Evaluation of Bayesian Spatial-Temporal Latent Models in Small Area Health Data

Health outcomes are linked to air pollution, demographic, or socioeconomic factors which vary across space and time. Thus, it is often found that relative risks in spatial health data have locally different patterns. In such cases, latent modeling is useful in the disaggregation of risk proï¬les. In particular, spatial-temporal mixture models can help to isolate spatial clusters each of which has a homogeneous temporal pattern in relative risks. Mixture models are assumed as they have various weight structures and considered in two situations: the number of underlying components is known or unknown. In this paper, we compare spatial-temporal mixture models with different weight structures in both situations. For comparison, we propose a set of spatial cluster detection diagnostics which are based on the posterior distribution of weights. We also develop new accuracy measures to assess the recovery of true relative risk. Based on the simulation study, we examine the performance of various spatial-temporal mixture models in terms of proposed methods and goodness-of-ï¬t measures. We examine two real data sets: low birth weight data and chronic obstructive pulmonary disease data

Investigating the spatio-temporal variation of hepatitis A in Korea using a Bayesian model

Hepatitis A is a water-borne infectious disease that frequently occurs in unsanitary environments. However, paradoxically, those who have spent their infancy in a sanitary environment are more susceptible to hepatitis A because they do not have the opportunity to acquire natural immunity. In Korea, hepatitis A is prevalent because of the distribution of uncooked seafood, especially during hot and humid summers. In general, the transmission of hepatitis A is known to be dynamically affected by socioeconomic, environmental, and weather-related factors and is heterogeneous in time and space. In this study, we aimed to investigate the spatio-temporal variation of hepatitis A and the effects of socioeconomic and weather-related factors in Korea using a flexible spatio-temporal model. We propose a Bayesian Poisson regression model coupled with spatio-temporal variability to estimate the effects of risk factors. We used weekly hepatitis A incidence data across 250 districts in Korea from 2016 to 2019. We found spatial and temporal autocorrelations of hepatitis A indicating that the spatial distribution of hepatitis A varied dynamically over time. From the estimation results, we noticed that the districts with large proportions of males and foreigners correspond to higher incidences. The average temperature was positively correlated with the incidence, which is in agreement with other studies showing that the incidences in Korea are noticeable in spring and summer due to the increased outdoor activity and intake of stale seafood. To the best of our knowledge, this study is the first to suggest a spatio-temporal model for hepatitis A across the entirety of Korean. The proposed model could be useful for predicting, preventing, and controlling the spread of hepatitis A

Bayesian zero-inflated spatio-temporal modelling of scrub typhus data in Korea, 2010-2014

Scrub typhus, a bacterial, febrile disease commonly occurring in the autumn, can easily be cured if diagnosed early. However, it can develop serious complications and even lead to death. For this reason, it is an important issue to find the risk factors and thus be able to prevent outbreaks. We analyzed the monthly scrub typhus data over the entire areas of South Korea from 2010 through 2014. A 2-stage hierarchical framework was considered since weather data are covariates and the scrub typhus data have different spatial resolutions. At the first stage, we obtained the administrative-level estimates for weather data using a spatial model; in the second, we applied a Bayesian zero-inflated spatio-temporal model since the scrub typhus data include excess zero counts. We found that the zero-inflated model considering the spatio-temporal interaction terms improves fitting and prediction performance. This study found that low humidity and a high proportion of elderly people are significantly associated with scrub typhus incidence

Bayesian 2-Stage Space-Time Mixture Modeling with Spatial Misalignment of the Exposure in Small Area Health Data

