

Because of frequent mining, heavy metals are brought into environment like soils, water and atmosphere, resulting heavy metal contamination in the agricultural region beside mines. Heavy metals contamination causes vegetation stress like destruction of chloroplast structure, chlorophyll content decrease, blunt photosynthesis, etc. Spectral responses to changes in chlorophyll content and photosynthesis make it possible that remote sensing is applied in monitoring heavy metals stress on paddy plants. Field spectroradiometer was used to acquire canopy reflectance spectra of paddy plants contaminated by heavy metals released from local mining. The present study was conducted to (1) investigate discrimination of canopy reflectance spectra of heavy metal polluted and normal paddy plants; (2) extract spectral characteristics of contaminated paddy plants and compare them. By means of correlation analysis, sensitive bands (SB) were firstly picked out from canopy spectra. Secondly, on the basis of these sensitive bands, normalized difference vegetation indices (NDVI) were established, and then red edge position (REP) was extracted from canopy spectra via curve fitting of inverted Gaussian model. As a result of correlation analysis, 460, 560, 660 and 1 100 nm were considered respectively as sensitive band for Pb, Zn, Cu and As concentration in paddy leaves. Furthermore, heavy metal concentrations (Pb, Zn, Cu and As) were significantly correlated with NDVIs (Pb, NDV(510, 810); Zn, NDVI(510, 870; Cu, NDVI(660, 870); As, NDVI(510, 810)). Heavy metals were also significantly correlated with REP, however, the inflexion termed as spectral critical value (SCV) between low and high heavy metals concentrations should be considered during applying REP in remote sensing monitoring. Moreover, NDVI and REP are much better than SB in terms of capability of expressing spectral information. Therefore, heavy metals contamination in paddy plants can be remotely monitored via ground spectroradiometer when NDVI and REP are selected as spectral characteristics.

[Spatial epidemiological study on malaria epidemics in Hainan province].

OBJECTIVE: To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics. METHODS: Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct., 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST) were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0. RESULTS: The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: I (monthly, unit: 1/1,000,000) = exp (-1.672-0.399xLST). CONCLUSION: Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.