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Background Modern transportation plays a key role in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and new variants. However, little is known about the exact transmission risk of the virus on airplanes. Methods Using the itinerary and epidemiological data of coronavirus disease 2019 (COVID-19) cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on 23 January 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travelers. Results In total, 175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60% (34/5622, 95% confidence interval [CI] .43–.84%) and 0.33% (18/5400, 95% CI .21–.53%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases, respectively. The seats immediately adjacent to the index cases had an AR of 9.2% (95% CI 5.7–14.4%), with a relative risk 27.8 (95% CI 14.4–53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7%, 95% CI .4%–1.2%). The upper-bound AR increased from 0.7% (95% CI 0.5%–1.0%) to 1.2% (95% CI .4–3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours. Conclusions The ARs among travelers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic.

A total of 55 volatiles including esters (29, 52.73%), alcohols (10, 18.18%), acids (3, 5.45%), alkanes (8, 14.55%), and other components (5, 9.09%) were evaluated in five regions. Total concentrations were 0.05–222.23 mg/L, which covered the highest esters (222.23 mg/L) and alcohols (120.65 mg/L) in Turpan, acid (0.53 mg/L) in Shihezi, and alkanes (1.43 mg/L) and others (3.10 mg/L) in the Ili River valley. It proved that numbers and concentrations of volatile compounds, including common ingredients of variety, were closely linked to ecological characteristics of a region. Esters and alcohols were the major ingredients in Xinjiang Cabernet Sauvignon wine. Additionally, appellation could affect performance of concentration, ODE, and OTH, especially for the same flavor substance by fermentation, aging, and even formation and transformation in wines. Therefore, three conditions for formation of flavors were successively appellations, metabolism and fermentation, and and appropriate altering according to technology and their decisive role in wine quality. Each volatile compound had its own flavor, the combination of which complicated the flavor. The unique materials in the region were grounded for the development of products with corresponding flavors by producing substrate for fermentation. When choosing a wine you enjoy, the right appellation should be considered first.

Previous studies indicate that the incidence of bacillary dysentery is closely related to meteorological factors. However, the impact of temperature and the spatial heterogeneity of the disease in regions of unbalanced socioeconomic development remains unclear. Therefore, this research collected data for 29,639 daily bacillary dysentery cases in children under 5 years of age, as well as the meteorological variables from China's Beijing-Tianjin-Hebei region, to analyze the spatial pattern of bacillary dysentery and reveal its nonlinear association with temperature. The SatScan method was employed first, to detect the spatial heterogeneity of the disease risk, and then the distributed lag nonlinear model (DLNM) was used to analyze the relationships between the daily minimum, mean, and maximum temperatures and bacillary dysentery in the stratified heterogeneous regions. The results indicated that bacillary dysentery incidence presented statistically significant spatial heterogeneity. The area of highest risk was found to be Beijing and its neighboring regions, which have high population densities. There was also a positive association between bacillary dysentery and temperature. Hotter temperatures were accompanied by higher relative risks. In the most likely spatial cluster region, the excess risk (ER) values for a 1°C rise in minimum, mean, and maximum temperatures above the median were 4.65%, 11.30%, and 19.21%, respectively. The effect of temperature on bacillary dysentery peaked at a lag of 3 to 4 days. The findings of this study will aid risk assessments and early warning systems for bacillary dysentery.

Acute lower respiratory infections caused by respiratory viruses are common and persistent infectious diseases worldwide and in China, which have pronounced seasonal patterns. Meteorological factors have important roles in the seasonality of some major viruses. Our aim was to identify the dominant meteorological factors and to model their effects on common respiratory viruses in different regions of China. We analysed monthly virus data on patients from 81 sentinel hospitals in 22 provinces in mainland China from 2009 to 2013. The geographical detector method was used to quantify the explanatory power of each meteorological factor, individually and interacting in pairs. 28369 hospitalised patients with ALRI were tested, 10387 were positive for at least one virus, including RSV, influenza virus, PIV, ADV, hBoV, hCoV and hMPV. RSV and influenza virus had annual peaks in the north and biannual peaks in the south. PIV and hBoV had higher positive rates in the spring summer months. hMPV had an annual peak in winter spring, especially in the north. ADV and hCoV exhibited no clear annual seasonality. Temperature, atmospheric pressure, vapour pressure, and rainfall had most explanatory power on most respiratory viruses in each region. Relative humidity was only dominant in the north, but had no significant explanatory power for most viruses in the south. Hours of sunlight had significant explanatory power for RSV and influenza virus in the north, and for most viruses in the south. Wind speed was the only factor with significant explanatory power for human coronavirus in the south. For all viruses, interactions between any two of the paired factors resulted in enhanced explanatory power, either bivariately or non-linearly., 6 figures and tables

