Temporal Dengue Outbreak Prediction from Climatic Variables using Finite
Element Machine for Regression
Abstract
The global burden of dengue, a mosquito-borne viral infection,
has alarmingly increased in recent decades. The rise in disease
occurrence is mainly attributed to changes in the climate, human
ecology, globalization, and demography. In such a scenario, an accurate
prediction of a dengue outbreak is essential to reduce the morbidity
rate significantly. Therefore, this paper employs two classes of
autoregressive models for dengue forecasting and a recently proposed
approach called Finite Element Machine for Regression (FEMaR). Further,
it proposes a variant of the latter, namely FEMaR-KD, which allows the
exploration of k -approximate nearest neighbors to interpolate data
points based on k -neighborhood instead of the whole dataset. Such
models are built considering environmental parameters, which denote one
of the main determinants for infection occurrence. Finally, the proposed
models’ performance is assessed over two distinct datasets, considering
differing spatial scales and regions. Results show that FEMaR obtained
Mean Absolute Error up to 51% smaller than the autoregressive
models considering univariate scenarios and Root Mean Squared Error up
to 63% smaller regarding the univariate models.
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