Construction of a Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) Model and Comparative Analysis with GWR-Based Models for Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China)

Factors affecting the incidence of pulmonary tuberculosis based on the GTWR model in China, 2004-2021

Contra-posing panel data on the incidence of pulmonary tuberculosis (PTB) at the provincial level in China through the years of 2004-2021 and introducing a geographically and temporally weighted regression (GTWR) model were used to explore the effect of various factors on the incidence of PTB from the perspective of spatial heterogeneity. The principal component analysis (PCA) was used to extract the main information from twenty-two indexes under six macro-factors. The main influencing factors were determined by the Spearman correlation and multi-collinearity tests. After fitting different models, the GTWR model was used to analyse and obtain the distribution changes of regression coefficients. Six macro-factors and incidence of PTB were both correlated, and there was no collinearity between the variables. The fitting effect of the GTWR model was better than ordinary least-squares (OLS) and geographically weighted regression (GWR) models. The incidence of PTB in China was mainly affected by six macro-factors, namely medicine and health, transportation, environment, economy, disease, and educational quality. The influence degree showed an unbalanced trend in the spatial and temporal distribution.

Spatiotemporal variations and potential influencing factors of hemorrhagic fever with renal syndrome: A case study in Weihe Basin, China

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) is a widespread zoonotic disease seriously threatening Chinese residents' health. HFRS of Weihe Basin remains highly prevalent in recent years and attracts wide attention. With the acceleration of urbanization and related environmental changes, the interaction among anthropogenic activities, environmental factors, and host animals becomes more complicated in this area, which posed increasingly complex challenges for implementing effective prevention measures. Identifying the potential influencing factors of continuous HFRS epidemics in this typical area is critical to make targeted prevention and control strategies.

METHODS

Spatiotemporal characteristics of HFRS epidemic were analyzed based on HFRS case point data in Weihe Basin from 2005 to 2020. MaxEnt models were constructed to explore the main influencing factors of HFRS epidemic based on HFRS data, natural environment factors and socioeconomic factors.

RESULTS

Results showed that the HFRS epidemics in Weihe Basin were temporally divided into three periods (the relatively stable period, the rapid rising period, and the fluctuating rising period) and were spatially featured by relatively concentrated in the plains alongside the Weihe River. Landscape played controlling effect in this area while land use, vegetation and population in the area interacted with each other and drove the change of HFRS epidemic. The potential high-risk area for HFRS epidemic was 419 km2, where the HFRS case density reached 12.48 cases/km2, especially in the northern plains of Xi'an City.

CONCLUSION

We suggested that the temporal and spatial variations in the HFRS epidemics, as well as their dominant influencing factors should be adequately considered for making and/or adjusting the targeted prevention and control strategies on this disease in Weihe Basin.

Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease that has threatened Chinese residents for nearly a century. Although comprehensive prevent and control measures were taken, the HFRS epidemic in China presents a rebounding trend in some areas. Urbanization is considered as an important influencing factor for the HFRS epidemic in recent years; however, the relevant research has not been systematically summarized. This review aims to summarize urbanization-related environmental factors and the HFRS epidemic in China and provide an overview of research perspectives. The literature review was conducted following the PRISMA protocol. Journal articles on the HFRS epidemic in both English and Chinese published before 30 June 2022 were identified from PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI). Inclusion criteria were defined as studies providing information on urbanization-related environmental factors and the HFRS epidemic. A total of 38 studies were included in the review. Changes brought by urbanization on population, economic development, land use, and vaccination program were found to be significantly correlated with the HFRS epidemic. By changing the ecological niche of humans-affecting the rodent population, its virus-carrying rate, and the contact opportunity and susceptibility of populations-urbanization poses a biphasic effect on the HFRS epidemic. Future studies require systematic research framework, comprehensive data sources, and effective methods and models.

Spatial-temporal dynamics and time series prediction of HFRS in mainland China: A long-term retrospective study

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China. The current study aims to characterize the spatial-temporal dynamics of HFRS in mainland China during a long-term period (1950-2018). A total of 1 665 431 cases of HFRS were reported with an average annual incidence of 54.22 cases/100 000 individuals during 1950-2018. The joint regression model was used to define the global trend of the HFRS cases with an increasing-decreasing-slightly increasing-decreasing-slightly increasing trend during the 68 years. Then spatial correlation analysis and wavelet cluster analysis were used to identify four types of clusters of HFRS cases located in central and northeastern China. Lastly, the prophet model outperforms auto-regressive integrated moving average model in the HFRS modeling. Our findings will help reduce the knowledge gap on the transmission dynamics and distribution patterns of the HFRS in mainland China and facilitate to take effective preventive and control measures for the high-risk epidemic area.

