Quantifying the influences of various ecological factors on land surface temperature of urban forests.

Identifying factors that influence the land surface temperature (LST) of urban forests can help improve simulations and predictions of spatial patterns of urban cool islands. This requires a quantitative analytical method that combines spatial statistical analysis with multi-source observational data. The purpose of this study was to reveal how human activities and ecological factors jointly influence LST in clustering regions (hot or cool spots) of urban forests. Using Xiamen City, China from 1996 to 2006 as a case study, we explored the interactions between human activities and ecological factors, as well as their influences on urban forest LST. Population density was selected as a proxy for human activity. We integrated multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) to develop a database on a unified urban scale. The driving mechanism of urban forest LST was revealed through a combination of multi-source spatial data and spatial statistical analysis of clustering regions. The results showed that the main factors contributing to urban forest LST were dominant tree species and elevation. The interactions between human activity and specific ecological factors linearly or nonlinearly increased LST in urban forests. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. In conclusion, quantitative studies based on spatial statistics and GeogDetector models should be conducted in urban areas to reveal interactions between human activities, ecological factors, and LST.

Clustering of disability caused by unintentional injury among 15- to 60-year-old: a challenge in rapidly developing countries.

Little is known about disability caused by unintentional injury (accidents) worldwide. This study estimates the prevalence of disability caused by unintentional injury amongst people aged 15-60 years across different cities in the People's Republic of China with the aim of providing a scientific basis for developing prevention and control programmes. The prevalence of disability caused by unintentional injury in this target group in sampled cities across the country was estimated from data from the Second Chinese National Sample Survey on Disability. Using the statistical evolution tree approach, cities automatically clustered into a tree structure according to the level of social security and industrial structure. The Kruskal- Wallis test was applied to compare the prevalence in various types of city. The results show that the prevalence of disability due to unintentional injury in the target population group varied significantly across the 16 types of city investigated, but that it was particularly common among the unemployed and poor. With regard to occupational structure, cities with activities oriented towards transport and construction had the highest average prevalence despite access to local, relatively sound social security systems and adequate medical resources. It was also found that people struck by unintentional injury were treated in various ways depending on the availability of social assistance, medical care and job training, which differed widely between cities depending on each city's main occupational activity. High-risk cities areas were identified for that would benefit particularly by additional medical resource allocation as it would reduce their burden of unintentional injury.

PSO/ACO algorithm-based risk assessment of human neural tube defects in Heshun County, China.

OBJECTIVE: To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world. METHODS: The region of interest in this study was Heshun County, the county in China with the highest rate of neural tube defects (NTDs). A hybrid particle swarm optimization/ant colony optimization (PSO/ACO) algorithm was used to quantify the probability of NTDs occurring at villages with no births. The hybrid PSO/ACO algorithm is a form of artificial intelligence adapted for hierarchical classification. It is a powerful technique for modeling complex problems involving impacts of causes. RESULTS: The algorithm was easy to apply, with the accuracy of the results being 69.5%±7.02% at the 95% confidence level. CONCLUSION: The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.

[Comparison on the performance of both temporal and temporal-spatial models for outbreak detection through China Infectious Diseases Automated-alert and Response System (CIDARS)].

OBJECTIVE: To analyze the pilot results of both temporal and temporal-spatial models in outbreaks detection in China Infectious Diseases Automated-alert and Response System (CIDARS) to further improve the system. METHODS: The amount of signal, sensitivity, false alarm rate and time to detection regarding these two models of CIDARS, were analyzed from December 6, 2009 to December 5, 2010 in 221 pilot counties of 20 provinces. RESULTS: The sensitivity of these two models was equal (both 98.15%). However, when comparing to the temporal model, the temporal-spatial model had a 59.86% reduction on the signals (15 702) while the false alarm rate of the temporal-spatial model (0.73%) was lower than the temporal model (1.79%), and the time to detection of the temporal-spatial model (0 day) was also 1 day shorter than the temporal model. CONCLUSION: Comparing to the temporal model, the temporal-spatial model of CIDARS seemed to be better performed on outbreak detection.

Hand, foot and mouth disease: spatiotemporal transmission and climate.

BACKGROUND: The Hand-Foot-Mouth Disease (HFMD) is the most common infectious disease in China, its total incidence being around 500,000~1,000,000 cases per year. The composite space-time disease variation is the result of underlining attribute mechanisms that could provide clues about the physiologic and demographic determinants of disease transmission and also guide the appropriate allocation of medical resources to control the disease. METHODS AND FINDINGS: HFMD cases were aggregated into 1456 counties and during a period of 11 months. Suspected climate attributes to HFMD were recorded monthly at 674 stations throughout the country and subsequently interpolated within 1456 × 11 cells across space-time (same as the number of HFMD cases) using the Bayesian Maximum Entropy (BME) method while taking into consideration the relevant uncertainty sources. The dimensionalities of the two datasets together with the integrated dataset combining the two previous ones are very high when the topologies of the space-time relationships between cells are taken into account. Using a self-organizing map (SOM) algorithm the dataset dimensionality was effectively reduced into 2 dimensions, while the spatiotemporal attribute structure was maintained. 16 types of spatiotemporal HFMD transmission were identified, and 3-4 high spatial incidence clusters of the HFMD types were found throughout China, which are basically within the scope of the monthly climate (precipitation) types. CONCLUSIONS: HFMD propagates in a composite space-time domain rather than showing a purely spatial and purely temporal variation. There is a clear relationship between HFMD occurrence and climate. HFMD cases are geographically clustered and closely linked to the monthly precipitation types of the region. The occurrence of the former depends on the later.

Prediction of neural tube defect using support vector machine.

OBJECTIVE: To predict neural tube birth defect (NTD) using support vector machine (SVM). METHOD: The dataset in the pilot area was divided into non overlaid training set and testing set. SVM was trained using the training set and the trained SVM was then used to predict the classification of NTD. RESULT: NTD rate was predicted at village level in the pilot area. The accuracy of the prediction was 71.50% for the training dataset and 68.57% for the test dataset respectively. CONCLUSION: Results from this study have shown that SVM is applicable to the prediction of NTD.

Assessing local determinants of neural tube defects in the Heshun Region, Shanxi Province, China.

BACKGROUND: Neural tube defect (NTD) prevalence in northern China is among the highest worldwide. Dealing with the NTD situation is ranked as the number one task in China's scientific development plan in population and health field for the next decade. Physical and social environments account for much of the disease's occurrence. The environmental determinants and their effects on NTD vary across geographical regions, whereas factors that play a significant role in NTD occurrence may be buried by global statistics analysis to a pooled dataset over the entire study area. This study aims at identification of the local determinants of NTD across the study area and exploration of the epidemiological implications of the findings. METHODS: NTD prevalence rate is represented in terms of the random field theory, and Rushton's circle method is used to stabilize NTD rate estimation across the geographical area of interest; NTD determinants are represented by their measurable proxy variables and the geographical weighted regression (GWR) technique is used to represent the spatial heterogeneity of the NTD determinants. RESULTS: Informative maps of the NTD rates and the statistically significant proxy variables are generated and rigorously assessed in quantitative terms. CONCLUSIONS: The NTD determinants in the study area are investigated and interpreted on the basis of the maps of the proxy variables and the relationships between the proxy variables and the NTD determinants. No single determinant was found to dominate the NTD occurrence in the study area. Villages where NTD rates are significantly linked to environmental determinants are identified (some places are more closely linked to certain environmental factors than others). The results improve current understanding of NTD spread in China and provide valuable information for adequate disease intervention planning.