  Predicting an infectious disease can help reduce its impact by advising
public health interventions and personal preventive measures. Novel data
streams, such as Internet and social media data, have recently been reported to
benefit infectious disease prediction. As a case study of dengue in Brazil, we
have combined multiple traditional and non-traditional, heterogeneous data
streams (satellite imagery, Internet, weather, and clinical surveillance data)
across its 27 states on a weekly basis over seven years. For each state, we
nowcast dengue based on several time series models, which vary in complexity
and inclusion of exogenous data. The top-performing model varies by state,
motivating our consideration of ensemble approaches to automatically combine
these models for better outcomes at the state level. Model comparisons suggest
that predictions often improve with the addition of exogenous data, although
similar performance can be attained by including only one exogenous data stream
(either weather data or the novel satellite data) rather than combining all of
them. Our results demonstrate that Brazil can be nowcasted at the state level
with high accuracy and confidence, inform the utility of each individual data
stream, and reveal potential geographic contributors to predictive performance.
Our work can be extended to other spatial levels of Brazil, vector-borne
diseases, and countries, so that the spread of infectious disease can be more
effectively curbed.
