Spatio-temporal characterization of earthquake sequence parameters and forecasting of strong aftershocks in Xinjiang based on the ETAS model.

In this paper, the Integrated Nested Laplace Algorithm (INLA) is applied to the Epidemic Type Aftershock Sequence (ETAS) model, and the parameters of the ETAS model are obtained for the earthquake sequences active in different regions of Xinjiang. By analyzing the characteristics of the model parameters over time, the changes in each earthquake sequence are studied in more detail. The estimated values of the ETAS model parameters are used as inputs to forecast strong aftershocks in the next period. We find that there are significant differences in the aftershock triggering capacity and aftershock attenuation capacity of earthquake sequences in different seismic regions of Xinjiang. With different cutoff dates set, we observe the characteristics of the earthquake sequence parameters changing with time after the mainshock occurs, and the model parameters of the Ms7.3 earthquake sequence in Hotan region change significantly with time within 15 days after the earthquake. Compared with the MCMC algorithm, the ETAS model fitted with the INLA algorithm can forecast the number of earthquakes in the early period after the occurrence of strong aftershocks more effectively and can forecast the sudden occurrence time of earthquakes more accurately.

Prediction of outpatients with conjunctivitis in Xinjiang based on LSTM and GRU models.

BACKGROUND: Reasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity. METHODS: Based on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction. RESULTS: In predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99. CONCLUSIONS: The GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management.

Spatio-temporal distribution characteristics of the risk of viral hepatitis B incidence based on INLA in 14 prefectures of Xinjiang from 2004 to 2019.

This study aimed to explore the spatio-temporal distribution characteristics and risk factors of hepatitis B (HB) in 14 prefectures of Xinjiang, China, and to provide a relevant reference basis for the prevention and treatment of HB. Based on HB incidence data and risk factor indicators in 14 prefectures in Xinjiang from 2004 to 2019, we explored the distribution characteristics of the risk of HB incidence using global trend analysis and spatial autocorrelation analysis and established a Bayesian spatiotemporal model to identify the risk factors of HB and their spatio-temporal distribution to fit and extrapolate the Bayesian spatiotemporal model using the Integrated Nested Laplace Approximation (INLA) method. There was spatial autocorrelation in the risk of HB and an overall increasing trend from west to east and north to south. The natural growth rate, per capita GDP, number of students, and number of hospital beds per 10, 000 people were all significantly associated with the risk of HB incidence. From 2004 to 2019, the risk of HB increased annually in 14 prefectures in Xinjiang, with Changji Hui Autonomous Prefecture, Urumqi City, Karamay City, and Bayangol Mongol Autonomous Prefecture having the highest rates.

Spatial age-period-cohort analysis of hepatitis B risk in Xinjiang from 2006 to 2019.

Objective: The objective of this study was to investigate the spatio-temporal distribution and epidemiological characteristics of hepatitis B in 96 districts and counties of Xinjiang and to give useful information for hepatitis B prevention and treatment. Methods: Based on the incidence data of hepatitis B in 96 districts and counties of Xinjiang from 2006 to 2019, the global trend analysis method was used to characterize the spatial variability of the disease, and the spatial autocorrelation and spatio-temporal aggregation analysis were used to explore the spatial clustering of hepatitis B and to identify high-risk areas and periods. The Integrated Nested Laplace Approximation (INLA)-based spatial age-period-cohort model was established to further explore the influence of age, period, birth queue effect, and spatial distribution on the incidence risk of hepatitis B, and sum-to-zero constraint was adopted to avoid the issue of model unrecognition. Results: The risk of hepatitis B in Xinjiang is increasing from west to east and from north to south, with spatial heterogeneity and spatio-temporal scanning statistics yielding five clustering areas. The spatial age-period-cohort model showed two peaks in the average risk of hepatitis B, at [25,30) years old and [50,55) years old, respectively. The mean risk of hepatitis B incidence fluctuated up and down around 1 with time, and the average risk of disease by birth cohort displayed an increasing-decreasing-stabilizing trend. Taking age, period, and cohort effect into consideration, it was found that the areas with a high risk of hepatitis B are Tianshan District, Xinshi District, Shuimogou District, Changji City, Aksu City, Kashi City, Korla City, Qiemo County and Yopurga County in Xinjiang. According to the spatio-temporal effect item, it was found that there are unobserved variables affecting the incidence of hepatitis B in some districts and counties of Xinjiang. Conclusion: The spatio-temporal characteristics of hepatitis B and the high-risk population needed to be taken into attention. It is suggested that the relevant disease prevention and control centers should strengthen the prevention and control of hepatitis B among young people while paying attention to middle-aged and older adult people, and strengthening the prevention and monitoring of high-risk areas.