We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999-2008

Spatial epidemic dynamics of the COVID-19 outbreak in China

BACKGROUND: On December 31, 2019, an outbreak of COVID-19 in Wuhan, China, was reported. The outbreak spread rapidly to other Chinese cities and to multiple countries. We describe the spatio-temporal pattern and measure the spatial association of the early stages of the COVID-19 epidemic in mainland China from January 16 to February 6, 2020. METHODS: We explored the spatial epidemic dynamics of COVID-19 in mainland China. Moran’s I spatial statistic with various definitions of neighbors was used to conduct a test to determine whether a spatial association of the COVID-19 infections existed. RESULTS: We observed the spatial spread of the COVID-19 pandemic in China. The results showed that most of the models, except medical-care-based connection models, indicated a significant spatial association of COVID-19 infections from around January 22, 2020. CONCLUSIONS: Spatial analysis is of great help in understanding the spread of infectious diseases, and spatial association is the key to the spatial spread during the early stages of the COVID-19 pandemic in mainland China

Spatial-temporal association between fine particulate matter and daily mortality

Fine particulate matter (PM2.5) is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM2.5 varies across space and time, the association between PM2.5 and mortality could also change with space and season. A statistical multi-stage Bayesian framework is developed and implemented, which provides a very broad and flexible approach to studying the spatiotemporal associations between mortality and population exposure to daily PM2.5 mass, while accounting for different sources of uncertainty. The first stage of the framework maps ambient PM2.5 air concentrations using all available monitoring data (IMPROVE and FRM) and an air quality model (CMAQ) at different spatial and temporal scales. The second stage of the framework examines the spatial temporal relationships between the health end-points and the exposures to PM2.5 by introducing a spatial-temporal generalized Poisson regression model. A method to adjust for time-varying confounders such as seasonal trends is proposed. A common seasonal trends model uses a fixed number of basis functions to account for these confounders, but the results can be sensitive to the number of basis functions. Thus, instead the number of the basis functions is treated as an unknown parameter in the Bayesian model, and a space-time stochastic search variable selection approach is used. The framework is illustrated using a data set in North Carolina for the year 2001.

Multivariate spatial-temporal modeling and prediction of speciated fine particles 1

Fine particulate matter (PM2.5) is an atmospheric pollutant that has been linked to serious health problems, including mortality. PM2.5 is a mixture of pollutants, and it has five main components: sulfate, nitrate, total carbonaceous mass, ammonium, and crustal material. These components have complex spatial-temporal dependency and cross dependency structures. It is important to gain insight and better understanding about the spatial-temporal distribution of each component of the total PM2.5 mass, and also to estimate how the composition of PM2.5 might change with space and time, by spatially interpolating speciated PM2.5. This type of analysis is needed to conduct spatial-temporal epidemiological studies of the association of these pollutants and adverse health effects. We introduce a multivariate spatial-temporal model for speciated PM2.5. We pro-pose a Bayesian hierarchical framework with spatiotemporally varying coefficients. In addition, a linear model of coregionalization is developed to account for spatial and temporal dependency structures for each component as well as the associations among the components. We also introduce a statistical framework to combine different sources of data, which accounts for bias and measurement error. We apply our framework to speciated PM2.5 data in the United States for the year 2004. Our study shows that sulfate concentrations are the highest during the summer while nitrate concentrations are the highest during the winter. The results also show total carbonaceous mas

Exploring the catchment area of an urban railway station by using transit card data: Case study in Seoul

To enhance transit ridership, Seoul introduced a transfer discount fare scheme that uses an automated fare collection system in Seoul 2004. The transfer discount fare system records all transfer information between rail and buses in transit smartcard data, which enabled us to explore an urban railway station\u27s catchment area. In this study, we examined the geographic distribution of rail-to-bus transfer trips and their characteristics by using transit smartcard data. Mokdong station in Seoul was used as a case study to demonstrate the benefits of data mining for the depiction and easy evaluation of a station\u27s catchment area. The results showed that the average transfer passenger traveled 1.7 km with five bus stops after boarding to access the business district during the morning peak hour. The cumulative distribution of alighting passengers by bus route helped with inferring the shape and size of the urban railway station\u27s catchment area in each direction and depending on the time of day. We found that reliable transfer travel data constitute valuable information for evaluating an urban railway station\u27s catchment area with respect to the type of land use and will help transit agencies with providing better transit services in terms of enhanced accessibility by changing bus headways and routes, as well as land use planners with evaluating transit-oriented development based on the expanded concept of a metro station\u27s catchment area