Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Morand, Serge, Chaisiri, Kittipong, Kritiyakan, Anamika, Kumlert, Rawadee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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
_version_ 1783555959978721280
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
work_keys_str_mv AT morandserge diseaseecologyofrickettsialspeciesadatascienceapproach
AT chaisirikittipong diseaseecologyofrickettsialspeciesadatascienceapproach
AT kritiyakananamika diseaseecologyofrickettsialspeciesadatascienceapproach
AT kumlertrawadee diseaseecologyofrickettsialspeciesadatascienceapproach