  Vector-borne diseases cause more than 1 million deaths annually. Estimates of
epidemic risk at high spatial resolutions can enable effective public health
interventions. Our goal is to identify the risk of importation of such diseases
into vulnerable cities at the granularity of neighborhoods. Conventional models
cannot achieve such spatial resolution, especially in real-time. Besides, they
lack real-time data on demographic heterogeneity, which is vital for accurate
risk estimation. Social media, such as Twitter, promise data from which
demographic and spatial information could be inferred in real-time. On the
other hand, such data can be noisy and inaccurate. Our novel approach leverages
Twitter data, using machine learning techniques at multiple spatial scales to
overcome its limitations, to deliver results at the desired resolution. We
validate our method against the Zika outbreak in Florida in 2016. Our main
contribution lies in proposing a novel approach that uses machine learning on
social media data to identify the risk of vector-borne disease importation at a
sufficiently fine spatial resolution to permit effective intervention. This
will lead to a new generation of epidemic risk assessment models, promising to
transform public health by identifying specific locations for targeted
intervention.
