  Leptospiroris, the infectious disease caused by the spirochete bacteria
Leptospira interrogans, constitutes an important public health problem all over
the world. In Argentina, some regions present climate and geographic
characteristics that favors the habitat of the bacteria Leptospira, whose
survival strongly depends on climatic factors. For this reason, regional public
health systems should include, as a main factor, the incidence of the disease
in order to improve the prediction of potential outbreaks, helping to stop or
delay the virus transmission. The classic methods used to perform this kind of
predictions are based in autoregressive time series tools which, as it is well
known, perform poorly when the data do not meet their requirements. Recently,
several nonparametric methods have been introduced to deal with those problems.
In this work, we compare a semiparametric method, called Semi-Functional
Partial Linear Regression (SFPLR) with the classic ARIMA and a new alternative
ARIMAX, in order to select the best predictive tool for the incidence of
leptospirosis in the Argentinian Litoral region. In particular, SFPLR and
ARIMAX are methods that allow the use of (hydrometeorological) covariables
which could improve the prediction of outbreaks of leptospirosis.

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