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Analysis of significant factors for dengue fever incidence prediction.
BMC Bioinformatics. 2016 Apr 16; 17:166.BB

Abstract

BACKGROUND

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.

RESULTS

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.

CONCLUSIONS

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.

Authors+Show Affiliations

Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand. Excellence Center for Emerging Infectious Disease, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, 10330, Thailand.Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand.Department of Mathematics, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand.Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand. kraisakk@nu.ac.th.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

27083696

Citation

Siriyasatien, Padet, et al. "Analysis of Significant Factors for Dengue Fever Incidence Prediction." BMC Bioinformatics, vol. 17, 2016, p. 166.
Siriyasatien P, Phumee A, Ongruk P, et al. Analysis of significant factors for dengue fever incidence prediction. BMC Bioinformatics. 2016;17:166.
Siriyasatien, P., Phumee, A., Ongruk, P., Jampachaisri, K., & Kesorn, K. (2016). Analysis of significant factors for dengue fever incidence prediction. BMC Bioinformatics, 17, 166. https://doi.org/10.1186/s12859-016-1034-5
Siriyasatien P, et al. Analysis of Significant Factors for Dengue Fever Incidence Prediction. BMC Bioinformatics. 2016 Apr 16;17:166. PubMed PMID: 27083696.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Analysis of significant factors for dengue fever incidence prediction. AU - Siriyasatien,Padet, AU - Phumee,Atchara, AU - Ongruk,Phatsavee, AU - Jampachaisri,Katechan, AU - Kesorn,Kraisak, Y1 - 2016/04/16/ PY - 2015/06/23/received PY - 2016/04/12/accepted PY - 2016/4/17/entrez PY - 2016/4/17/pubmed PY - 2016/12/15/medline KW - Climate factor analysis KW - Dengue hemorrhagic fever KW - Forecasting model KW - Multivariate poisson regression KW - Prediction model SP - 166 EP - 166 JF - BMC bioinformatics JO - BMC Bioinformatics VL - 17 N2 - BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. SN - 1471-2105 UR - https://www.unboundmedicine.com/medline/citation/27083696/Analysis_of_significant_factors_for_dengue_fever_incidence_prediction_ L2 - https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1034-5 DB - PRIME DP - Unbound Medicine ER -