Dengue Prediction Through Machine Learning and Deep Learning: A Scoping Review Protocol.

Abstract

Background: Dengue is an endemic disease caused by the DENV virus. There are four types of serology for this virus (DENV1, DENV2, DENV3 e DENV4). All of these variations can cause the disease and, once infected with one type, the patient is not immune against other serologies. Due to the particularity of the virus serology, as well as the ease of reproduction of the transmitting mosquito, approximately 4.3 million people suffered from this disease in 2019. Although it is not a new disease, there is still no effective vaccine against the virus. The best form of combat is prevention against mosquito proliferation. In this sense, machine learning and deep learning techniques have been used to predict dengue cases. In this work we show a scope review to clarify how it is possible to predict dengue cases through machine and deep learning.Methods: This scope review will follow the methodology defined in the article “Scoping studies: advancing the methodology”. The methodology consists of six phases. We chose to use only the mandatory ones: 1 - Identify the research question, 2 - Identify the relevant studies, 3 - Select the studies, 4 - Map the data and 5 - Compile, summarize and make the report. The main research question is to verify the feasibility of using machine learning and deep learning in the prediction of dengue cases. Derived from this question, the machine learning and deep learning techniques used will be investigated, where the studies are carried out, which data are being used, how the models are validated and which produce better results. The review used electronic databases: Scopus Document Search, IEEE Xplore Digital Library, PubMed, ACM Digital Library, and Web of Science.Results: After completing this study, a technical-scientific opinion was created and the suggested protocol was executed. As a result of the execution, 301 papers were selected and 14 approved.Conclusions: We can prove the effectiveness of using Machine Learning and Deep Learning techniques to predict dengue cases. Systematic Review registrations: Submitted on October 16,2020 Open Science Framework

A systematic review of dengue outbreak prediction models: Current scenario and future directions

“…Batista et al confirmed superiority with ML techniques demonstrating a lower error rate compared to the conventional statistics-based model in predicting dengue cases. In the age of big data, this technique can leverage data availability and in addition to being non-parametric in nature, can also provide some leeway in terms of strict assumption [86]. Random forest, neural networks, gradient boosting and support vector algorithms are notable subsets of machine learning algorithms, which have made significant contributions to several areas of public health, particularly in the forecasting of infectious diseases like malaria [87] and COVID-19 [88], and may have similar utility for making dengue outbreak predictions.…”

A systematic review of dengue outbreak prediction models: current scenario and future directions

“…In the age of big data, this technique can leverage data availability and in addition to being non-parametric in nature, can also provide some leeway in terms of strict assumption. 72 Random forest, neural networks, gradient boosting and support vector algorithms are notable subsets of machine learning algorithms, which have made significant contributions to several areas of public health, particularly in the forecasting of infectious diseases like malaria 73 and COVID-19 74 , and may have similar utility for making dengue outbreak predictions.…”