Evaluation of scrub typhus diagnosis in China: analysis of nationwide surveillance data from 2006 to 2016.

BACKGROUND: Scrub typhus is a life-threatening disease caused by Orientia tsutsugamushi, and specific antimicrobial medicine is available. Early and accurate diagnosis is essential for reducing the risk of severe complications and death. In this study, we aimed to evaluate the case diagnosis situation among medical care institutions and geographical regions in China, and the results will benefit both clinical practice and the disease surveillance system. METHODS: We extracted individual scrub typhus case data 2006-2016 from a national disease surveillance system in China. The diagnosis category and interval time from illness onset to diagnosis were compared among three levels of medical care institutions and provinces. The descriptive analysis method was performed in our study. RESULTS: During the 11-year study period, 93 481 scrub typhus cases, including 57 deaths, were recorded in the nationwide surveillance system. The overall proportion of laboratory-confirmed cases was only 4.7%, and this proportion varied greatly among primary medical centres (2.8%), county level hospitals (4.2%), and city level hospitals (6.3%). Notably, the proportion of laboratory-confirmed cases has consistently decreased from 16.3% in 2006 to 2.6% in 2016, and the same decreasing trend was found among all three levels of medical care institutions. The interval from illness onset to case diagnosis (Tdiag) for all cases was 5 days (interquartile range [IQR]: 2-9 days) and decreased from 7 days (IQR: 3-11 days) in 2006 to 5 days (IQR: 2-8 days) in 2016. The risk of death for patients with a Tdiag of > 7 days was 2.2 times higher (OR = 2.21, 95% CI: 1.05-5.21) than that of patients with a Tdiag of < 2 days. CONCLUSIONS: The interval time from illness onset to diagnosis for scrub typhus cases decreased greatly in China; however, the diagnosis rate of cases with laboratory-confirmed results must be increased among all levels of medical care institutions to reduce both the risk of death and the misuse of antibiotics associated with scrub typhus.

Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data.

The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.

Spatio-temporal analysis of malaria vectors in national malaria surveillance sites in China.

BACKGROUND: To reveal the spatio-temporal distribution of malaria vectors in the national malaria surveillance sites from 2005 to 2010 and provide reference for the current National Malaria Elimination Programme (NMEP) in China. METHODS: A 6-year longitudinal surveillance on density of malaria vectors was carried out in the 62 national malaria surveillance sites. The spatial and temporal analyses of the four primary vectors distribution were conducted by the methods of kernel k-means and the cluster distribution of the most widely distribution vector of An.sinensis was identified using the empirical mode decomposition (EMD). RESULTS: Totally 4 species of Anopheles mosquitoes including An.sinensis, An.lesteri, An.dirus and An.minimus were captured with significant difference of distribution as well as density. An. sinensis was the most widely distributed, accounting for 96.25% of all collections, and its distribution was divided into three different clusters with a significant increase of density observed in the second cluster which located mostly in the central parts of China. CONCLUSION: This study first described the spatio-temporal distribution of malaria vectors based on the nationwide surveillance during 2005-2010, which served as a baseline for the ongoing national malaria elimination program.

Estimation of citywide air pollution in Beijing.

There has been discrepancies between the daily air quality reports of the Beijing municipal government, observations recorded at the U.S. Embassy in Beijing, and Beijing residents' perceptions of air quality. This study estimates Beijing's daily area PM(2.5) mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM(2.5) observation station of the U.S Embassy and the 18 PM(10) observation stations of the Beijing Municipal Environmental Protection Bureau. The proposed technique accounts for empirical relationships between different types of observations, and generates best linear unbiased pollution estimates (in a statistical sense). The technique extends the daily PM(2.5) mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM(2.5) monitoring station and at the 18 official PM(10) stations that are evenly distributed across the city. Insight about the technique's spatial estimation accuracy (uncertainty) is gained by means of theoretical considerations and numerical validations involving real data. The technique was used to study citywide PM(2.5) pollution during the 423-day period of interest (May 10, 2010 to December 6, 2011). Finally, a freely downloadable software library is provided that performs all relevant calculations of pollution estimation.

Spatial estimation of antibiotic residues in surface soils in a typical intensive vegetable cultivation area in China.

Antibiotic residues in surface soils can lead to serious health risks and ecological hazards. Spatial mean concentration of antibiotic residues in the soil is the most important indicator of a region's environmental risk to antibiotic residues. Considerable estimation error would lead to an inefficient strategy of pollution control that happens when sample size is small and the estimation model does not match the spatial features of the object to be surveyed. On the basis of the available datasets, it was found that the distribution of antibiotics residue in soil follows a spatial stratification pattern. Accordingly, we used a new spatial estimation method called Mean of Surface with Non-homogeneity (MSN) to estimate antibiotic concentrations in surface soil of the Shandong Province, an important vegetable growing region in China. The standard error of the mean estimates obtained by MSN was significantly smaller (by about 1.02-6.82 µg/kg) than the estimation errors produced by three mainstream methods, simple arithmetic estimation (2.9-11.8 µg/kg), stratified estimation (2.5-10.6 µg/kg) and ordinary kriging estimation (2.2-8.2 µg/kg).