Identifying the Determinants of Distribution of Oncomelania hupensis Based on Geographically and Temporally Weighted Regression Model along the Yangtze River in China

Background: As the unique intermediate host of Schistosoma japonicum, the geographical distribution of Oncomelania hupensis (O. hupensis) is an important index in the schistosomiasis surveillance system. This study comprehensively analyzed the pattern of snail distribution along the Yangtze River in Jiangsu Province and identified the dynamic determinants of the distribution of O. hupensis. Methods: Snail data from 2017 to 2021 in three cities (Nanjing, Zhenjiang, and Yangzhou) along the Yangtze River were obtained from the annual cross-sectional survey produced by the Jiangsu Institute of Parasitic Diseases. Spatial autocorrelation and hot-spot analysis were implemented to detect the spatio–temporal dynamics of O. hupensis distribution. Furthermore, 12 factors were used as independent variables to construct an ordinary least squares (OLS) model, a geographically weighted regression (GWR) model, and a geographically and temporally weighted regression (GTWR) model to identify the determinants of the distribution of O. hupensis. The adjusted coefficients of determination (adjusted R², AICc, RSS) were used to evaluate the performance of the models. Results: In general, the distribution of O. hupensis had significant spatial aggregation in the past five years, and the density of O. hupensis increased eastwards in the Jiangsu section of the lower reaches of the Yangtze River. Relatively speaking, the distribution of O. hupensis wase spatially clustered from 2017 to 2021, that is, it was found that the border between Yangzhou and Zhenjiang was the high density agglomeration area of O. hupensis snails. According to the GTWR model, the density of O. hupensis was related to the normalized difference vegetation index, wetness, dryness, land surface temperature, elevation, slope, and distance to nearest river, which had a good explanatory power for the snail data in Yangzhou City (adjusted R² = 0.7039, AICc = 29.10, RSS = 6.81). Conclusions: The distribution of O. hupensis and the environmental factors in the Jiangsu section of the lower reaches of the Yangtze River had significant spatial aggregation. In different areas, the determinants affecting the distribution of O. hupensis were different, which could provide a scientific basis for precise prevention and control of O. hupensis. A GTWR model was prepared and used to identify the dynamic determinants for the distribution of O. hupensis and contribute to the national programs of control of schistosomiasis and other snail-borne diseases.

Has Urban Construction Land Achieved Low-Carbon Sustainable Development? A Case Study of North China Plain, China

The rapid expansion of urban construction land (UCL) provides a guarantee to support rapid economic development and meet the social needs of urban residents. However, urban construction land is also an important source of carbon dioxide emissions. Therefore, it is of great research value to investigate the relationship between UCL and carbon emissions in depth. Based on this, using panel data of 57 cities in the North China Plain from 2007 to 2018, the study found that there is a strong positive correlation between UCL and CO2 emissions. It can be seen that the expansion of UCL is an important source of CO2 emissions. On the basis of this research conclusion, first, this paper uses the Tapio decoupling model to analyze the decoupling relationship between UCL and carbon emissions in the North China Plain. Then, the spatial autocorrelation analysis was applied to explore the spatial correlation characteristics of the carbon emission intensity of UCL in cities in the North China Plain. Finally, using the GTWR model to analyze the influencing factors of the carbon emission intensity of UCL, the following conclusions were drawn. In 2007–2015, the decoupling relationship performed well, but it deteriorated significantly from 2015 to 2018; in addition, there was a significant positive spatial correlation of carbon emission intensity of UCL. Various influencing factors have a significant impact on the carbon emission intensity of UCL, for example, the urbanization rate, industrial structure, economic development level, and population density have a positive impact, and environmental regulations, foreign investment intensity, land use efficiency and greenery coverage have a negative impact. The research results of this paper provide a scientific basis for making decisions and optimizing pathways to achieve carbon emission reduction from UCL in the North China Plain, as well as certain reference values for other regions to achieve low-carbon development of UCL. This is significant for exploring the optimal solution of land and carbon emissions and building a harmonious human–land relationship.

