Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies

With dengue becoming a major concern in tropical countries such as the Philippines, it is important that public health officials are able to accurately determine the presence and magnitude of dengue activity as quickly as possible to facilitate fast emergency response. The prevalence of massive streams of publicly available data from social media make this possible through infoveillance. Infoveillance involves observing and analyzing online interactions to gather health-related data for informing decisions on public health. In this paper, we present a public health agent model that performs dengue infoveillance using a gated recurrent neural network classification model incorporated with pre-trained word embeddings and cross-label frequency calculation. We setup the agent to work on the Philippine Twitter stream as its primary environment. Further, we evaluate the agents classification ability using a holdout set of human-labeled tweets. Afterwards, we run a historical simulation where the trained agent works with a stream of six months worth of tweets from the Philippines and we correlate its infoveillance results with actual dengue morbidity data of that time period. Experiments show that the agent is capable of accurately identifying dengue-related tweets with low loss. Moreover, we confirm that the agent model can be used for determining actual dengue activity and can serve as an early warning system with high confidence.