In this article, the problem of receiver autonomous integrity monitoring (RAIM) is transformed into a modeling problem using dynamic data and an artificial neural network. A new RAIM method based on a probabilistic neural network (P-RAIM) is presented to improve integrity monitoring performance. Compared with existing RAIM methods, P-RAIM has a greater ability to meet the monitoring requirements for localizer performance with vertical guidance down to altitudes of 250 feet (LPV-250) in a single global navigation satellite system. First, by projecting the pseudorange error model from the measurement domain into the positioning domain through multiconvolution, patterns including a satellite fault pattern and a fault-free pattern are obtained based on variance inflation theory. Second, the P-RAIM model is proposed as a modified dynamic-data-driven probabilistic neural network with five layers; moreover, unique methods for training sample collection and integrity support are presented. Then, particle swarm optimization is applied to optimize a fitness function based on the false alarm probability and missed detection probability thereby improving the ability of P-RAIM to meet the LPV-250 requirements, including the false alarm probability, missed detection probability, vertical alarm limit and alarm time. Finally, utilizing real satellite data from a receiver located in Beijing to verify the effectiveness and universality of P-RAIM, evaluation experiments show that both the false alarm probability and missed detection probability can be effectively reduced to meet the LPV-250 requirements when the positioning bias is no less than 40 m. Compared with least-squares-residuals RAIM, P-RAIM can more easily detect potential faulty satellites in a single constellation.

Typhoon disasters cause billions of dollars in losses each year, and many countries around the world are adversely affected by these events. Presently, the determinant powers of both natural and socioeconomic factors on disaster losses (such as the house collapse rate following a typhoon), as well as their interaction effects, remain unclear. In this study, the GeoDetector method was used to quantify the impacts of natural and socioeconomic factors and their interactions on the house collapse rate caused by Super Typhoon Lekima in 2019; and then detect the dominant factor, involving in the spatial pattern of house collapses was identified by the local indicators of spatial association (LISA) method. This study found that in addition to natural factors, socioeconomic factors also played a non-negligible role in the house collapse rate caused by Super Typhoon Lekima. The dominant factor was maximum precipitation, and the statistical value of q was 0.39. Next in importance were the elevation and maximum wind speed. Among the interactive effects of 14 influencing factors, the interaction between maximum precipitation and the ratio of four-six floor buildings was the largest (q = 0.74). In southeastern Zhejiang and northern Shandong, highly concentrated areas of ​collapsed houses were found. The results of the study can be used to develop more specific policies aimed at safety improvements and successful property protection.

The Least Squares Residual (LSR) algorithm is commonly used in the Receiver Autonomous Integrity Monitoring (RAIM). However, LSR algorithm presents high Missed Detection Risk (MDR) caused by a large-slope faulty satellite and high False Alert Risk (FAR) caused by a small-slope faulty satellite. In this paper, the LSR algorithm is improved to reduce the MDR for a large-slope faulty satellite and the FAR for a small-slope faulty satellite. Based on the analysis of the vertical critical slope, the optimal decentralized factor is defined and the optimal test statistic is conceived, which can minimize the FAR with the premise that the MDR does not exceed its allowable value of all three directions. To construct a new test statistic approximating to the optimal test statistic, the Optimal Decentralized Factor weighted LSR (ODF-LSR) algorithm is proposed. The new test statistic maintains the sum of pseudo-range residual squares, but the specific pseudo-range residual is weighted with a parameter related to the optimal decentralized factor. The new test statistic has the same decentralized parameter with the optimal test statistic when single faulty satellite exists, and the difference between the expectation of the new test statistic and the optimal test statistic is the minimum when no faulty satellite exists. The performance of the ODF-LSR algorithm is demonstrated by simulation experiments.

Typhoons are an environmental threat that mainly affects coastal regions worldwide. The interactive effects of natural and socioeconomic factors on the losses caused by typhoon disasters need further examination. In this study, GeoDetector was used to quantify the determinant powers of natural and socioeconomic factors and their interactive effects on the rate of house collapse in Guangdong and Guangxi Provinces of southeast China caused by Typhoon Mangkhut in 2018. We further identify the dominant factors that influenced the disaster losses. The local indicators of spatial association method was then introduced to explain the spatial heterogeneity of the disaster losses under the influence of the dominant factor. The results indicate that both natural and socioeconomic factors significantly affected the house collapse rate. The maximum precipitation was the dominant factor, with a q value of 0.21, followed by slope and elevation, with q values of 0.17 and 0.13, respectively. Population density and per capita gross domestic product had q values of 0.15 and 0.13, respectively. Among all of the interactive effects of the influencing factors, the interactive effect of elevation and the ratio of brick-wood houses had the greatest influence (q = 0.63) on the house collapse rate. These results can contribute to the formulation of more specific safety and property protection policies.

