Disease Ecology of Rickettsial Species: A Data Science Approach
1
CNRS ISEM—CIRAD ASTRE—Montpellier University, 34090 Montpellier, France
2
Faculty of Veterinary Technology, Kasetsart University, Bangkok 10900, Thailand
3
Department of Helminthology, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
4
The Office of Disease Prevention and Control 12, Songkhla Province (ODPC12), Department of Disease Control, Ministry of Public Health, Songkhla 90000, Thailand
*
Author to whom correspondence should be addressed.
Trop. Med. Infect. Dis. 2020, 5(2), 64; https://doi.org/10.3390/tropicalmed5020064
Received: 3 March 2020
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Revised: 13 April 2020
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Accepted: 20 April 2020
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Published: 27 April 2020
(This article belongs to the Special Issue Selected Papers from The 2nd Asia Pacific Rickettsia Conference)
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.
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Keywords:
Rickettsia; Orientia; Ehrlichia; Anaplasma; rickettsial diseases; ticks; mammals; scrub typhus; disease ecology; network analysis; data science; EID2
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MDPI and ACS Style
Morand, S.; Chaisiri, K.; Kritiyakan, A.; Kumlert, R. Disease Ecology of Rickettsial Species: A Data Science Approach. Trop. Med. Infect. Dis. 2020, 5, 64. https://doi.org/10.3390/tropicalmed5020064
AMA Style
Morand S, Chaisiri K, Kritiyakan A, Kumlert R. Disease Ecology of Rickettsial Species: A Data Science Approach. Tropical Medicine and Infectious Disease. 2020; 5(2):64. https://doi.org/10.3390/tropicalmed5020064
Chicago/Turabian StyleMorand, Serge, Kittipong Chaisiri, Anamika Kritiyakan, and Rawadee Kumlert. 2020. "Disease Ecology of Rickettsial Species: A Data Science Approach" Tropical Medicine and Infectious Disease 5, no. 2: 64. https://doi.org/10.3390/tropicalmed5020064
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