Bronchiolitis (inflammation of the lower respiratory tract) in infants is primarily due to viral infection and is the single most common cause of infant hospitalization in the United States. To increase epidemiological understanding of bronchiolitis (and, subsequently, develop better prevention strategies), this research analyzes data on infant bronchiolitis cases from the U.S. Military Health System between the years 2003-2013 in Norfolk, Virginia, USA. For privacy reasons, child home addresses, birth dates, and diagnosis dates were randomized (jittered) creating spatio-temporal uncertainty in the geographic location and timing of bronchiolitis incidents. Using spatio-temporal point patterns, we created a modeling strategy that accounts for the jittering to estimate and quantify the uncertainty for the incidence proportion (IP) of bronchiolitis. Additionally, we regress the IP onto key covariates including pollution where we adequately account for uncertainty in the pollution levels (i.e., covariate uncertainty) using a land use regression model. Our analysis results indicate that the IP is positively associated with sulfur dioxide and population density. Further, we demonstrate how scientific conclusions may change if various sources of uncertainty (either spatio-temporal or covariate uncertainty) are not accounted for. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
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http://dx.doi.org/10.1080/01621459.2019.1609480 | DOI Listing |
J Environ Manage
December 2024
School of Geography and Ocean Science, Nanjing University, Xianlin Ave.163, 210023, Nanjing, China.
The complex life cycle traits of amphibians make them especially sensitive to environmental change, and their ongoing conservation requires the maintenance of suitable habitat that accounts for such life cycle characteristics which may impacted by local environmental dynamics arising from climate change and human disturbance. Many existing studies on amphibian habitats disregard this important issue, leading to uncertainty in managing critical habitats. The application of appropriate conservation practices is therefore constrained by the fact that the major factors influencing amphibian habitats, and their spatio-temporal dynamics at different life stages, are poorly understood.
View Article and Find Full Text PDFProc Biol Sci
December 2024
NASA Ames Research Center, Moffett Field, CA, USA.
Outbreaks of COVID-19 in humans, Dutch elm disease in forests, and highly pathogenic avian influenza in wild birds and poultry highlight the disruptive impacts of infectious diseases on public health, ecosystems and economies. Infectious disease dynamics often depend on environmental conditions that drive occurrence, transmission and outbreaks. Remote sensing can contribute to infectious disease research and management by providing standardized environmental data across broad spatial and temporal extents, often at no cost to the user.
View Article and Find Full Text PDFSci Total Environ
December 2024
Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstrasse 19, Garmisch-Partenkirchen 82467, Germany.
Global fluvial ecosystems are important sources of greenhouse gases (CO, CH and NO) to the atmosphere, but their estimates are plagued by uncertainties due to unaccounted spatio-temporal variabilities in the fluxes. In this study, we tested the potential of modeling these variabilities using several machine learning models (ML) and three different input datasets (remotely sensed vegetation indices, in-situ water quality, and a combination of both) from 20 headwater catchments in Germany that differ in catchment land use and stream size. We also upscaled fluvial GHG fluxes for Germany using the best ML model and explored the role of catchment land use on the GHG spatial-temporal trends.
View Article and Find Full Text PDFPLoS One
December 2024
College of Computer Science and Software Engineering, Hohai University, Nanjing, China.
Accurate medium- to long-term runoff forecasting is of great significance for flood control, drought mitigation, comprehensive water resource management, and ecological restoration. However, runoff formation is a complex process influenced by various natural and anthropogenic factors, resulting in nonlinearity, nonstationarity, and long prediction periods, which complicate forecasting efforts. Traditional statistical models, which primarily focus on individual runoff sequences, struggle to integrate multi-source data, limiting their predictive accuracy.
View Article and Find Full Text PDFGlob Chang Biol
December 2024
Thünen Earth Observation (ThEO), Thünen Institute of Farm Economics, Braunschweig, Germany.
Soil monitoring requires accurate and spatially explicit information on soil organic carbon (SOC) trends and changes over time. Spatiotemporal SOC models based on Earth Observation (EO) satellite data can support large-scale SOC monitoring but often lack sufficient temporal validation based on long-term soil data. In this study, we used repeated SOC samples from 1986 to 2022 and a time series of multispectral bare soil observations (Landsat and Sentinel-2) to model high-resolution cropland SOC trends for almost four decades.
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