In this work, a synthesis of the Bayesian maximum entropy (BME) and the Kalman filter (KF) methods, which enhances their individual strengths and overcomes certain of their weaknesses for spatiotem...

BACKGROUND China has one of the highest tuberculosis (TB) burdens in the world. However, the unbalanced spatial and temporal trends of TB risk at a fine level remain unclear. OBJECTIVE We aimed to investigate the unbalanced risks of pulmonary tuberculosis (PTB) at different levels and how they evolved from both temporal and spatial aspects using PTB notification data from 2851 counties over a decade in China. METHODS County-level notified PTB case data were collected from 2009 to 2018 in mainland China. A Bayesian hierarchical model was constructed to analyze the unbalanced spatiotemporal patterns of PTB notification rates during this period at subnational scales. The Gini coefficient was calculated to assess the inequality of the relative risk (RR) of PTB across counties. RESULTS From 2009 to 2018, the number of notified PTB cases in mainland China decreased from 946,086 to 747,700. The average number of PTB cases in counties was 301 (SD 26) and the overall average notification rate was 60 (SD 6) per 100,000 people. There were obvious regional differences in the RRs for PTB (Gini coefficient 0.32, 95% CI 0.31-0.33). Xinjiang had the highest PTB notification rate, with a multiyear average of 155/100,000 (RR 2.3, 95% CI 1.6-2.8; PPPPPPP CONCLUSIONS PTB risk showed significant regional inequality among counties in China, and western China presented a high plateau of disease burden. Improvements in economic and medical service levels are required to boost PTB case detection and eventually reduce PTB risk in the whole country.

Enterovirus 71 (EV71) can cause large outbreaks of hand, foot, and mouth disease (HFMD) and severe neurological diseases, which is regarded as a major threat to public health, especially in Asia-Pacific regions. However, the global spatiotemporal spread of this virus has not been identified. In this study, we used large sequence datasets and a Bayesian phylogenetic approach to compare the molecular epidemiology and geographical spread patterns of different EV71 subgroups globally. The study found that subgroups of HFMD presented global spatiotemporal variation, subgroups B0, B1, and B2 have caused early infections in Europe and America, and then subgroups C1, C2, C3, and C4 replaced B0-B2 as the predominant genotypes, especially in Asia-Pacific countries. The dispersal patterns of genotype B and subgroup C4 showed the complicated routes in Asia and the source might in some Asian countries, while subgroups C1 and C2 displayed more strongly supported pathways globally, especially in Europe. This study found the predominant subgroup of EV71 and its global spatiotemporal transmission patterns, which may be beneficial to reveal the long-term global spatiotemporal transmission patterns of human EV71 and carry out the HFMD vaccine development.

Background: Previous geographic studies of HIV infection have usually used prevalence data, which cannot indicate the hot-spot areas of current transmission. To develop quantitative analytic measures for accurately identifying hot-spot areas in growth of new HIV infection, we investigated the geographic distribution features of recent HIV infection and long-term HIV infection using data from a whole-population physical examination in four key counties in Liangshan prefecture, which are most severely affected by HIV in China.Methods: Through a whole-population physical examination during November 2017- June 2018 in the four key counties, a total of 5,555 HIV cases were diagnosed and 246 cases were classified as recently infected by laboratory HIV recency tests. The geospatial patterns of recent and long-term HIV infected cases were compared using ordinary least squares regression and Geodetector. Further, geospatial-heterogeneity was quantified and indicated using a residual map to visualize hot-spot areas where new infection is increasing.Results: The geographic location of HIV cases showed an uneven distribution along major roads and clustered at road intersections. The geographic mapping showed that several areas were clustered with more recently infected HIV cases than long-term infected cases. The quantitative analyses showed that the geospatial asymmetry between recent and long-term HIV infection was 0.30 and 0.31 in ordinary least squares regression and Geodetector analysis, respectively. The quantitative analyses found twenty-three townships showing an increase in the number of recent infections.Conclusions: Quantitative analysis of geospatial-heterogeneous areas by comparing between recent and long-term HIV infections allows accurate identification of hot-spot areas where new infections are expanding, which can be used as a potent methodological tool to guide targeted interventions and curb the spread of the epidemic.

When a marine study area is large, the environmental variables often present spatially stratified non-homogeneity, violating the spatial second-order stationary assumption. The stratified non-homog...

