Computational methods applied to syphilis

Where are we, and where are we going

Bibliographic Data

ID22073459
AuthorsGabriela Albuquerque (0000-0002-0212-6868, Universidade Federal do Rio Grande do Norte, corresponding author), Felipe Fernandes (0000-0002-7627-1873, Universidade Federal do Rio Grande do Norte), Ingridy M P Barbalho (0000-0001-5696-4114, Universidade Federal do Rio Grande do Norte), Daniele Montenegro Da Silva Barros (0000-0001-5037-1545, Universidade Federal do Rio Grande do Norte), Philippi Sedir Grilo De Morais (0000-0002-0636-7562, Universidade Federal do Rio Grande do Norte), Antonio Higor Freire de Morais (0000-0002-5921-6696, Instituto Federal do Rio Grande do Norte), Marquiony Marques Dos Santos (0000-0001-5812-4004, Universidade Federal do Rio Grande do Norte), Leonardo J Galvão-Lima (0000-0002-4860-9734, Universidade Federal do Rio Grande do Norte), Ana Isabela L Sales-Moioli (0000-0001-9212-2344, Universidade Federal do Rio Grande do Norte), João Paulo Queiroz dos Santos (0000-0002-9130-7723, Instituto Federal do Rio Grande do Norte), Paulo Gil (0000-0003-0937-4044, University of Coimbra), J Henriques (0000-0003-4622-474X, University of Coimbra), César Teixeira (0000-0001-9396-1211, University of Coimbra), Tânia Stolze Lima (0000-0001-8276-4124, Universidade Federal do Rio Grande do Norte), Thaisa Santos Lima, Karilany Dantas Coutinho (0000-0002-2051-8611, Universidade Federal do Rio Grande do Norte), Talita K B Pinto (Universidade Federal do Rio Grande do Norte), Ricardo Alexsandro De Medeiros Valentim (0000-0002-9216-8593, Universidade Federal do Rio Grande do Norte)
Year2023
Volume11
Pages1201725-1201725
Publication date2023-08-23
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1201725
PMID37680278
OpenAlexW4386097375
LanguageEN
References cited64

Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions

Data science · Family medicine · Psychological intervention · Public health · Syphilis · Computer Science · HIV, Drug Use, Sexual Risk · Medicine · Reproductive tract infections research · Syphilis Diagnosis and Treatment · Artificial Intelligence

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