JMIR Publications

ABSTRACT

Background:

Dengue fever, also known as “tropical flu”, is an infectious disease caused by the dengue virus, which most often occurs in tropical and subtropical regions. To combat this virus and prevent dengue epidemics in France, Santé publique France (SpFrance) has set up both individual and collective actions. In Martinique, the Programme de Surveillance, d'Alerte et de Gestion des Epidémies de dengue (PSAGE dengue), set up by SpFrance in 2006, coordinates these collective actions. It brings together a few local players involved in both clinical surveillance (general practitioners, Martinique University Hospital, laboratories, etc.) and vector surveillance (Centre de Démoustication et de Recherches Entomologiques - Lutte Antivectorielle; CEDRE-LAV).

Objective:

At present, there is a delay of several weeks between the increase in the number of dengue cases detected in the field and the declaration of the epidemic phase by the PSAGE. The main objective of this study is to use Machine Learning algorithms to predict dengue epidemics in Martinique.

Methods:

Different Machine Learning algorithms using heterogeneous real-life data (administrative and clinical data, laboratory data, Google Trends data and entomological data) were evaluated and compared on their ability to predict the 2019-2021 dengue epidemic.

Results:

The best-performing model was the random forest model (correlation at 0.933 [0.915 ;0.947]).

Conclusions:

This study highlighted the value of integrating real-life data into the surveillance and prediction of dengue epidemics in Martinique. The results show that the use of multiple data sources, such as clinical, entomological and Google Trends data, improves disease surveillance. Thus, the implementation of these different sources can contribute to the prediction of future epidemics.