• Corpus ID: 219305109

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

  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},
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

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

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.



Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors

Temperature improves dengue outbreaks forecasts better than humidity and rainfall, and SARIMA models using climatic data as independent variables could be easily incorporated into an early (3 months-ahead) and reliably monitoring system of d Dengue outbreaks.

A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data

A novel prediction method utilizing Fuzzy Association Rule Mining to extract relationships between clinical, meteorological, climatic, and socio-political data from Peru has the potential to be extended to other environmentally influenced infections.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

A framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting d Dengue incidence in Mexico found short-term and seasonal autocorrelation were key to improving short- term and long-term forecasts.

Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

Background: With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170

The development of an early warning system for climate‐sensitive disease risk with a focus on dengue epidemics in Southeast Brazil

A thorough evaluation and validation of model performance is conducted using out-of-sample predictions and demonstrates considerable improvement over a model that mirrors current surveillance practice, and a novel visualisation technique is proposed to map ternary probabilistic forecasts of dengue risk.

Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil

When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% forThe forecast model compared to 33% for thenull model.

Intra- and Interseasonal Autoregressive Prediction of Dengue Outbreaks Using Local Weather and Regional Climate for a Tropical Environment in Colombia

Two environment-based, multivariate, autoregressive forecast models are developed that allow d Dengue outbreaks to be anticipated from 2 weeks to 6 months in advance and have the potential to enhance existing dengue early warning systems, ultimately supporting public health decisions on the timing and scale of vector control efforts.

Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data

Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak of Zika virus and show the predictive power of these data and a dynamic multivariable approach.

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance

The results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons.

Developing a dengue forecast model using machine learning: A case study in China

The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study and had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China.