MCMCINLA estimation of varying coefficient spatial lag model-A study of China's economic development in the context of population aging.

The dominant spatial econometric model in spatial econometrics is the parametric form, while in the realistic context, the variables often do not satisfy the assumption of linearity and have nonlinear relationships with each other. In this paper, we introduce nonparametric terms into spatial econometric models and propose the MCMCINLA estimation method for varying coefficient spatial lag models. The empirical analysis is conducted with the socioeconomic data of mainland China from 2015 to 2020 to discuss the influencing factors and spatial and temporal distribution characteristics of China's economic development under the classical spatial lag model and the varying coefficient spatial lag model with population aging as a special covariate, respectively. The results show that with the gradual aging of the population, foreign trade will inhibit the development of regional economy to a certain extent, while urbanization process, resident income, real estate development and high-tech development will have a driving effect on economic growth, and high-tech development has the strongest mobilization on regional economic development. Compared with the classical spatial lag model, the varying coefficient spatial lag model can more fully exploit the information of variables in a more realistic context and derive the variable evolution process.

Analysis of air pollution in Fenwei Plain in China based on functional spatial autoregressive combined model.

The Fenwei Plain is listed as one of the most serious air pollution regions in China, along with Beijing-Tianjin-Hebei and Yangtze River Delta regions. This paper proposed a functional data analysis method to study the environmental pollution problem in the Fenwei Plain of China. Functional spatial autoregressive combined (FSAC) model with spatial autocorrelation of both the response variable and error term is developed. The model takes the SO2 concentration of Fenwei Plain as the dependent variable and the dew point temperature as the independent variable and realizes the maximum likelihood estimation using functional principal component analysis to obtain the asymptotic properties of parameter estimation and the confidence interval of the slope function. According to the findings of the empirical analysis of the Fenwei Plain, the SO2 concentration has significant seasonal characteristics and has decreased year over year for three years in a row. Winter is the season with the highest concentration on the Fenwei Plain, followed by spring and autumn, while summer is the season with the lowest concentration. Winter also has a high spatial autocorrelation. The FSAC model is more effective at fitting the concentration and dew point temperature of the Fenwei Plain in China because its mean square error (MSE) is significantly lower than that of the other models. As a result, this paper can more thoroughly study the pollution problem on the Fenwei Plain and offer guidance for prevention and control.

Bayesian spatiotemporal modelling analysis of hemorrhagic fever with renal syndrome outbreaks in China using R-INLA.

Hemorrhagic fever with renal syndrome (HFRS) is a category B infectious disease caused by Hantavirus infection, which can cause acute kidney injury and has a high mortality rate. At present, China is the country most severely afflicted by HFRS in the world, and it is critical to carry out efficient HFRS prevention and management in a scientific and accurate manner. The study used data on the incidence of HFRS in mainland China from 2015 to 2018, built a Bayesian hierarchical spatiotemporal distribution model, and applied the Integrated Nested Laplace Approximation algorithm to analyse the factors influencing the development of HFRS, the spatial and temporal distribution characteristics, and the threshold exceedance locations. The results revealed that the woodland and grassland area (RR = 1.357, 95% CI: 1.005-1.791), economic level (RR = 1.299, 95% CI: 1.007-1.649), and traffic level (RR = 2.442, 95% CI: 1.825-3.199) were all significantly and positively associated with the development of HFRS, with traffic level having the strongest promoting effect. The seasonal cycle was obvious in time, with peaks in May-June and October-December each year, most notably in November. Spatially, there was a south-heavy north-light trend, with a high risk of incidence largely in places rich in mountain and forest vegetation, of which Guizhou, Guangxi, Guangdong, and Jiangxi provinces continuing to have a high incidence in recent years, and the evolution of the epidemic in Hubei and Hunan was becoming more serious. When the early warning threshold was set at 0.2, the detection impact was best, and Guizhou, Guangxi, Guangdong, Jiangxi, Hainan, and Tianjin were positioned near the critical point of the exceedance threshold with the highest risk of incidence. It is recommended that the relevant managers call for active vaccination of outdoor workers, such as those working in agriculture and construction sites, implement rat prevention and extermination before winter arrives, and warn high-risk and medium-high-risk areas to conduct early outbreak surveillance. Move the prevention and control gates forward based on the exceedance threshold for doing preventive and control detection and epidemic research and judgement work.

Analysis of spatial and temporal impact differences of birth rate in mainland China.

