  The performance of data-driven prediction models depends on the availability
of data samples for model training. A model that learns about dengue fever
incidence in a population uses historical data from that corresponding
location. Poor performance in prediction can result in places with inadequate
data. This work aims to enhance temporally limited dengue case data by
methodological addition of epidemically relevant data from nearby locations as
predictors (features). A novel framework is presented for windowing incidence
data and computing time-shifted correlation-based metrics to quantify feature
relevance. The framework ranks incidence data of adjacent locations around a
target location by combining the correlation metric with two other metrics:
spatial distance and local prevalence. Recurrent neural network-based
prediction models achieve up to 33.6% accuracy improvement on average using the
proposed method compared to using training data from the target location only.
These models achieved mean absolute error (MAE) values as low as 0.128 on [0,1]
normalized incidence data for a municipality with the highest dengue prevalence
in Brazil's Espirito Santo. When predicting cases aggregated over geographical
ecoregions, the models achieved accuracy improvements up to 16.5%, using only
6.5% of incidence data from ranked feature sets. The paper also includes two
techniques for windowing time series data: fixed-sized windows and outbreak
detection windows. Both of these techniques perform comparably, while the
window detection method uses less data for computations. The framework
presented in this paper is application-independent, and it could improve the
performances of prediction models where data from spatially adjacent locations
are available.
