  Exploiting the large amount of available data for addressing relevant social
problems has been one of the key challenges in data mining. Such efforts have
been recently named "data science for social good" and attracted the attention
of several researchers and institutions. We give a contribution in this
objective in this paper considering a difficult public health problem, the
timely monitoring of dengue epidemics in small geographical areas. We develop a
generative simple yet effective model to connect the fluctuations of disease
cases and disease-related Twitter posts. We considered a hidden Markov process
driving both, the fluctuations in dengue reported cases and the tweets issued
in each region. We add a stable but random source of tweets to represent the
posts when no disease cases are recorded. The model is learned through a Markov
chain Monte Carlo algorithm that produces the posterior distribution of the
relevant parameters. Using data from a significant number of large Brazilian
towns, we demonstrate empirically that our model is able to predict well the
next weeks of the disease counts using the tweets and disease cases jointly.