The continuing decline in the birth rate has led to a series of problems, such as the disproportion of population structure and severe aging population, which have restricted the country's economic development. To have a deeper understanding of the geographical differences and influencing factors of the birth rate, this paper collects and organizes the birth population data of 31 provinces in mainland China from 2011 to 2019. The national region is divided into seven natural geographical regions to obtain the spatial hierarchy, and a hierarchical Bayesian Spatio-temporal model is established. The INLA algorithm estimates the model parameters. The results show significant spatial and temporal differences in birth rates in mainland China, which are reflected mainly in the combination of spatial, temporal, and Spatio-temporal interaction effects. In the spatial dimension, the northeast is low, the northwest and southwest are high, and the birth rate has an upward trend from east to west. These trends are caused by unbalanced economic development, different fertility attitudes and differences in fertility security, reflecting regional differences in spatial effects. From 2011 to 2019, China's birth rate showed an overall downward trend in the time dimension. However, all regions except the northeast saw a significant but temporary increase in birth rates in 2016 and 2017, reflecting the temporal effect difference in birth rates.

MCMCINLA Estimation of Missing Data and Its Application to Public Health Development in China in the Post-Epidemic Era.

Medical data are often missing during epidemiological surveys and clinical trials. In this paper, we propose the MCMCINLA estimation method to account for missing data. We introduce a new latent class into the spatial lag model (SLM) and use a conditional autoregressive specification (CAR) spatial model-based approach to impute missing values, making the model fit into the integrated nested Laplace approximation (INLA) framework. Combining the advantages of both the Markov chain Monte Carlo (MCMC) and INLA frameworks, the MCMCINLA algorithm is used to implement imputation of the missing data and fit the model to derive estimates of the parameters from the posterior margins. Finally, the economic data and the hemorrhagic fever with renal syndrome (HFRS) disease data of mainland China from 2016-2018 are used as examples to explore the development of public health in China in the post-epidemic era. The results show that compared with expectation maximization (EM) and full information maximum likelihood estimation (FIML), the predicted values of the missing data obtained using our method are closer to the true values, and the spatial distribution of HFRS in China can be inferred from the imputation results with a southern-heavy and northern-light distribution. It can provide some references for the development of public health in China in the post-epidemic era.

Analysis on the characteristics of spatio-temporal evolution and aggregation trend of early COVID-19 in mainland China.

To analyze the spatio-temporal aggregation of COVID-19 in mainland China within 20 days after the closure of Wuhan city, and provide a theoretical basis for formulating scientific prevention measures in similar major public health events in the future. Draw a distribution map of the cumulative number of COVID-19 by inverse distance weighted interpolation; analyze the spatio-temporal characteristics of the daily number of COVID-19 in mainland China by spatio-temporal autocorrelation analysis; use the spatio-temporal scanning statistics to detect the spatio-temporal clustering area of the daily number of new diagnosed cases. The cumulative number of diagnosed cases obeyed the characteristics of geographical proximity and network proximity to Hubei. Hubei and its neighboring provinces were most affected, and the impact in the eastern China was more dramatic than the impact in the western; the global spatio-temporal Moran's I index showed an overall downward trend. Since the 10th day of the closure of Wuhan, the epidemic in China had been under effective control, and more provinces had shifted into low-incidence areas. The number of new diagnosed cases had gradually decreased, showing a random distribution in time and space (P< 0.1), and no clusters were formed. Conclusion: the spread of COVID-19 had obvious spatial-temporal aggregation. China's experience shows that isolation city strategy can greatly contain the spread of the COVID-19 epidemic.

Spatiotemporal characters and influence factors of hand, foot and mouth epidemic in Xinjiang, China.

Hand, foot and mouth (HFM) disease is a common childhood illness. The paper aims to capture the spatiotemporal characters, and investigate the influence factors of the HFM epidemic in 15 regions of Xinjiang province from 2008 to 2017, China. Descriptive statistical analysis shows that the children aged 0-5 years have a higher HFM incidence, mostly boys. The male-female ratio is 1.5:1. Through the scanning method, we obtain the first cluster high-risk areas. The cluster time is usually from May to August every year. A spatiotemporal model is proposed to analyze the impact of meteorological factors on HFM disease. Comparing with the spatial model, the model is more effective in terms of R2, AIC, deviation, and mean-square error. Among meteorological factors, the number of HFM cases generally increases with the intensity of rainfall. As the temperature increases, there are more HFM patients. Some regions are mostly influenced by wind speed. Further, another spatiotemporal model is introduced to investigate the relationship between HFM disease and socioeconomic factors. The results show that socioeconomic factors have significant influence on the disease. In most areas, the risk of HFM disease tends to rise with the increase of the gross domestic product, the ratios of urban population and tertiary industry. The incidence is closely related to the number of beds and population density in some regions. The higher the ratio of primary school, the lower the number of HFM cases. Based on the above analysis, it is the key measure to prevent and control the spread of the HFM epidemic in high-risk areas, and influence factors should not be ignored.

Influential factors and spatial-temporal distribution of tuberculosis in mainland China.

