Chengdong Xu

A new estimate of the China temperature anomaly series and uncertainty assessment in 1900–2006

While global warming during the last century has been well recognized, the magnitude of the climate warming in regions such as China over the past 100years still has some uncertainty due to limited observations during the early years. Several series of temperature anomalies for the 20(th) century in China have been independently developed by different groups. The uncertainty arises mainly from the sparse observations before 1950, where statistics are sensitive to the small and potentially biased sample. In this study, BSHADE-MSN (Biased Sentinel Hospitals Areal Disease Estimation and Means of Stratified Nonhomogeneous Surface), a combination of two novel distinct statistical methods that are applicable with different sample situations to a spatial heterogeneous surface, is applied to estimate annual mean temperature anomalies for China. This method takes into account prior knowledge of geographical spatial autocorrelation and nonhomogeneity of target domains, remedies the biased sample, and maximizes an objective function for the best linear unbiased estimation (BLUE) of the regional mean quantity. For the period 1900-1999, the overall trend estimated by BSHADE-MSN is 0.80 degrees C with a 95% confidential interval between 0.41 degrees C and 1.18 degrees C. This is significantly lower than that calculated by Climate Anomaly Method (CAM) and Block Kriging. The new temperature anomaly series for China exhibits slightly warmer conditions for the period before 1950 than existing studies. All the methods applied so far agree well with each other for the period after 1950, when there are sufficient stations across the country for the estimation of temperature anomaly series. Cross validation shows that the new regional mean temperature anomaly series has smaller estimation error variance and higher accuracy than those based on the other methods assessed in this study.

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 PM2.5 mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM2.5 observation station of the U.S Embassy and the 18 PM10 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 PM2.5 mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM2.5 monitoring station and at the 18 official PM10 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 PM2.5 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.

Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique

Hand, foot and mouth disease (HFMD) is a common infectious disease, causing thousands of deaths among children in China over the past two decades. Environmental risk factors such as meteorological factors, population factors and economic factors may affect the incidence of HFMD. In the current paper, we used a novel model—geographical detector technique to analyze the effect of these factors on the incidence of HFMD in China. We collected HFMD cases from 2,309 counties during May 2008 in China. The monthly cumulative incidence of HFMD was calculated for children aged 0–9 years. Potential risk factors included meteorological factors, economic factors, and population density factors. Four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) were used to analyze the effects of some potential risk factors on the incidence of HFMD in China. We found that tertiary industry and children exert more influence than first industry and middle school students on the incidence of HFMD. The interactive effect of any two risk factors increases the hazard for HFMD transmission.

Spatial association between dissection density and environmental factors over the entire conterminous United States

Previous studies of land dissection density (D) often find contradictory results regarding factors controlling its spatial variation. We hypothesize that the dominant controlling factors (and the interactions between them) vary from region to region due to differences in each regions local characteristics and geologic history. We test this hypothesis by applying a geographical detector method to eight physiographic divisions of the conterminous United States and identify the dominant factor(s) in each. The geographical detector method computes the power of determinant (q) that quantitatively measures the affinity between the factor considered and D. Results show that the factor (or factor combination) with the largest q value is different for physiographic regions with different characteristics and geologic histories. For example, lithology dominates in mountainous regions, curvature dominates in plains, and glaciation dominates in previously glaciated areas. The geographical detector method offers an objective framework for revealing factors controlling Earth surface processes.

Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE*

Some climate datasets are incomplete at certain places and times. A novel technique called the point estimationmodelofBiasedSentinelHospitals-basedAreaDiseaseEstimation(P-BSHADE)isintroducedto interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calculation of these weights. In this way, interpolation of missing data in the temperature dataset was improved, and best linear unbiased estimates (BLUE) were obtained. Using the same dataset, performance of P-BSHADE was compared against three estimators: kriging, inverse distance weighting (IDW), and spatial regression test (SRT). Kriging and IDW assume a homogeneous stochastic field, which may not be the case. SRT employs spatiotemporal data and has the potential to consider temperature nonhomogeneity caused by topographic differences, but has no objective function for the BLUE. Instead, P-BSHADE takes into account geographic spatial autocorrelation and nonhomogeneity, and maximizes an objective function for the BLUE of the target station. In addition to the theoretical advantages of P-BSHADE over the three other methods, case studies for an annual Chinese temperature dataset demonstrate its empirical superiority, except for the SRT from 1950 to 1970.

