• Corpus ID: 219305109

Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil

@article{Kempfert2020TimeSM,
  title={Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil},
  author={Katherine Kempfert and Kaitlyn Martinez and Amir S. Siraj and Jessica Conrad and Geoffrey Fairchild and Amanda Ziemann and Nidhi Parikh and Dave Osthus and Nicholas Generous and Sara Y. Del Valle and Carrie A. Manore},
  journal={arXiv: Applications},
  year={2020}
}
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… 

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

TLDR
A framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset is presented.

Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil

TLDR
A methodological framework to assess and compare dengue incidence estimates at the city level and evaluate the performance of a collection of models on 20 different cities in Brazil finds that real-time internet search data are the strongest predictors of Dengue incidence.

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