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Disease Ecology of Rickettsial Species: A Data Science Approach
We present an approach to assess the disease ecology of rickettsial species by investigating open databases and by using data science methodologies. First, we explored the epidemiological trend and changes of human rickettsial disease epidemics over the years and compared this trend with knowledge o...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344507/ https://www.ncbi.nlm.nih.gov/pubmed/32349270 http://dx.doi.org/10.3390/tropicalmed5020064 |
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author | Morand, Serge Chaisiri, Kittipong Kritiyakan, Anamika Kumlert, Rawadee |
author_facet | Morand, Serge Chaisiri, Kittipong Kritiyakan, Anamika Kumlert, Rawadee |
author_sort | Morand, Serge |
collection | PubMed |
description | We present an approach to assess the disease ecology of rickettsial species by investigating open databases and by using data science methodologies. First, we explored the epidemiological trend and changes of human rickettsial disease epidemics over the years and compared this trend with knowledge on emerging rickettsial diseases given by published reviews. Second, we investigated the global diversity of rickettsial species recorded in humans, domestic animals and wild mammals, using the Enhanced Infectious Disease Database (EID2) and employing a network analysis approach to represent and quantify transmission ecology of rickettsial species among their carriers, arthropod vectors or mammal reservoirs and humans. Our results confirmed previous studies that emphasized the increasing incidence in rickettsial diseases at the onset of 1970. Using the Global Infectious Diseases and Epidemiology Online Network (GIDEON) database, it was even possible to date the start of this increase of global outbreaks in rickettsial diseases in 1971. Network analysis showed the importance of domestic animals and peridomestic mammals in sharing rickettsial diseases with humans and other wild animals, acting as important hubs or connectors for rickettsial transmission. |
format | Online Article Text |
id | pubmed-7344507 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73445072020-07-14 Disease Ecology of Rickettsial Species: A Data Science Approach Morand, Serge Chaisiri, Kittipong Kritiyakan, Anamika Kumlert, Rawadee Trop Med Infect Dis Article We present an approach to assess the disease ecology of rickettsial species by investigating open databases and by using data science methodologies. First, we explored the epidemiological trend and changes of human rickettsial disease epidemics over the years and compared this trend with knowledge on emerging rickettsial diseases given by published reviews. Second, we investigated the global diversity of rickettsial species recorded in humans, domestic animals and wild mammals, using the Enhanced Infectious Disease Database (EID2) and employing a network analysis approach to represent and quantify transmission ecology of rickettsial species among their carriers, arthropod vectors or mammal reservoirs and humans. Our results confirmed previous studies that emphasized the increasing incidence in rickettsial diseases at the onset of 1970. Using the Global Infectious Diseases and Epidemiology Online Network (GIDEON) database, it was even possible to date the start of this increase of global outbreaks in rickettsial diseases in 1971. Network analysis showed the importance of domestic animals and peridomestic mammals in sharing rickettsial diseases with humans and other wild animals, acting as important hubs or connectors for rickettsial transmission. MDPI 2020-04-27 /pmc/articles/PMC7344507/ /pubmed/32349270 http://dx.doi.org/10.3390/tropicalmed5020064 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Morand, Serge Chaisiri, Kittipong Kritiyakan, Anamika Kumlert, Rawadee Disease Ecology of Rickettsial Species: A Data Science Approach |
title | Disease Ecology of Rickettsial Species: A Data Science Approach |
title_full | Disease Ecology of Rickettsial Species: A Data Science Approach |
title_fullStr | Disease Ecology of Rickettsial Species: A Data Science Approach |
title_full_unstemmed | Disease Ecology of Rickettsial Species: A Data Science Approach |
title_short | Disease Ecology of Rickettsial Species: A Data Science Approach |
title_sort | disease ecology of rickettsial species: a data science approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344507/ https://www.ncbi.nlm.nih.gov/pubmed/32349270 http://dx.doi.org/10.3390/tropicalmed5020064 |
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