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Understanding temporal and spatial variations of viral disease in the US: The need for a one-health-based data collection and analysis approach

Viral diseases exhibit spatial and temporal variation, and there are many factors that can affect their occurrence. The identification of these factors is critical in the efforts to predict and lessen viral disease burden. Because viral infection is able to spread to humans from the environment, ani...

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Autores principales: O'Brien, Evan, Xagoraraki, Irene
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6831848/
https://www.ncbi.nlm.nih.gov/pubmed/31709295
http://dx.doi.org/10.1016/j.onehlt.2019.100105
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author O'Brien, Evan
Xagoraraki, Irene
author_facet O'Brien, Evan
Xagoraraki, Irene
author_sort O'Brien, Evan
collection PubMed
description Viral diseases exhibit spatial and temporal variation, and there are many factors that can affect their occurrence. The identification of these factors is critical in the efforts to predict and lessen viral disease burden. Because viral infection is able to spread to humans from the environment, animals, and other humans, the One-Health framework can be used to investigate the critical pathways through which viruses are transported and transmitted. A holistic approach, incorporating publicly available clinical data for human, livestock, and wildlife disease occurrence, together with environmental data reported in federal and state databases such as parameters related to land use, environmental quality, and weather, can enhance the understanding of variations in disease patterns, leading to the design and implementation of surveillance systems. An example analysis approach is presented for Michigan, United States, which is a state with large urban centers as well as a sizeable rural and agricultural population. Analysis of publicly available data from 2017 indicates that gastrointestinal (GI) and influenza-associated illnesses in Michigan may have been related with agricultural land use to a higher extent than with developed land use during that year. Meanwhile, hepatitis A virus appears to be most closely related with developed land use in dense population areas. GI illnesses may be related to precipitation, and this relationship is strongest in the springtime, although GI illnesses are most common in the winter months. Integration of human-related clinical data, animal disease data, and environmental data can ultimately be used for prioritization of the most critical locations and times for viral outbreaks in both urban and rural environments.
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spelling pubmed-68318482019-11-08 Understanding temporal and spatial variations of viral disease in the US: The need for a one-health-based data collection and analysis approach O'Brien, Evan Xagoraraki, Irene One Health Research Paper Viral diseases exhibit spatial and temporal variation, and there are many factors that can affect their occurrence. The identification of these factors is critical in the efforts to predict and lessen viral disease burden. Because viral infection is able to spread to humans from the environment, animals, and other humans, the One-Health framework can be used to investigate the critical pathways through which viruses are transported and transmitted. A holistic approach, incorporating publicly available clinical data for human, livestock, and wildlife disease occurrence, together with environmental data reported in federal and state databases such as parameters related to land use, environmental quality, and weather, can enhance the understanding of variations in disease patterns, leading to the design and implementation of surveillance systems. An example analysis approach is presented for Michigan, United States, which is a state with large urban centers as well as a sizeable rural and agricultural population. Analysis of publicly available data from 2017 indicates that gastrointestinal (GI) and influenza-associated illnesses in Michigan may have been related with agricultural land use to a higher extent than with developed land use during that year. Meanwhile, hepatitis A virus appears to be most closely related with developed land use in dense population areas. GI illnesses may be related to precipitation, and this relationship is strongest in the springtime, although GI illnesses are most common in the winter months. Integration of human-related clinical data, animal disease data, and environmental data can ultimately be used for prioritization of the most critical locations and times for viral outbreaks in both urban and rural environments. Elsevier 2019-10-19 /pmc/articles/PMC6831848/ /pubmed/31709295 http://dx.doi.org/10.1016/j.onehlt.2019.100105 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Paper
O'Brien, Evan
Xagoraraki, Irene
Understanding temporal and spatial variations of viral disease in the US: The need for a one-health-based data collection and analysis approach
title Understanding temporal and spatial variations of viral disease in the US: The need for a one-health-based data collection and analysis approach
title_full Understanding temporal and spatial variations of viral disease in the US: The need for a one-health-based data collection and analysis approach
title_fullStr Understanding temporal and spatial variations of viral disease in the US: The need for a one-health-based data collection and analysis approach
title_full_unstemmed Understanding temporal and spatial variations of viral disease in the US: The need for a one-health-based data collection and analysis approach
title_short Understanding temporal and spatial variations of viral disease in the US: The need for a one-health-based data collection and analysis approach
title_sort understanding temporal and spatial variations of viral disease in the us: the need for a one-health-based data collection and analysis approach
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6831848/
https://www.ncbi.nlm.nih.gov/pubmed/31709295
http://dx.doi.org/10.1016/j.onehlt.2019.100105
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