On Bayesian shared component disease mapping and ecological regression with errors in covariates

2010 ◽  
Vol 29 (11) ◽  
pp. 1239-1249 ◽  
Author(s):  
Ying C. MacNab
Keyword(s):  
Disease Mapping ◽  
Ecological Regression ◽  
Shared Component

scholarly journals Geospatial Analysis of COVID-19: A Scoping Review

2021 ◽  
Vol 18 (5) ◽  
pp. 2336
Author(s):  
Munazza Fatima ◽  
Kara J. O’Keefe ◽  
Wenjia Wei ◽  
Sana Arshad ◽  
Oliver Gruebner
Keyword(s):  
Disease Mapping ◽  
Geospatial Analysis ◽  
Hotspot Analysis ◽  
Spatial Studies ◽  
Epidemiological Modeling ◽  
Wide Range ◽  
Scoping Reviews ◽  
Disease Pattern ◽  
The Usa ◽  
Meta Analyses

The outbreak of SARS-CoV-2 in Wuhan, China in late December 2019 became the harbinger of the COVID-19 pandemic. During the pandemic, geospatial techniques, such as modeling and mapping, have helped in disease pattern detection. Here we provide a synthesis of the techniques and associated findings in relation to COVID-19 and its geographic, environmental, and socio-demographic characteristics, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology for scoping reviews. We searched PubMed for relevant articles and discussed the results separately for three categories: disease mapping, exposure mapping, and spatial epidemiological modeling. The majority of studies were ecological in nature and primarily carried out in China, Brazil, and the USA. The most common spatial methods used were clustering, hotspot analysis, space-time scan statistic, and regression modeling. Researchers used a wide range of spatial and statistical software to apply spatial analysis for the purpose of disease mapping, exposure mapping, and epidemiological modeling. Factors limiting the use of these spatial techniques were the unavailability and bias of COVID-19 data—along with scarcity of fine-scaled demographic, environmental, and socio-economic data—which restrained most of the researchers from exploring causal relationships of potential influencing factors of COVID-19. Our review identified geospatial analysis in COVID-19 research and highlighted current trends and research gaps. Since most of the studies found centered on Asia and the Americas, there is a need for more comparable spatial studies using geographically fine-scaled data in other areas of the world.

scholarly journals Disease Mapping of Leprosy in Maluku Province, 2019 using Hierarchical Bayes

2021 ◽  
Vol 1863 (1) ◽  
pp. 012012
Author(s):  
I F Mahdy ◽  
R Fitriani ◽  
W D Revildy
Keyword(s):  
Disease Mapping ◽  
Hierarchical Bayes

scholarly journals Imputation of confidential data sets with spatial locations using disease mapping models

2014 ◽  
Vol 33 (11) ◽  
pp. 1928-1945 ◽  
Author(s):  
Thais Paiva ◽  
Avishek Chakraborty ◽  
Jerry Reiter ◽  
Alan Gelfand
Keyword(s):  
Disease Mapping ◽  
Data Sets ◽  
Confidential Data ◽  
Spatial Locations ◽  
Mapping Models

Modelling risks in disease mapping

2006 ◽  
Vol 15 (1) ◽  
pp. 21-35 ◽  
Author(s):  
M D Ugarte ◽  
B Ibáñez ◽  
A F Militino
Keyword(s):  
Disease Mapping
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