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Articles 1 - 6 of 6
Full-Text Articles in Entire DC Network
Temporal Geospatial Analysis Of Covid-19 Pre-Infection Determinants Of Risk In South Carolina, Tianchu Lyu, Nicole Hair, Nicholas Yell, Zhenlong Li, Shan Qiao Ph.D., Xiaoming Li Ph.D.
Temporal Geospatial Analysis Of Covid-19 Pre-Infection Determinants Of Risk In South Carolina, Tianchu Lyu, Nicole Hair, Nicholas Yell, Zhenlong Li, Shan Qiao Ph.D., Xiaoming Li Ph.D.
Faculty Publications
Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal-geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including …
Temporal Geospatial Analysis Of Covid-19 Pre-Infection Determinants Of Risk In South Carolina, Tianchu Lyu, Nicole L. Hair, Nicholas Yell, Zhenlong Li, Shan Qiao, Chen Liang, Xiaoming Li
Temporal Geospatial Analysis Of Covid-19 Pre-Infection Determinants Of Risk In South Carolina, Tianchu Lyu, Nicole L. Hair, Nicholas Yell, Zhenlong Li, Shan Qiao, Chen Liang, Xiaoming Li
Faculty Publications
Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal–geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including …
Marine Artificial Light At Night: An Empirical And Technical Guide, Thomas W. Davies, Svenja Tidau, Andrew J. Grimmer
Marine Artificial Light At Night: An Empirical And Technical Guide, Thomas W. Davies, Svenja Tidau, Andrew J. Grimmer
School of Biological and Marine Sciences
No abstract provided.
Machine Learning & Big Data Analyses For Wildfire & Air Pollution Incorporating Gis & Google Earth Engine, Abdullah Al Saim
Machine Learning & Big Data Analyses For Wildfire & Air Pollution Incorporating Gis & Google Earth Engine, Abdullah Al Saim
Graduate Theses and Dissertations
The climatic condition, the vegetation type, and the landscape of the United States have made it susceptible to wildfires. This research is divided into two parts based on the analysis of two different aspects of wildfires of two distinct regions. The first part of the study investigates the wildfire susceptibility in Arkansas. Arkansas is a natural state, and it is heavily dependent on its forest and agricultural resources. During the last 30 years, more than 1,000 wildfires occurred in Arkansas and caused more than 10,000 acres of burned areas. Therefore, identifying wildfire-susceptible areas is crucial for ensuring sustainable forest and …
Trends And Opportunities In Tick-Borne Disease Geography, Catherine A. Lippi, Sadie J. Ryan, Alexis L. White, Holly D. Gaff, Colin J. Carlson
Trends And Opportunities In Tick-Borne Disease Geography, Catherine A. Lippi, Sadie J. Ryan, Alexis L. White, Holly D. Gaff, Colin J. Carlson
Biological Sciences Faculty Publications
Tick-borne diseases are a growing problem in many parts of the world, and their surveillance and control touch on challenging issues in medical entomology, agricultural health, veterinary medicine, and biosecurity. Spatial approaches can be used to synthesize the data generated by integrative One Health surveillance systems, and help stakeholders, managers, and medical geographers understand the current and future distribution of risk. Here, we performed a systematic review of over 8,000 studies and identified a total of 303 scientific publications that map tick-borne diseases using data on vectors, pathogens, and hosts (including wildlife, livestock, and human cases). We find that the …
Geocoding Cryptosporidiosis Cases In Ireland (2008–2017)—Development Of A Reliable, Reproducible, Multiphase Geocoding Methodology, Lisa Domegan, Patricia Garvey, Paul Mckeown, Howard Johnson, Paul Hynds, Jean O'Dwyer, Coilín Óhaiseadha
Geocoding Cryptosporidiosis Cases In Ireland (2008–2017)—Development Of A Reliable, Reproducible, Multiphase Geocoding Methodology, Lisa Domegan, Patricia Garvey, Paul Mckeown, Howard Johnson, Paul Hynds, Jean O'Dwyer, Coilín Óhaiseadha
Articles
Background: Geocoding (the process of converting a text address into spatial data) quality may affect geospatial epidemiological study findings. No national standards for best geocoding practice exist in Ireland. Irish postcodes (Eircodes) are not routinely recorded for infectious disease notifications and > 35% of dwellings have non-unique addresses. This may result in incomplete geocoding and introduce systematic errors into studies.
Aims: This study aimed to develop a reliable and reproducible methodology to geocode cryptosporidiosis notifications to fine-resolution spatial units (Census 2016 Small Areas), to enhance data validity and completeness, thus improving geospatial epidemiological studies.
Methods: A protocol was devised to utilise …