Area disease estimation based on sentinel hospital records.

BACKGROUND: Population health attributes (such as disease incidence and prevalence) are often estimated using sentinel hospital records, which are subject to multiple sources of uncertainty. When applied to these health attributes, commonly used biased estimation techniques can lead to false conclusions and ineffective disease intervention and control. Although some estimators can account for measurement error (in the form of white noise, usually after de-trending), most mainstream health statistics techniques cannot generate unbiased and minimum error variance estimates when the available data are biased. METHODS AND FINDINGS: A new technique, called the Biased Sample Hospital-based Area Disease Estimation (B-SHADE), is introduced that generates space-time population disease estimates using biased hospital records. The effectiveness of the technique is empirically evaluated in terms of hospital records of disease incidence (for hand-foot-mouth disease and fever syndrome cases) in Shanghai (China) during a two-year period. The B-SHADE technique uses a weighted summation of sentinel hospital records to derive unbiased and minimum error variance estimates of area incidence. The calculation of these weights is the outcome of a process that combines: the available space-time information; a rigorous assessment of both, the horizontal relationships between hospital records and the vertical links between each hospital's records and the overall disease situation in the region. In this way, the representativeness of the sentinel hospital records was improved, the possible biases of these records were corrected, and the generated area incidence estimates were best linear unbiased estimates (BLUE). Using the same hospital records, the performance of the B-SHADE technique was compared against two mainstream estimators. CONCLUSIONS: The B-SHADE technique involves a hospital network-based model that blends the optimal estimation features of the Block Kriging method and the sample bias correction efficiency of the ratio estimator method. In this way, B-SHADE can overcome the limitations of both methods: Block Kriging's inadequacy concerning the correction of sample bias and spatial clustering; and the ratio estimator's limitation as regards error minimization. The generality of the B-SHADE technique is further demonstrated by the fact that it reduces to Block Kriging in the case of unbiased samples; to ratio estimator if there is no correlation between hospitals; and to simple statistic if the hospital records are neither biased nor space-time correlated. In addition to the theoretical advantages of the B-SHADE technique over the two other methods above, two real world case studies (hand-foot-mouth disease and fever syndrome cases) demonstrated its empirical superiority, as well.

Super-resolution reconstruction of remote sensing images using multifractal analysis.

Satellite remote sensing (RS) is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intra-urban). In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolution-enhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well in detail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics.

[Soil taxonomy on the basis of reflectance spectral characteristics].

Soil spectral reflectance is the comprehensive representation of soil physical and chemical parameters, and its study is the physical basis for soil remote sensing and provides a new way and standard for soil properties themselves' research. Soil room spectra significantly correlate with that derived from hyperspectral images. So the room spectra are very important for soil taxonomy and investigation. To seek for the feasibility of soil taxonomy on the basis of topsoil reflectance spectral characteristics, and provide the theory foundation for quick soil taxonomy based on remote sensing methods, the spectral reflectance in the visible and near infrared region (400-2 500 nm) of 248 soil samples (black soil, chernozem, meadow soil, blown soil, alluvial soil) collected from Nongan county, Jilin province was measured with a hyperspectral device in room, and the soil spectral characteristics were determined with continuum removal method, and soil spectral indices (spectral absorption area, depth and asymmetry) were computed, which were introduced into BP network models as external input variables. The models consist of three layers (input, output and hidden layer), the training function is "TRAINLM", learning function "LEARNGDM", and transferring function "TAN SIG". The results showed that: (1) There are some differences among different soils in their spectral characteristics, but with similar parental matrix and climate, the spectral differences of soils in Nongan county are not significant. So it's difficult to analyze soil spectral characteristics based on soil reflectance. (2) The curves after continuum removal strengthened soil spectral absorption characteristics, and simplified soil spectral analysis. The soil spectral curves in Nongan county mainly have five spectral absorption vales at 494, 658, 1 415, 1 913 and 2 206 nm, and the former two vales are caused by soil organic matter, Fe and mechanical composition, the latter three are due to soil moisture; the differences of the latter three vales among different soils are not apparent, and the significant differences are in the former two vales region. (3) Soil reflectance is sensitive to organic matter, soil moisture, Fe, mechanical composition, roughness, and so on. The sensitivity of soil spectral indices derived with continuum removing method is decreased. Then the models with these indices as input variables are more stable and general. As the input variables were external, the BP network model based on the former two vales' shape characteristics was better than that based on reflectance values or all five vales, the classifying accuracy of the main three soils (chernozem, meadow soil, blown soil) was bigger than 60%, and the model could be used for soil taxonomy. However, this work still needs further study, and to improve classifying accuracy, auxiliary data, such as topography, vegetation, and land use should be introduced.