The Huai River basin (located in eastern China) has a population of 180 million and has the highest risk of esophageal cancer (EC) mortality in China. Some studies found that contaminants in drinking water are a major risk factor for cancers of the digestive system. However, the effect of water pollution in the historical period on the current EC mortality remains unclear. Data were collected on the EC mortality rate in 2004 in the Huai River basin in 11 counties, and data on the surface water quality in the region from 1987 to 2004 were used. The Pearson correlation and the GeoDetector q-statistic were employed to explore the association between water pollution and the EC mortality rate in different lag periods, from linear and nonlinear perspectives, respectively. The study showed apparently spatial heterogeneity of the EC mortality rate in the region. The EC mortality rate downstream is significantly higher than that in other regions; in the midstream, the region north of the mainstream has a lower average mortality rate than that south of the area. Upstream, the region north of the mainstream has a higher mortality rate than that in the southern area. The spatial pattern was formed under the influence of water pollution in the historical period. 1996, 1997, and 1998 have the strongest linear or nonlinear effect on the EC mortality rate in 2004, in which the Pearson correlation coefficient and the q-statistic were the highest, 0.79 and 0.89, respectively. Rapid industrialization in the past 20 years has caused environmental problems and poses related health risks. The study indicated that the current EC mortality rate was mainly caused by water pollution from the previous 8 years. The findings provide knowledge about the lag time for pollution effects on the EC mortality rate, and can contribute to the controlling and preventing esophageal cancer.

In the last decade, extensive application of hydraulic fracturing technologies to unconventional low-permeability hydrocarbon-rich formations has significantly increased natural-gas production in the United States and abroad. The injection of surface-sourced fluids to generate fractures in the deep subsurface introduces microbial cells and substrates to low-permeability rock. A subset of injected organic additives has been investigated for their ability to support biological growth in shale microbial community members; however, to date, little is known on how complex xenobiotic organic compounds undergo biotransformations in this deep rock ecosystem. Here, high-resolution chemical, metagenomic, and proteomic analyses reveal that widely-used surfactants are degraded by the shale-associated taxa Halanaerobium, both in situ and under laboratory conditions. These halotolerant bacteria exhibit surfactant substrate specificities, preferring polymeric propoxylated glycols (PPGs) and longer alkyl polyethoxylates (AEOs) over polyethylene glycols (PEGs) and shorter AEOs. Enzymatic transformation occurs through repeated terminal-end polyglycol chain shortening during co-metabolic growth through the methylglyoxal bypass. This work provides the first evidence that shale microorganisms can transform xenobiotic surfactants in fracture fluid formulations, potentially affecting the efficiency of hydrocarbon recovery, and demonstrating an important association between injected substrates and microbial growth in an engineered subsurface ecosystem.

Hand, foot and mouth disease (HFMD) remains an increasing public health concern. The spatiotemporal variation of HFMD can be represented from multiple-perspectives, and it may be driven by different dominant factors. In this study, the HFMD cases in children under the age of five years in each county in Henan province, China, from 2009 to 2013 were assessed to explore the integrative spatiotemporal patterns of HFMD and investigate their driving factors. The empirical orthogonal function was applied to identify representative spatiotemporal patterns. Then, GeoDetector was used to quantify the determinant powers of driving factors to the disease. The results indicated that the most prominent spatiotemporal pattern explained 56.21% of the total variance, presented in big cities, e.g. capital city and municipal districts. The dominant factors of this pattern were per capita gross domestic product and relative humidity, with determinant powers of 62% and 42%, respectively. The secondary spatiotemporal pattern explained 10.52% of the total variance, presented in the counties around big cities. The dominant factors for this pattern were the ratio of urban to rural population and precipitation, with determinant powers of 26% and 41%, respectively. These findings unveiled the key spatiotemporal features and their determinants related to the disease; this will be helpful in establishing accurate spatiotemporal preventing of HFMD.

To better understand the physiological processes involved in responses of trellises to the transportation and distribution of nutrient within shoots of grapevines, analysis method of disodium ethylenediamine tetraacetate (EDTA - Na2) was employed at the whole development stages of vine. In the trial, the traditional independent long-stem pruning (ILSP) was taken as control to compare the effect of new single crawled cordon training (SCCT) on the transportation and distribution of phloem sap on the experimental material Ecolly. Thus the consumption level of phloem sap in the grapevine shoots of various trellises was analyzed by EDTA-Na2 phloem sap exudation technique during the whole development. Results was that the consumption rate of EDTA - Na2 solution in the SCCT and ILSP shoots increased before August 15 (15.130 g–16.216 g in SCCT and 10.653 g–15.042 g in ILSP, respectively) including 16.348–20.809 g in SCCT and 11.912–19.593 g in ILSP from August 25 to September 4 as well as decreased in August 15–25 (17.216–16.348 g in SCCT and 15.042–11.912 g in ILSP, respectively) and after September 4 (20.809–7.669 g in SCCT and 19.593–4.072 g in ILSP), respectively, and values of SCCT were always higher than that of ILSP during the whole growth. In addition, EDTA - Na2 consumption content in the phloem sap per shoots in SCCT kept almost around 16 g, more than that in ILSP, from 21 July to 25 August. Therefore, SCCT was more advantageous to accumulate nutrient compared with ILSP, and ensured an even distribution of phloem sap in the grape shoots from berry growth to maturity.

