  Due to a lack of treatments and universal vaccine, early forecasts of Dengue
are an important tool for disease control. Neural networks are powerful
predictive models that have made contributions to many areas of public health.
In this systematic review, we provide an introduction to the neural networks
relevant to Dengue forecasting and review their applications in the literature.
The objective is to help inform model design for future work. Following the
PRISMA guidelines, we conduct a systematic search of studies that use neural
networks to forecast Dengue in human populations. We summarize the relative
performance of neural networks and comparator models, model architectures and
hyper-parameters, as well as choices of input features. Nineteen papers were
included. Most studies implement shallow neural networks using historical
Dengue incidence and meteorological input features. Prediction horizons tend to
be short. Building on the strengths of neural networks, most studies use
granular observations at the city or sub-national level. Performance of neural
networks relative to comparators such as Support Vector Machines varies across
study contexts. The studies suggest that neural networks can provide good
predictions of Dengue and should be included in the set of candidate models.
The use of convolutional, recurrent, or deep networks is relatively unexplored
but offers promising avenues for further research, as does the use of a broader
set of input features such as social media or mobile phone data.
