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Analysis of significant factors for dengue fever incidence prediction

Overview of attention for article published in BMC Bioinformatics, April 2016
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Title
Analysis of significant factors for dengue fever incidence prediction
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1034-5
Pubmed ID
Authors

Padet Siriyasatien, Atchara Phumee, Phatsavee Ongruk, Katechan Jampachaisri, Kraisak Kesorn

Abstract

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
United States 2 1%
Unknown 136 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 27 19%
Researcher 23 16%
Student > Ph. D. Student 22 16%
Student > Bachelor 15 11%
Professor > Associate Professor 7 5%
Other 23 16%
Unknown 23 16%
Readers by discipline Count As %
Computer Science 28 20%
Medicine and Dentistry 26 19%
Agricultural and Biological Sciences 13 9%
Nursing and Health Professions 8 6%
Biochemistry, Genetics and Molecular Biology 8 6%
Other 27 19%
Unknown 30 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 April 2016.
All research outputs
#5,726,411
of 7,565,447 outputs
Outputs from BMC Bioinformatics
#2,847
of 3,404 outputs
Outputs of similar age
#172,312
of 244,750 outputs
Outputs of similar age from BMC Bioinformatics
#101
of 108 outputs
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