Uneven spatial distribution of population and human activity still remain an inevitable problem for China's urbanization strategy and regional policies. The geographical detector technique was employed to re-investigated “Hu Line”, an important population dividing line in China, and impact factors of population distribution from 1953 to 2010. Different from traditional views, we believe that rapid population concentration driven by social and economic factors leads to increasing similar spatial patterns between two sides of the “Hu Line”. This trend offers a chance to realize a more balanced development by focusing on small-scale population concentration areas such as urban agglomerations. However, due to fundamental influence of some natural factors, gap among large-scale regions hard to be eliminated in China.

Landslides are destructive not only to property and infrastructure but also to people living in landslide-prone regions. Landslide susceptibility mapping (LSM) is critical for preventing and mitigating the negative impacts of landslides. However, many previously proposed LSM modeling techniques included only the attribute information of spatial objects and ignored the spatial structural information of spatial objects, which led to suboptimal LSM. In addition, the selection of condition factors was not objective to such an extent that it may have reduced the reliability of LSM. To address these problems, a new method based on GeoDetector and a spatial logistic regression (SLR) model is proposed. GeoDetector is used to select condition factors based on the spatial distribution of landslides. The SLR model is used to make full use of the structural and attribute information of spatial objects simultaneously in LSM. The GeoDetector-SLR model is validated using data from the Duwen Highway Basin, which includes the epicenter of the May 12, 2008 Wenchuan earthquake in southwestern China. Prediction accuracy of the GeoDetector-SLR model is found to be 86.1%, which is an 11.9% improvement over the traditional logistic regression model, indicating an improved and reliable solution for evaluating landslide susceptibility.

Most previous studies have indicated inconsistent relationships between rice cadmium (Cd) and the soil properties of paddy fields at a regional scale under the adverse effects of confounding factors and spatial heterogeneity. In order to reduce these effects, this study integrates Geodetector, a stepwise regression model, and a hierarchical Bayesian method (collectively called GDSH). The GDSH framework is validated in a large typical rice production area in southeastern China. According to the results, significant stratified heterogeneity of the bioaccumulation factor is observed among different subregions and pH strata (q = 0.23, p 0.01). Additionally, the soil-rice relationships and dominant factors vary by the subregions, and the available soil Cd and pH are found to be the dominant factors in 64% and 50% of subregions, respectively. In the entire region, when the pH 6, the dominant factors are organic matter and available Cd, and when pH ≥ 6 they are organic matter, pH, and available Cd. Furthermore, these factors presented different sensitivity to the spatial heterogeneity. The results indicate that, at the subregional level, the GDSH framework can reduce the confounding effects and accurately identify the dominant factors of rice Cd. At the regional level, this model can evaluate the sensitivity of the dominant factors to spatial heterogeneity in a large area. This study provides a new scheme for the complete utilization of regional field survey data, which is conducive to formulating precise pollution control strategies.

Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here, we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exometabolomics analysis in sample matrices containing up to 2 M total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and it can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5 to 6 orders of magnitude) with excellent linearity, and low median interday reproducibility (e.g., 2.6%). MetFish was successfully applied in targeted and untargeted exometabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exometabolome during community succession; in situ in a native prairie soil, whose exometabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exometabolome over time in the well. IMPORTANCE The identification and accurate quantification of metabolites using electrospray ionization-mass spectrometry (ESI-MS) in hypersaline samples is a challenge due to matrix effects. Clean-up and desalting strategies that typically work well for samples with lower salt concentrations are often ineffective in hypersaline samples. To address this gap, we developed and demonstrated a simple yet sensitive and accurate method—MetFish—using chemical derivatization to enable mass spectrometry-based metabolomics in a variety of hypersaline samples from varied ecosystems and containing up to 2 M dissolved salts.

