Introduction:

Machine learning methods allow to manipulate structured and unstructured data to build predictive models and support decision-making.

Objective:

To identify machine learning methods applied to predict the epidemiological behavior of vector-borne diseases using epidemiological surveillance data.

Methodology:

A literature search in EMBASE and PubMed, bibliometric analysis, and information synthesis were performed.

Results:

A total of 41 papers were selected, all of them were published in the last decade. The most frequent keyword was dengue. Most authors (88.3 %) participated in a research article. Sixteen machine learning methods were found, the most frequent being Artificial Neural Network, followed by Support Vector Machines.

Conclusions:

In the last decade there has been an increase in the number of articles that aim to predict the epidemiological behavior of vector-borne diseases using by means of various machine learning methods that incorporate time series of cases, climatological variables, and other sources of open data information.