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A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment
BACKGROUND: Source identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens. METHODS: As a case study, we retrospectively analyzed three Q fever outbreaks in the Neth...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3850919/ https://www.ncbi.nlm.nih.gov/pubmed/24324598 http://dx.doi.org/10.1371/journal.pone.0080412 |
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author | van Leuken, Jeroen P. G. Havelaar, Arie H. van der Hoek, Wim Ladbury, Georgia A. F. Hackert, Volker H. Swart, Arno N. |
author_facet | van Leuken, Jeroen P. G. Havelaar, Arie H. van der Hoek, Wim Ladbury, Georgia A. F. Hackert, Volker H. Swart, Arno N. |
author_sort | van Leuken, Jeroen P. G. |
collection | PubMed |
description | BACKGROUND: Source identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens. METHODS: As a case study, we retrospectively analyzed three Q fever outbreaks in the Netherlands in 2009, each with suspected exposure from a single large dairy goat farm. Model input consisted only of case residential addresses, day of first clinical symptoms, and human population density data. We defined a spatial grid and fitted an exponentially declining function to the incidence-distance data of each grid point. For any grid point with a fit significant at the 95% confidence level, we calculated a measure of risk. For validation, we used results from abortion notifications, voluntary (2008) and mandatory (2009) bulk tank milk sampling at large (i.e. >50 goats and/or sheep) dairy farms, and non-systematic vaginal swab sampling at large and small dairy and non-dairy goat/sheep farms. In addition, we performed a two-source simulation study. RESULTS: Hotspots – areas most likely to contain the actual source – were identified at early outbreak stages, based on the earliest 2–10% of the case notifications. Distances between the hotspots and suspected goat farms varied from 300–1500 m. In regional likelihood rankings including all large dairy farms, the suspected goat farms consistently ranked first. The two-source simulation study showed that detection of sources is most clear if the distance between the sources is either relatively small or relatively large. CONCLUSIONS: Our model identifies the most likely location of sources in an airborne pathogen outbreak area, even at early stages. It can help to reduce the number of potential sources to be investigated by microbial testing and to allow rapid implementation of interventions to limit the number of human infections and to reduce the risk of source-to-source transmission. |
format | Online Article Text |
id | pubmed-3850919 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38509192013-12-09 A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment van Leuken, Jeroen P. G. Havelaar, Arie H. van der Hoek, Wim Ladbury, Georgia A. F. Hackert, Volker H. Swart, Arno N. PLoS One Research Article BACKGROUND: Source identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens. METHODS: As a case study, we retrospectively analyzed three Q fever outbreaks in the Netherlands in 2009, each with suspected exposure from a single large dairy goat farm. Model input consisted only of case residential addresses, day of first clinical symptoms, and human population density data. We defined a spatial grid and fitted an exponentially declining function to the incidence-distance data of each grid point. For any grid point with a fit significant at the 95% confidence level, we calculated a measure of risk. For validation, we used results from abortion notifications, voluntary (2008) and mandatory (2009) bulk tank milk sampling at large (i.e. >50 goats and/or sheep) dairy farms, and non-systematic vaginal swab sampling at large and small dairy and non-dairy goat/sheep farms. In addition, we performed a two-source simulation study. RESULTS: Hotspots – areas most likely to contain the actual source – were identified at early outbreak stages, based on the earliest 2–10% of the case notifications. Distances between the hotspots and suspected goat farms varied from 300–1500 m. In regional likelihood rankings including all large dairy farms, the suspected goat farms consistently ranked first. The two-source simulation study showed that detection of sources is most clear if the distance between the sources is either relatively small or relatively large. CONCLUSIONS: Our model identifies the most likely location of sources in an airborne pathogen outbreak area, even at early stages. It can help to reduce the number of potential sources to be investigated by microbial testing and to allow rapid implementation of interventions to limit the number of human infections and to reduce the risk of source-to-source transmission. Public Library of Science 2013-12-04 /pmc/articles/PMC3850919/ /pubmed/24324598 http://dx.doi.org/10.1371/journal.pone.0080412 Text en © 2013 van Leuken et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article van Leuken, Jeroen P. G. Havelaar, Arie H. van der Hoek, Wim Ladbury, Georgia A. F. Hackert, Volker H. Swart, Arno N. A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment |
title | A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment |
title_full | A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment |
title_fullStr | A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment |
title_full_unstemmed | A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment |
title_short | A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment |
title_sort | model for the early identification of sources of airborne pathogens in an outdoor environment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3850919/ https://www.ncbi.nlm.nih.gov/pubmed/24324598 http://dx.doi.org/10.1371/journal.pone.0080412 |
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