187 Results Found

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependenci...

Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a z...

Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear mode...

Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the...

Ecosystems are undergoing continuous degradation due to the dual perturbation of global climate change and human activities, posing unprecedented threats and challenges to the ecosystem services they provide. To gain a deeper understanding of the spa...

Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road colli...

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited system...

Ozone concentrations are key indicators of air quality. Modeling ozone concentrations is challenging because they change both spatially and temporally with complicated structures. Missing data bring even more difficulties. One of our interests in thi...

Previous studies have shown an association between mortality and ambient air pollution in South Korea. However, these studies may have been subject to bias, as they lacked adjustment for spatio-temporal structures. This paper addresses this research...

Driven by carbon neutrality policies, the cumulative issuance volume of the global green bond market has surpassed $2.5 trillion over the past five years, with China, as the second largest issuer, accounting for 15%. However, there exists a yield dif...

Quantile modeling has been seen as an alternative and useful complement to ordinary regression mainly focusing on the mean. To directly apply quantile modeling to areal data the discrete conditional quantile function of the data can be an issue. Alth...

Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic...

There has been an increasing number of clinical and epidemiologic research projects providing supporting evidence that short-term exposure to ambient air pollution contributes to the exacerbation of cardiovascular disease. However, few studies consid...

With the rapid industrial development and urbanization in China over the past three decades, PM2.5 pollution has become a severe environmental problem that threatens public health. Due to its unbalanced development and intrinsic topography features,...

Accurate dengue forecasting is essential for sustainable public health planning, especially in tropical regions where the disease remains a persistent threat. This study evaluates the predictive performance of seven modeling approaches—Seasonal...

Understanding geospatial impacts of multi-sourced drivers on the tourism industry is of great significance for formulating tourism development policies tailored to regional-specific needs. To date, no research in China has explored the combined impac...

Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. I...

Background: Hand, foot, and mouth disease (HFMD) is a common infectious disease among children. Guangdong Province is one of the most severely affected provinces in south China. This study aims to identify the spatiotemporal distribution characterist...

A substantially growing health expenditure has become an important global issue. Thus, how and why health expenditure is rising should be urgently investigated in systematic research. The Bayesian space-time model and the Bayesian least absolute shri...

Climatic change is expected to affect forest development in the short term, as well as the spatial distribution of species in the long term. Species distribution models are potentially useful tools for guiding species choices in reforestation and for...

Crime is a negative phenomenon that affects the daily life of the population and its development. When modeling crime data, assumptions on either the spatial or the temporal relationship between observations are necessary if any statistical analysis...

Scrub typhus has emerged as a life-threatening and increasingly prevalent vector-borne disease. While the spatial and temporal distributions of scrub typhus have been studied by the research community, the main driving factors that influence the spat...

An important parameter in the monitoring and surveillance systems is the probability of detection. Advanced wildlife monitoring systems rely on camera traps for stationary wildlife photography and have been broadly used for estimation of population s...

Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hi...

Border regions have been implicated as important hot spots of malaria transmission, particularly in Latin America, where free movement rights mean that residents can cross borders using just a national ID. Additionally, rural livelihoods largely depe...

Alcohol outlets have been associated with different social problems, such as crime, violence, intimate partner violence, and child maltreatment. The spatial analysis of neighborhood availability of alcohol outlets is key for better understanding of t...

Increasingly intense marine heatwaves threaten the persistence of many marine ecosystems. Heat stress-mediated episodes of mass coral bleaching have led to catastrophic coral mortality globally. Remotely monitoring and forecasting such biotic respons...

Studies of land surface dynamics in heterogeneous landscapes often require satellite images with a high resolution, both in time and space. However, the design of satellite sensors often inherently limits the availability of such images. Images with...

La Crosse virus (LACV) is a rare cause of pediatric encephalitis, yet identifying and mitigating transmission foci is critical to detecting additional cases. Neurologic disease disproportionately occurs among children, and survivors often experience...

The urban heat island (UHI) phenomenon has been identified and studied for over two centuries. As one of the most important factors, land use, in terms of both composition and configuration, strongly influences the UHI. As a result of the availabilit...

Loss to follow-up (LTFU) is a risk factor for poor outcomes in HIV patients. The spatio-temporal risk of LTFU is useful to identify hotspots and guide policy. Secondary data on adult HIV patients attending a clinic in provinces of Zimbabwe between 20...

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesi...

Background: Globally, prostate cancer is the second leading cause of cancer deaths among males. It is the most commonly diagnosed cancer in Australia. The quality of life of prostate cancer patients is poorer when compared to the general population d...

Introduction: Malaria is a life-threatening disease ocuring mainly in developing countries. Almost half of the world’s population was at risk of malaria in 2020. Children under five years age are among the population groups at considerably high...

To establish a safety monitoring method for the uplift pressure of concrete dams, spatiotemporal information from monitoring data is needed. In the present study, the method of ordering points to identify the clustering structure is employed to spati...

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Pro...

Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects...

Hospital emergency departments are often overused by patients that do not really need urgent care. These admissions are one of the major factors contributing to hospital costs, which should not be allowed to compromise the response and effectiveness...

The field of spatio-temporal modelling has witnessed a recent surge as a result of developments in computational power and increased data collection. These developments allow analysts to model the evolution of health outcomes in both space and time s...

The growing deployment of electric vehicles (EVs) offers a unique opportunity to utilize them as mobile energy resources during large-scale emergencies. However, existing emergency dispatch strategies often neglect the compounded uncertainties of haz...

Satellite tracking has dramatically improved research on wide-ranging large marine vertebrates such as sea turtles. Traditionally, sea turtle tracking has relied on Argos-based satellite telemetry tags, which estimate location via Doppler shift and c...

Accurate and efficient estimation of streamflow in a watershed’s tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and...

Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. Recently, as deep learning models hav...

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 cont...

Spatiotemporal disease mapping modeling with count data is gaining increasing prominence. This approach serves as a benchmark in developing early warning systems for diverse disease types. Spatiotemporal modeling, characterized by its inherent comple...

Background: Diarrhea in children under five years of age remains a challenge in reducing child mortality in Nepal. Understanding the spatiotemporal patterns and influencing factors of the disease is important for control and intervention. Methods: Da...

Epidemiological approaches for examining human health responses to environmental exposures in observational studies frequently address confounding by employing advanced matching techniques and statistical methods grounded in conditional likelihood. T...

In this paper, we present algorithms for predicting a spatio-temporal random field measured by mobile robotic sensors under uncertainties in localization and measurements. The spatio-temporal field of interest is modeled by a sum of a time-varying me...

Spatio-temporal Bayesian disease mapping is the branch of spatial epidemiology interested in providing valuable risk estimates in certain geographical regions using administrative areas as statistical units. The aim of the present paper is to describ...

Exploring spatiotemporal patterns of traffic accidents from historic crash databases is one essential prerequisite for road safety management and traffic risk prevention. Presently, with the emergence of GIS and data mining technologies, numerous geo...