Spatiotemporal Heterogeneity and Driving Mechanism of Co-Ordinated Urban Development: A Case Study of the Central Area of the Yangtze River Delta, China

Urban system is a complex entirety composed of various subsystems, including land, population, economy, and society. The coordinated development of these subsystems is important for ensuring the advancement and improvement of the new urbanization process. Quantitatively evaluating the coordinated development level of a city or urban agglomeration is conducive to scientific urban planning and decision-making. In this paper, we constructed a multi-index evaluation system that includes land urbanization, population urbanization, economic urbanization, and social urbanization indicators to assess the degree of coordinated urbanization development. Experiments were then conducted in the central area of the Yangtze River Delta (YRD) from 2000 to 2019 using remote sensing images and statistical yearbook data. The driving factors of the urbanization evolution were also evaluated by the Geographically and Temporally Weighted Regression (GTWR) model. The conclusions were drawn as follows: (1) The degree of coordinated urbanization development in the study area was constantly improving, from an extremely uncoordinated level in 2000 to a high-quality coordinated level in 2019; (2) During the period 2000–2019, the distribution of the cities with high coordinated development levels presented a northwest-southeast pattern, and the gravity center of the region constantly moved to the south; (3) In terms of the impact of various urbanization subsystems on the coordinated urbanization development in the YRD, economic urbanization had the greatest impact, while land urbanization had the least impact. (4) In connection with the problems existing in different dimensions of urbanization, we put forward corresponding development countermeasures and path suggestions based on the actual situation of the study area.

A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective

Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various "densities" were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the "densities" were actually an abstract reflection of the "contact" frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect "contact" frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional "densities". Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R2) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling.

Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China

Background

“Schistosomiasis” is a highly recurrent parasitic disease that affects a wide range of areas and a large number of people worldwide. In China, schistosomiasis has seriously affected the life and safety of the people and restricted the economic development. Schistosomiasis is mainly distributed along the Yangtze River and in southern China. Anhui Province is located in the Yangtze River Basin of China, with dense water system, frequent floods and widespread distribution of Oncomelania hupensis that is the only intermediate host of schistosomiasis, a large number of cattle, sheep and other livestock, which makes it difficult to control schistosomiasis. It is of great significance to monitor and analyze spatiotemporal risk of schistosomiasis in Anhui Province, China. We compared and analyzed the optimal spatiotemporal interpolation model based on the data of schistosomiasis in Anhui Province, China and the spatiotemporal pattern of schistosomiasis risk was analyzed.

Methods

In this study, the root-mean-square-error (RMSE) and absolute residual (AR) indicators were used to compare the accuracy of Bayesian maximum entropy (BME), spatiotemporal Kriging (STKriging) and geographical and temporal weighted regression (GTWR) models for predicting the spatiotemporal risk of schistosomiasis in Anhui Province, China.

Results

The results showed that (1) daytime land surface temperature, mean minimum temperature, normalized difference vegetation index, soil moisture, soil bulk density and urbanization were significant factors affecting the risk of schistosomiasis; (2) the spatiotemporal distribution trends of schistosomiasis predicted by the three methods were basically consistent with the actual trends, but the prediction accuracy of BME was higher than that of STKriging and GTWR, indicating that BME predicted the prevalence of schistosomiasis more accurately; and (3) schistosomiasis in Anhui Province had a spatial autocorrelation within 20 km and a temporal correlation within 10 years when applying the optimal model BME.

Conclusions

This study suggests that BME exhibited the highest interpolation accuracy among the three spatiotemporal interpolation methods, which could enhance the risk prediction model of infectious diseases thereby providing scientific support for government decision making.

Modeling the Spatiotemporal Association Between COVID-19 Transmission and Population Mobility Using Geographically and Temporally Weighted Regression

The ongoing Coronavirus Disease 2019 (COVID-19) has posed a serious threat to human public health and global economy. Population mobility is an important factor that drives the spread of COVID-19. This study aimed to quantitatively evaluate the impact of population flow on the spread of COVID-19 from a spatiotemporal perspective. To this end, a case study was carried out in Hubei Province, which was once the most affected area of COVID-19 outbreak in Mainland China. The geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal association between COVID-19 epidemic and population mobility. Two patterns of population flows, including the population inflow from Wuhan and intra-city population movement, were considered to construct explanatory variables. Results indicate that the GTWR model can reveal the spatial-temporal-varying relationships between COVID-19 and population mobility. Moreover, the association between COVID-19 case counts and population movements presented three stages of temporal variation characteristics due to the virus incubation period and implementation of strict lockdown measures. In the spatial dimension, evident geographical disparities were observed across Hubei Province. These findings can provide policymakers useful knowledge about the impact of population movement on the spatio-temporal transmission of COVID-19. Thus, targeted interventions, if necessary in certain time periods, can be implemented to restrict population flow in cities with high transmission risk.

Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014-2016

Numerous methods have been implemented to evaluate the relationship between environmental factors and respiratory mortality. However, the previous epidemiological studies seldom considered the spatial and temporal variation of the independent variables. The present study aims to detect the relations between respiratory mortality and related affecting factors across Xi'an during 2014-2016 based on a novel geographically and temporally weighted regression model (GTWR). Meanwhile, the ordinary least square (OLS) and the geographically weighted regression (GWR) models were developed for cross-comparison. Additionally, the spatial autocorrelation and Hot Spot analysis methods were conducted to detect the spatiotemporal dynamic of respiratory mortality. Some important outcomes were obtained. Socioeconomic and environmental determinants represented significant effects on respiratory diseases. The respiratory mortality exhibited an obvious spatial correlation feature, and the respiratory diseases tend to occur in winter and rural areas of the study area. The GTWR model outperformed OLS and GWR for determining the relations between respiratory mortality and socioeconomic as well as environmental determinants. The influence degree of anthropic factors on COPD mortality was higher than natural factors, and the effects of independent variables on COPD varied timely and locally. The results can supply a scientific basis for respiratory disease controlling and health facilities planning.

Understanding Spatiotemporal Variations of Ridership by Multiple Taxi Services

Recent years have seen the big growth of app-based taxi services by not only competing for rides with street-hailing taxi services but also generating new taxi rides. Moreover, the innovation in dynamic pricing also makes it competitive in both passenger and driver sides. However, current literature still lacks better understandings of induced changes in spatiotemporal variations in multiple taxi ridership after app-based taxi service launch. This study develops two study cases in New York City to explore impacts of presence of app-based taxi services on daily total and street-hailing taxi rides and impacts of dynamic pricing on hourly app-based taxi rides. Considering the panel data and treatment effect measurement in this problem, we introduce a mixed modeling structure with both geographically weighted panel regression and difference-in-difference estimator. This mixed modeling structure outperforms traditional fixed effects model in our study cases. Empirical analyses identified the significant spatiotemporal variations in impacts of presence of app-based taxi services; for instance, impacts daily total taxi rides in 2014 and 2016 and impacts on street-hailing taxi rides from 2012 to 2016. Moreover, we capture the spatial variations in impacts of dynamic pricing on hourly app-based taxi rides, as well as significant impacts of time of day, day of week, and vehicle supply.

Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space-time cube-based approach

Objective

Hemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the spatial and temporal characteristics of HFRS epidemics and their probable influencing factors.

Methods

We used the space–time cube (STC) method to investigate HFRS epidemics in different space–time locations. STC can be used to visualize the trajectories of moving objects (or changing tendencies) in space and time in three dimensions. We applied space–time statistical methods, including space–time hot spot and space–time local outlier analyses, based on a calculated STC model of HFRS cases, to identify spatial and temporal hotspots and outlier distributions. We used the space–time gravity center method to reveal associations between possible factors and HFRS epidemics.

Results

In this research, HFRS cases for each space–time location were defined by the STC model, which can present the dynamic characteristics of HFRS epidemics. The STC model delivered accurate and detailed results for the spatiotemporal patterns of HFRS epidemics.

Conclusion

The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns.

A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS)

Hemorrhagic fever with renal syndrome (HFRS) is a naturally-occurring, fecally transmitted disease caused by a Hantavirus (HV). It is extremely damaging to human health and results in many deaths annually, especially in Hubei Province, China. One of the primary characteristics of HFRS is the spatiotemporal heterogeneity of its occurrence, with notable seasonal differences. In view of this heterogeneity, the present study suggests that there is a need to focus on trend simulation and the spatiotemporal prediction of HFRS outbreaks. To facilitate this, we constructed a new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model. The SD-STARIMA model is based on the spatial and temporal characteristics of the Space-Time Autoregressive Integrated Moving Average (STARMA) model first developed by Cliff and Ord in 1974, which has proven useful in modelling the temporal aspects of spatially located data. This model can simulate the trends in HFRS epidemics, taking into consideration both spatial and temporal variations. The SD-STARIMA model is also able to make seasonal difference calculations to eliminate temporally non-stationary problems that are present in the HFRS data. Experiments have demonstrated that the proposed SD-STARIMA model offers notably better prediction accuracy, especially for spatiotemporal series data with seasonal distribution characteristics.