Dengue models based on machine learning techniques: A systematic literature review

Highlights

• Dengue models based on machine learning techniques are reviewed.

• Logistic models are the most used for the diagnosis of dengue.

• The spatial-temporal analysis is the most widely used approach to predicting dengue.

• This paper identifies challenges and future works on dengue modeling.

Abstract

Background

Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years.

Methods

Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified.

Results

Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%.

Conclusions

We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.

Introduction

Dengue is a vector-borne disease, with high importance in public health [1]. This disease is widely distributed worldwide; especially, in tropical and subtropical areas [2]. The disease is produced by an arbovirus (DENV) that receives the same name. To date, four virus serotypes have been identified: DENV-1, DENV-2, DENV-3 and DENV-4 [3]. The infection is transmitted to humans by the bite of mosquitoes of the genus Aedes, mainly A. aegypti and A. albopictus [4].

In 1997, the World Health Organization (WHO) classified the disease like dengue fever and dengue hemorrhagic fever [5]. A new classification was proposed in 2009, which was based on the severity level of the disease: non-severe dengue (with or without warning signs) and severe dengue (SD). This last includes the dengue shock syndrome (DSS) [6]. According to the WHO, more than 350 million dengue virus infections occur annually worldwide. In addition, 20,000 deaths related to dengue in the same period of time [7].

Dengue has been the subject of various studies worldwide. Its high prevalence in tropical and subtropical regions of the world has generated interest in its diagnosis, treatment and control. Different systematic literature reviews (SLRs) have been carried out of dengue. Most of them have been focused on the evaluation of molecules for the generation of vaccines, control of transmission, epidemiology and development of rapid-detection tests. In what follows, we briefly explain previous SLRs.

Several SLRs have described the epidemiology of dengue. Jing and Wang [8] showed the epidemiology of dengue according to its geographical and temporal distribution. Besides, Jing and Wang evaluated risk factors for transmission and control of dengue. Alhaeli et al. [9] conducted a review of the epidemiology of dengue in Saudi Arabia, where environmental conditions are extreme. Other reviews on the epidemiology of dengue have been carried out in different countries, such as Pakistan [10], Thailand [11], Malaysia [12], Philippines [13], Mexico [14] and Brazil [15]. Finally, Villar et al. [1] conducted a SLR of the epidemiological trends of dengue, in Colombia, for over 12 years (2000−2011).

Another group of SLRs has focused on the production of rapid-detection tests and vaccines against the virus. For instance, Lim et al. [16] and Luo et al. [17] conducted reviews and meta-analyses to assess the economic impact of rapid-screening tests. Reviews have also been conducted to identify the latest economic studies of dengue vaccination [18,19]. For the development of dengue vaccines, it has been evaluated the immunogenicity, safety and efficacy of the vaccine [20,21,22].

In recent years, with the emergence of machine learning and the increase in data generation, computational methods have been developed for the prediction and evaluation of disease-transmission dynamics. This has generated interest for SLRs on this subject, to know the latest developments and opportunities in this domain. As an example, Louis et al. [23] developed an SLR of dengue to identify the main modeling approaches of the disease risk. Another SLR on computational methods was conducted by Naish et al. [24], focusing on quantitative modeling with respect to climate change. Andraud et al. [25] conducted a review of deterministic models of dengue transmission to identify features for future models. Finally, Lourenço et al. [26] published a review of the challenges in dengue research from a computational perspective. The authors focused on real-time data collection, genetic analysis and integrative modeling approaches. Particularly, integrative-modeling approaches simulate the epidemiology and molecular evolution of the virus.

We present a review of three modeling approaches of dengue: diagnostic, epidemic and intervention. The goal is to present the development of machine learning models for these contexts. The first approach is to determine whether a patient has dengue or any of its variants. The second is to analyze the population-level dengue epidemic; in addition, to study morbidity and mortality rates. The third is to analyze the impact of interventions to mitigate epidemics of dengue. To date, there is no SLR that studies these three aspects related to the disease together. In addition, it is the first SLR to focus on models to evaluate the impact of interventions to mitigate dengue epidemics. Finally, this SLR establishes the state-of-the-art in these approaches, and, additionally, defines new challenges and opportunities for future research. The objectives of this SLR are:

• To collect and describe machine learning models for dengue.

• To visualize challenges for future work in dengue modeling.

The present document is structured as follows: Section 2 describes search and selection process of relevant articles; Section 3 describes general results of the research; Section 4 discusses the papers, as well as the challenges and opportunities for research on dengue modeling for diagnosis, epidemics and interventions to control dengue. The last section shows the conclusions, with a description of the works that would be a priority to develop in this research domain.