  Dengue fever is a virulent disease spreading over 100 tropical and
subtropical countries in Africa, the Americas, and Asia. This arboviral disease
affects around 400 million people globally, severely distressing the healthcare
systems. The unavailability of a specific drug and ready-to-use vaccine makes
the situation worse. Hence, policymakers must rely on early warning systems to
control intervention-related decisions. Forecasts routinely provide critical
information for dangerous epidemic events. However, the available forecasting
models (e.g., weather-driven mechanistic, statistical time series, and machine
learning models) lack a clear understanding of different components to improve
prediction accuracy and often provide unstable and unreliable forecasts. This
study proposes an ensemble wavelet neural network with exogenous factor(s)
(XEWNet) model that can produce reliable estimates for dengue outbreak
prediction for three geographical regions, namely San Juan, Iquitos, and
Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate
exogenous climate variable(s) confirmed by statistical causality tests in its
scalable framework. The proposed model is an integrated approach that uses
wavelet transformation into an ensemble neural network framework that helps in
generating more reliable long-term forecasts. The proposed XEWNet allows
complex non-linear relationships between the dengue incidence cases and
rainfall; however, mathematically interpretable, fast in execution, and easily
comprehensible. The proposal's competitiveness is measured using computational
experiments based on various statistical metrics and several statistical
comparison tests. In comparison with statistical, machine learning, and deep
learning methods, our proposed XEWNet performs better in 75% of the cases for
short-term and long-term forecasting of dengue incidence.
