  Dengue is a viral vector-borne infectious disease that affects many countries
worldwide, infecting around 390 million people per year. The main outbreaks
occur in subtropical and tropical countries. We study here the influence of
climate on dengue in Natal (2016-2019), Brazil, Iquitos (2001-2012), Peru, and
Barranquilla (2011-2016), Colombia. For the analysis and simulations, we apply
Machine Learning (ML) techniques, especially the Random Forest (RF) algorithm.
In addition, regarding a feature in the ML technique, we analyze three
possibilities: only dengue cases (D); climate and dengue cases (CD); humidity
and dengue cases (HD). Depending on the city, our results show that the climate
data can improve or not the forecast. For instance, for Natal, D induces a
better forecast. For Iquitos, it is better to use CD. Nonetheless, for
Barranquilla, the forecast is better, when we include cases and humidity data.
For Natal, when we use more than 64\% and less than 80\% of the time series for
training, we obtain results with correlation coefficients ($r$) among 0.917 and
0.949 and mean absolute errors (MAE) among 57.783 and 71.768 for the D case in
forecasting. The optimal range for Iquitos is obtained when 79\% up to 88\% of
the time series is considered for training. For this case, the best case is CD,
having a minimum $r$ equal to 0.850 and maximum 0.887, while values of MAE
oscillate among 2.780 and 4.156. For Barranquilla, the optimal range occurs
between 72\% until 82\% of length training. In this case, the better approach
is HD, where the measures exhibit a minimum $r$ equal to 0.942 and a maximum
0.953, while the minimum and maximum MAE vary between 6.085 and 6.669. We show
that the forecast of dengue cases is a challenging problem and climate
variables do not always help. However, when we include the mentioned climate
variables, the most important one is humidity.