Background Typhoons greatly threaten human life and property, especially in China. Therefore, it is important to make effective policy decisions to minimize losses associated with typhoons. Methods In this study, the GeoDetector method was used to quantify the determinant powers of natural and socioeconomic factors, and their interactions, on the population casualty rate of super typhoon Lekima. The local indicator of spatial association (LISA) method was followed to explore the spatial pattern of the population casualty rate under the influence of the identified dominant factors. Results Both natural and socioeconomic factors were found to have significantly impacted the population casualty rate due to super typhoon Lekima. Among the selected factors, maximum precipitation was dominant factor (q = 0.56), followed by maximum wind speed (q = 0.45). In addition, number of health technicians (q = 0.35) and number of health beds (q = 0.27) have a strong influence on the population casualty rate. Among the interactive effects of 12 influencing factors, the combined effects of maximum precipitation and ratio of brick-wood houses, the maximum precipitation and ratio of steel-concrete houses, maximum precipitation and number of health technicians were highest (q = 0.72). Furthermore, high-risk areas with very high casualty rates were concentrated in the southeastern part of Zhejiang and northern Shandong Provinces, while lower-risk areas were mainly distributed in northern Liaoning and eastern Jiangsu provinces. Conclusions These results contribute to the development of more specific policies aimed at safety and successful property protection according to the regional differences during typhoons.

Background Previous studies have indicated that the risk of infectious disease spread is greatest in locations where a population has massive and convenient access to the epicenter of an outbreak. However, the spatiotemporal variations and risk determinants of COVID-19 in typical labor export regions of China remain unclear. Understanding the geographical distribution of the disease and the socio-economic factors affecting its transmission is critical for disease prevention and control. Methods A total of 2152 COVID-19 cases were reported from January 21 to February 24, 2020 across the 34 cities in Henan and Anhui. A Bayesian spatiotemporal hierarchy model was used to detect the spatiotemporal variations of the risk posed by COVID-19, and the GeoDetector q statistic was used to evaluate the determinant power of the potential influence factors. Results The risk posed by COVID-19 showed geographical spatiotemporal heterogeneity. Temporally, there was an outbreak period and control period. Spatially, there were high-risk regions and low-risk regions. The high-risk regions were mainly in the southwest areas adjacent to Hubei and cities that served as economic and traffic hubs, while the low-risk regions were mainly in western Henan and eastern Anhui, far away from the epicenter. The accessibility, local economic conditions, and medical infrastructure of Wuhan in Hubei province all played an important role in the spatiotemporal heterogeneity of COVID-19 transmission. The results indicated that the q statistics of the per capita GDP and the proportion of primary industry GDP were 0.47 and 0.47, respectively. The q statistic of the population flow from Wuhan was 0.33. In particular, the results showed that the q statistics for the interaction effects between population density and urbanization, population flow from Wuhan, per capita GDP, and the number of doctors were all greater than 0.8. Conclusions COVID-19 showed significant spatiotemporal heterogeneity in the labor export regions of China. The high-risk regions were mainly located in areas adjacent to the epicenter as well as in big cities that served as traffic hubs. Population access to the epicenter, as well as local economic and medical conditions, played an important role in the interactive effects of the disease transmission.

Additional file 1. The relationship between COVID-19 risk and the socioeconomic risk factors.

Background: Although significant correlations have been observed between air pollutants and the development of pulmonary tuberculosis (PTB) in many developed countries, data are scarce for developing and highly polluted regions. Method: A combined Poisson generalized linear regression–distributed lag nonlinear model was used to determine the associations between long-term exposure (2005–2017) to air pollutants and the risk of PTB in the Beijing–Tianjin–Hebei region. Results: The monthly PTB cases exhibited a fluctuating downward trend. For each 10 μg/m3 increase in concentration, the maximum lag-specific risk and cumulative relative risk (RR) were 1.011 (95% confidence interval (CI): 1.0091.012, lag: 3 months) and 1.042 (1.036–1.048, 5 months) for PM2.5, and 1.023 (1.015–1.031, 0 months) and 1.041 (1.026–1.055, 2 months) for NO2. The risk of PTB was negatively correlated with O3 exposure, and the minimum lag-specific risk and cumulative RR were 0.991 (95% CI: 0.987–0.994, lag: 0 months) and 0.974 (0.968–0.981, 4 months), respectively. No age-dependent effects were observed. Conclusions: Our results revealed potential associations between outdoor exposure to PM2.5, NO2, and O3 and the risk of PTB. Further research should explore the corresponding interactions and potential mechanisms.

