Systematic Review of Dengue Detection and Diagnosis Methods from 2004 to 2023

Ramos-Cosi S.; Miñope-Anchaya E.; Sevilla D.; Alva-Mantari A. · International Journal of Engineering Trends and Technology (2023) · DOI: 10.14445/22315381/ijett-v71i12p225

Resumen

Dengue is caused by the bite of an infected mosquito, leading to symptoms in humans such as fever, muscle aches, and headaches. In 2019, the World Health Organization reported 5.2 billion dengue incidents. Thanks to technology, there are methods for detecting and diagnosing dengue within a certain number of days after being bitten. The current study aims to analyze different dengue detection methods through a systematic review from 2004 to 2023. We adopted the hybrid PRISMA methodology, depicted in a flowchart, for data processing in the Scopus database. Boolean algorithms were used for searching documents related to dengue, followed by data processing using the R language and Biblioshiny libraries. This yielded 28,801 documents (articles, conference papers, and books), constituting 74.42%, 3.94%, and 0.12% of the total, respectively, discussing dengue and its detection methods. As a result, it becomes evident that countries like the USA publish a substantial number of documents, around 7,000, related to dengue, primarily due to substantial funding. However, the study does not reveal significant technological advancements for evaluating dengue detection, posing a challenge to healthcare centers regarding early diagnosis for the population affected by this infection.

Instituciones: Pontificia Universidad Católica del Perú; Universidad de Ciencias y Humanidades