  In 2015 the US federal government sponsored a dengue forecasting competition
using historical case data from Iquitos, Peru and San Juan, Puerto Rico.
Competitors were evaluated on several aspects of out-of-sample forecasts
including the targets of peak week, peak incidence during that week and total
season incidence across each of several seasons. Our team was one of the top
performers of that competition, outperforming all other teams in multiple
targets/locals. In this paper we report on our methodology, a large component
of which, surprisingly, ignores the known biology of epidemics at large---in
particular relationships between dengue transmission and environmental
factors---and instead relies on flexible nonparametric nonlinear Gaussian
process (GP) regression fits that "memorize" the trajectories of past seasons,
and then "match" the dynamics of the unfolding season to past ones in
real-time. Our phenomenological approach has advantages in situations where
disease dynamics are less well understood, e.g., at sites with shorter
histories of disease (such as Iquitos), or where measurements and forecasts of
ancillary covariates like precipitation are unavailable and/or where the
strength of association with cases are as yet unknown. In particular, we show
that the GP approach generally outperforms a more classical generalized linear
(autoregressive) model (GLM) that we developed to utilize abundant covariate
information. We illustrate variations of our method(s) on the two benchmark
locales alongside a full summary of results submitted by other contest
competitors.