Background: Previous studies had indicated that the incidence of Tuberculosis (TB) was closed related to the regional aging population and socio-economic condition. However, spatio-temporal variation and risk determinants of TB in aging population and socio-economic development unbalanced region have been unclear.Methods: The data on TB registered cases and social-economic factors from 2009 to 2014 were collected for each district/county in the region with Beijing, Tianjin and Hebei province of China, which is consist of aging population and social-economic development disparity region. The Bayesian space–time hierarchy model was used to reveal the spatio-temporal variation of elderly TB incidence from 2009 to 2014, and GeoDetector was applied to measure the determinant power (q statistic) of elderly TB risk factors.Results: Elderly TB incidence present geographical spatial heterogeneity, they were higher in underdeveloped rural areas compared with that in urban areas. The hotspots of elderly TB incidence risk were mostly located in the northeastern and southern regions of the study region distant from metropolitan areas. Areas with low risk were mainly distributed in the Beijing-Tianjin metropolitan areas. Social-economic factors presented nonlinear influence on elderly TB incidence, the dominant factors were income of rural populations (q statistic = 0.20) and medical conditions (q statistic = 0.17). These factors presented nonlinearly interact with each other in influencing the elderly TB incidence; medical conditions and level of economic development showed the strongest relationship (q statistic = 0.54).Conclusions: These findings explain spatiotemporal variation and risk determinants of elderly TB incidence where there are social-economic development disparities. High-risk zones were mainly located in rural areas far from metropolitan centers. Medical conditions and economic development level were significantly association with elderly TB incidence, and they showed nonlinearly interact with each other in influencing the elderly TB incidence. Knowing this helps to optimize the allocation of health resources and to control the transmission of the TB epidemic in the aging population in this region.

Vegetation productivity dynamics are closely related to climate change, and water availability determines vegetation growth in water-limited ecosystems. Nevertheless, how changes in the interactions between climatic factors and vegetation activity variation regulate the relationship between their trends remains unclear. The Normalized Difference Vegetation Index (NDVI) is an effective proxy of vegetation growth. First, we investigated the NDVI trends, and the results revealed a vegetation activity with weaker greening and greater spatial heterogeneity after an obvious land-cover breakpoint in 1999 compared with that before 1999 in northwest China. Notably, the Loess Plateau greatly led the greenness trends, but the Tibet Plateau showed mean browning after 1999, which implied that the coupling of climate change and vegetation trends varied with spatio-temporal changes. Subsequently, using the Geographical Detector Method (GDM), we quantified and compared the association between climate change and the interannual variability of NDVI in the two stages. Vegetation productivity variation is more closely related to changes in climatic factors after 1999 compared with that before 1999. Precipitation (PPT) and vapor pressure deficit (VPD) are the primary constraints to vegetation growth in both stages. Patterns in NDVI trend increases are consistent with those of increased PPT and decreased VPD and vice versa after 1999. However, the same patterns were not observed before 1999 because of the weak association between climate change and NDVI variation. This implicated a great significance of the association between climate change and changes in vegetation activity for the prediction of potential carbon sequestration due to the shift of dominant factors and their trends under future climate change.

Background Previous research pointed to a close relationship between the incidence of tuberculosis (TB) in aging populations and socio-economic conditions, however there has been lack of studies focused on a region of unbalanced socio-economic development. The aim of this paper is to explore the spatio-temporal variation in TB incidence and examine risk determinants of the disease among aging populations in a typical region. Methods Data on TB-registered cases between 2009 and 2014, in addition to social-economic factors, were collected for each district/county in Beijing, Tianjin and Hebei, a region characterized by an aging population and disparities in social-economic development. A Bayesian space–time hierarchy model (BSTHM) was used to reveal spatio-temporal variation in the incidence of TB among the elderly in this region between 2009 to 2014. GeoDetector was applied to measure the determinant power (q statistic) of risk factors for TB among the elderly. Results The incidence of TB among the elderly exhibited geographical spatial heterogeneity, with a higher incidence in underdeveloped rural areas compared with that in urban areas. Hotspots of TB incidence risk among the elderly were mostly located in north-eastern and southern areas in the study region, far from metropolitan areas. Areas with low risk were distributed mainly in the Beijing-Tianjin metropolitan areas. Social-economic factors had a non-linear influence on elderly TB incidence, with the dominant factors among rural populations being income (q = 0.20) and medical conditions (q = 0.17). These factors had a non-linear interactive effect on the incidence of TB among the elderly, with medical conditions and the level of economic development having the strongest effect (q = 0.54). Conclusions The findings explain spatio-temporal variation in TB incidence and risk determinants of elderly TB in the presence of disparities in social-economic development. High-risk zones were located mainly in rural areas, far from metropolitan centres. Medical conditions and the economic development level were significantly associated with elderly TB incidence, and these factors had a non-linear interactive effect on elderly TB incidence. The findings can help to optimize the allocation of health resources and to control TB transmission in the aging population in this region.