Tuberculosis (TB) is an infectious disease that threatens human safety. Mainland China is an area with a high incidence of tuberculosis, and the task of tuberculosis prevention and treatment is arduous. This paper aims to study the impact of seven influencing factors and spatial-temporal distribution of the relative risk (RR) of tuberculosis in mainland China using the spatial-temporal distribution model and INLA algorithm. The relative risks and confidence intervals (CI) corresponding to average relative humidity, monthly average precipitation, monthly average sunshine duration and monthly per capita GDP were 1.018 (95% CI 1.001-1.034), 1.014 (95% CI 1.006-1.023), 1.026 (95% CI 1.014-1.039) and 1.025 (95% CI 1.011-1.040). The relative risk for average temperature and pressure were 0.956 (95% CI 0.942-0.969) and 0.767 (95% CI 0.664-0.875). Spatially, the two provinces with the highest relative risks are Xinjiang and Guizhou, and the remaining provinces with higher relative risks were mostly concentrated in the Northwest and South China regions. Temporally, the relative risk decreased year by year from 2013 to 2015. It was higher from February to May each year and was most significant in March. It decreased from June to December. Average relative humidity, monthly average precipitation, monthly average sunshine duration and monthly per capita GDP had positive effects on the relative risk of tuberculosis. The average temperature and pressure had negative effects. The average wind speed had no significant effect. Mainland China should adapt measures to local conditions and develop tuberculosis prevention and control strategies based on the characteristics of different regions and time.

Analysis of HIV/AIDS Epidemic and Socioeconomic Factors in Sub-Saharan Africa.

Sub-Saharan Africa has been the epicenter of the outbreak since the spread of acquired immunodeficiency syndrome (AIDS) began to be prevalent. This article proposes several regression models to investigate the relationships between the HIV/AIDS epidemic and socioeconomic factors (the gross domestic product per capita, and population density) in ten countries of Sub-Saharan Africa, for 2011-2016. The maximum likelihood method was used to estimate the unknown parameters of these models along with the Newton-Raphson procedure and Fisher scoring algorithm. Comparing these regression models, there exist significant spatiotemporal non-stationarity and auto-correlations between the HIV/AIDS epidemic and two socioeconomic factors. Based on the empirical results, we suggest that the geographically and temporally weighted Poisson autoregressive (GTWPAR) model is more suitable than other models, and has the better fitting results.

Niclosamide and its derivative DK-520 inhibit RANKL-induced osteoclastogenesis.

Niclosamide is a potent inhibitor of osteoclastogenesis and bone remodeling. DK-520 is an acyl derivative of Niclosamide and significantly increased both the plasma concentration and the duration of exposure of Niclosamide when dosed orally. However, at present the effect of DK-520 on osteoclastogenesis has not been reported. Here, we investigated whether DK-520 can regulate receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclastogenesis of bone marrow macrophages (BMMs) in vitro. Following induction of BMMs with RANKL for three days, we detected differentiated osteoclasts with typical morphology and high levels of tartrate-resistant acid phosphatase (TRAP), RANKL, and cathepsin K (CTSK) expression. Treatment with either Niclosamide or DK-520 did not affect the viability of osteoclast precursors (OCPs), but significantly inhibited RANKL-induced transdifferentiation of macrophages into OCPs, particularly in the early stage of osteoclastogenesis. Both Niclosamide and DK-520 significantly decreased the relative levels of transcription factor PU.1 mRNA transcripts and dendritic cell-specific transmembrane protein (DC-STAMP), but not v-ATPasev0 d2 protein expression in OCPs. In addition, the inhibitory effect of DK-520 on osteoclastogenesis is realized through impairment of the NF-kB (nuclear factor-κB) and MAPK (mitogen-activated protein kinase) signaling pathways. These results demonstrate that DK-520, like Niclosamide, effectively inhibits the early stage of osteoclastogenesis. The findings presented here, together with its increased oral plasma concentrations and bioavailability, suggest that DK-520 may be a promising drug candidate for treatment of osteoclast-related diseases.

An analytical comparison of the principal component method and the mixed effects model for association studies in the presence of cryptic relatedness and population stratification.

The principal component method and the mixed effects model represent two popular approaches to controlling for population structure and cryptic relatedness in genetic association studies. There are only a handful of studies comparing their performance. These studies are typically based on simulation studies and the results are therefore limited in their applicability. In this paper, we conduct an analytical comparison of these two approaches in the presence of cryptic relatedness and population structure in terms of their validity and efficiency. In the presence of cryptic relatedness, we show that both methods are valid, but the mixed effects model is more powerful for detecting association. In the presence of population structure, however, we show that both methods can be invalid. The biases and variances of the estimates from the two methods are compared. Examples and simulation studies are provided to demonstrate the conclusions.