• Corpus ID: 8773172

Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals

@article{Stolerman2016DataDrivenFO,
  title={Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals},
  author={Lucas M. Stolerman and Pedro D. Maia and J. Nathan Kutz},
  journal={arXiv: Quantitative Methods},
  year={2016}
}
Local climate conditions play a major role in the development of the mosquito population responsible for transmitting Dengue Fever. Since the {\em Aedes Aegypti} mosquito is also a primary vector for the recent Zika and Chikungunya epidemics across the Americas, a detailed monitoring of periods with favorable climate conditions for mosquito profusion may improve the timing of vector-control efforts and other urgent public health strategies. We apply dimensionality reduction techniques and… 

Tables from this paper

A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
TLDR
A novel machine learning dengue forecasting approach is presented, which identifies local patterns in weather and population susceptibility to make epidemic predictions at the city level in Brazil, months ahead of the occurrence of disease outbreaks.
Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach
TLDR
A novel machine learning dengue forecasting approach, which, dynamically in time and adaptively in space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city-level in Brazil, months ahead of the occurrence of disease outbreaks.
Incorporating human mobility data improves forecasts of Dengue fever in Thailand
TLDR
It is shown that long-distance connectivity is correlated with dengue incidence at forecasting horizons of up to three months, and that incorporating mobility data improves traditional time-series forecasting approaches.
Spatio-temporal characteristics of dengue outbreaks
TLDR
The results show that the characteristic correlation length of the epidemic is of the order of the system size, suggesting that factors such as citizen mobility may play a major role as a drive for spatial spreading of vector-borne diseases.
Novel Physical and Computer-Based methods for Adult Mosquito Pest Control and Monitoring
TLDR
The focus is on applications using imaging systems, acoustical detection, and lasers in combination with advanced signal analysis and processing for mosquito identification, killing, and monitoring.

References

SHOWING 1-10 OF 91 REFERENCES
Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators
TLDR
These findings indicate that outbreak resurgence can be modeled using a simple combination of climate indicators, which might be useful for anticipating public health actions to mitigate the effects of major outbreaks, particularly in areas where resources are limited and medical infrastructures are generally insufficient.
A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data
TLDR
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.
Regional response of dengue fever epidemics to interannual variation and related climate variability
Dengue is a major international public health concern and one of the most important vector-borne diseases. The purpose of this article is to investigate the association among temperature, rainfall,
Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico
TLDR
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.
Prediction of High Incidence of Dengue in the Philippines
TLDR
The Philippines dengue prediction models predicted high or low incidence of d Dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity.
Impact of human mobility on the emergence of dengue epidemics in Pakistan
TLDR
It is shown that an epidemiological model of dengue transmission in travelers, based on mobility data from ∼40 million mobile phone subscribers and climatic information, predicts the geographic spread and timing of epidemics throughout the country.
The global distribution and burden of dengue
TLDR
These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue and will help to guide improvements in disease control strategies using vaccine, drug and vector control methods, and in their economic evaluation.
The development of an early warning system for climate‐sensitive disease risk with a focus on dengue epidemics in Southeast Brazil
TLDR
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.
Forecast of Dengue Incidence Using Temperature and Rainfall
TLDR
It is demonstrated that models using temperature and rainfall could be simple, precise, and low cost tools for dengue forecasting which could be used to enhance decision making on the timing, scale of vector control operations, and utilization of limited resources.
...
...