Estimation of Uncertainty in Temperature Observations Made at Meteorological Stations Using a Probabilistic Spatiotemporal Approach

A probabilistic spatiotemporal approach based on a spatial regression test (SRT-PS) is proposed for the quality control of climate data. It provides a quantitative probability that represents the uncertainty in each temperature observation. The assumption of SRT-PS is that there might be large uncertainty in the station record if there is a large residual difference between the record estimated in the spatial regression test and the true station record. The result of SRT-PS is expressed as a confidence probability ranging from 0 to 1, where a value closer to 1 indicates less uncertainty . The potential of SRT-PS to estimate quantitatively the uncertainty in temperature observations was demonstrated using an annual temperature dataset for China for the period 1971-2000 with seeded errors. SRT-PS was also applied to assess a real dataset, and was compared with two traditional quality control approaches: biweight mean and biweight standard deviation and SRT. The study provides a new approach to assess quantitatively the uncertainty in temperature observations at meteorological stations.

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.

Spatiotemporal pattern of hand–foot–mouth disease in China: an analysis of empirical orthogonal functions

OBJECTIVES Hand-foot-mouth disease (HFMD) is the most common infectious disease in China. Spatial and temporal patterns of HFMD in China provide valuable information on the relationship between HFMD and the geographical environment, and help in the prediction of HFMD transmission. STUDY DESIGN Cross-sectional study. METHODS Total HFMD morbidity per 10 days from May 2008 to March 2009 was recorded in 1966 counties in China. Empirical orthogonal function (EOF) analysis was used to obtain spatial and temporal patterns of HFMD. RESULTS The first five modes of HFMD morbidity explained 84.24% of the total variance. The dominant mode (first mode showing the highest variance) showed high HFMD morbidity in the western counties of Bohai Bay, the mid-south of China, the Yangtze River delta, the Pearl River delta and the areas bordering Vietnam from early May to late July 2008. The second mode showed high HFMD morbidity in the western counties of Bohai Bay, the north-east of China, north of Xinjiang and the Yangtze River delta from late May to the middle of August 2008. The third mode showed high HFMD morbidity in the Yangtze River delta, the Pearl River delta and the middle of the Huaihe River basin in early May 2008. CONCLUSIONS EOF analysis of HFMD morbidity shows the main spatiotemporal patterns and can explain variance in HFMD in China.

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

BackgroundAs 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.MethodsUsing 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.ResultsThe 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.ConclusionsWe 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.

Spatiotemporal epidemic characteristics and risk factor analysis of malaria in Yunnan Province, China

BackgroundMalaria remains an important public health concern in China and is particularly serious in Yunnan, a China’s provincial region of high malaria burden with an incidence of 1.79/105 in 2012. This study aims to examine the epidemiologic profile and spatiotemporal aspects of epidemics of malaria, and to examine risk factors which may influence malaria epidemics in Yunnan Province.MethodsThe data of malaria cases in 2012 in 125 counties of Yunnan Province was used in this research. The epidemical characteristics of cases were revealed, and time and space clusters of malaria were detected by applying scan statistics method. In addition, we applied the geographically weighted regression (GWR) model in identifying underlying risk factors.ResultsThere was a total of 821 cases of malaria, and male patients accounted for 83.9% (689) of the total cases. The incidence in the group aged 20–30 years was the highest, at 3.00/105. The majority (84.1%) of malaria cases occurred in farmers and migrant workers, according to occupation statistics. On a space-time basis, epidemics of malaria of varying severity occurred in the summer and autumn months, and the high risk regions were mainly distributed in the southwest counties. Annual average temperature, annual cumulative rainfall, rice yield per square kilometer and proportion of rural employees mainly showed a positive association with the malaria incidence rate, according to the GWR model.ConclusionsMalaria continues to be one of serious public health issues in Yunnan Province, especially in border counties in southwestern Yunnan. Temperature, precipitation, rice cultivation and proportion of rural employees were positively associated with malaria incidence. Individuals, and disease prevention and control departments, should implement more stringent preventative strategies in locations with hot and humid environmental conditions to control malaria.