Background Identifying young individuals living with human immunodeficiency virus (HIV) who are unaware of their status is a major challenge for HIV control in China. To address this, an innovative, anonymous vending machine–based urine self-collection for HIV testing (USCT) program was implemented in 2016 in colleges across China. Methods From June 2016 to December 2019, 146 vending machines stocked with urine self-collection kits were distributed on 73 college campuses across 11 provinces of China. Urine samples were collected, delivered, and tested in an anonymous manner. We analyzed the returned rate, reactive rate (likelihood of HIV screening positive), testing effectiveness (the annual number of college students living with HIV screened by USCT or other testing methods), and the spatiotemporal relationship between USCT usage and student activity per college generated from the usage of a social networking application. Results Among the 5178 kits sold, 3109 (60%) samples were returned; of these, 2933 (94%) were eligible for testing. The HIV reactive rate was 2.3% (66 of 2933). The average effectiveness ratio among the 34 participating Beijing colleges was 0.39 (12:31) between USCT and conventional testing methods. A strong spatiotemporal correlation between USCT numbers and online student activity was observed during school semesters in Beijing. Conclusions USCT is a powerful complement to current interventions that target at-risk students and promote HIV testing. The social networking–based evaluation framework can be a guide in prioritizing at-risk target populations.

Background Train travel is a common mode of public transport across the globe; however, the risk of coronavirus disease 2019 (COVID-19) transmission among individual train passengers remains unclear. Methods We quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2334 index patients and 72 093 close contacts who had co-travel times of 0–8 hours from 19 December 2019 through 6 March 2020 in China. We analyzed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time. Results The attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI], 5.3%–19.0%), with a mean of 0.32% (95% CI, .29%–.37%). Passengers in seats on the same row (including the adjacent passengers to the index patient) as the index patient had an average attack rate of 1.5% (95% CI, 1.3%–1.8%), higher than that in other rows (0.14% [95% CI, .11%–.17%]), with a relative risk (RR) of 11.2 (95% CI, 8.6–14.6). Travelers adjacent to the index patient had the highest attack rate (3.5% [95% CI, 2.9%–4.3%]) of COVID-19 infection (RR, 18.0 [95% CI, 13.9–23.4]) among all seats. The attack rate decreased with increasing distance, but increased with increasing co-travel time. The attack rate increased on average by 0.15% (P = .005) per hour of co-travel; for passengers in adjacent seats, this increase was 1.3% (P = .008), the highest among all seats considered. Conclusions COVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when traveling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.

Previous studies indicate that the incidence of bacillary dysentery is closely related to meteorological factors. However, the impact of temperature and the spatial heterogeneity of the disease in regions of unbalanced socioeconomic development remains unclear. Therefore, this research collected data for 29,639 daily bacillary dysentery cases in children under 5 years of age, as well as the meteorological variables from China's Beijing-Tianjin-Hebei region, to analyze the spatial pattern of bacillary dysentery and reveal its nonlinear association with temperature. The SatScan method was employed first, to detect the spatial heterogeneity of the disease risk, and then the distributed lag nonlinear model (DLNM) was used to analyze the relationships between the daily minimum, mean, and maximum temperatures and bacillary dysentery in the stratified heterogeneous regions. The results indicated that bacillary dysentery incidence presented statistically significant spatial heterogeneity. The area of highest risk was found to be Beijing and its neighboring regions, which have high population densities. There was also a positive association between bacillary dysentery and temperature. Hotter temperatures were accompanied by higher relative risks. In the most likely spatial cluster region, the excess risk (ER) values for a 1°C rise in minimum, mean, and maximum temperatures above the median were 4.65%, 11.30%, and 19.21%, respectively. The effect of temperature on bacillary dysentery peaked at a lag of 3 to 4 days. The findings of this study will aid risk assessments and early warning systems for bacillary dysentery.

Aboveground biomass (AGB) estimates at the plot level plays a major part in connecting accurate single-tree AGB measurements to relatively difficult regional-scale AGB estimates. However, complex and spatially heterogeneous landscapes, where multiple environmental covariates (such as longitude, latitude, and forest structure) affect the spatial distribution of AGB, make upscaling of plot-level models more challenging. To address this challenge, this study proposes an approach that combines machine learning with spatial statistics to construct a more accurate plot-level AGB model. The study was conducted in a Eucalyptus plantation in Nanjing, China. We developed, evaluated, and compared the accuracy and performance of three different machine learning models [support vector machine (SVM), random forest (RF), and the radial basis function artificial neural network (RBF-ANN)], one spatial statistics model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, RBF-ANN & P-BSHADE) for forest AGB estimates based on AGB data from 30 sample plots and their corresponding environmental covariates. The results show that the performance indices RMSE, nRMSE, MAE, and MRE of all combined models are substantially smaller than those of any individual models, with the RF & P-BSHADE combined method giving the smallest value. These results demonstrate clearly that combined models, especially the RF & P-BSHADE model, can improve the accuracy of plot-level AGB models and reduce uncertainty on plot-level AGB estimates or even on large-forested-landscape AGB estimates. These research results are important because they reduce the uncertainty in estimates of the regional carbon balance.