A Robust and Non‑parametric Model for Prediction of Dengue Incidence

Abstract

Disease surveillance is essential not only for the prior detection
of outbreaks, but also for monitoring trends of the disease in the long
run. In this paper, we aim to build a tactical model for the surveillance of
dengue, in particular. Most existing models for dengue prediction exploit
its known relationships between climate and socio-demographic factors
with the incidence counts; however, they are not flexible enough to capture
the steep and sudden rise and fall of the incidence counts. This has
been the motivation for the methodology used in our paper. We build
a non-parametric, flexible, Gaussian process (GP) regression model
that relies on past dengue incidence counts and climate covariates,
and show that the GP model performs accurately, in comparison with
the other existing methodologies, thus proving to be a good tactical and
robust model for health authorities to plan their course of action.