Development of Temporal Modeling for Prediction of Dengue Infection in Northeastern Thailand

Overview

Journal Asian Pac J Trop Med

Specialties Infectious Diseases
Tropical Medicine

Date 2012 Feb 7

PMID 22305794

Citations 19

Authors

Siriwan Wongkoon

Mullica Jaroensutasinee

Krisanadej Jaroensutasinee

Affiliations

Soon will be listed here.

Abstract

Objective: To model the monthly number of dengue fever cases in northeastern Thailand using time series analysis.

Methods: Autoregressive Integrated Moving Average (ARIMA) models have been developed on the monthly data collected from January 1981 to December 2006 and validated using the data from January 2007 to April 2010.

Results: The ARIMA (3,1,4) model has been found as the most suitable model with the least Akaike Information Criterion (AIC) of 14.060 and Mean Absolute Percent Error (MAPE) of 7.000. The model was further validated by the Portmanteau test with no significant autocorrelation between residuals at different lag times.

Conclusions: Early warning based on the data in the previous months could assist in improving vector control, community intervention, and personal protection.

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Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction.

Zaw W, Lin Z, Ko Ko J, Rotejanaprasert C, Pantanilla N, Ebener S PLoS Negl Trop Dis. 2023; 17(6):e0011331.

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Temporal Variations and Spatial Clusters of Dengue in Thailand: Longitudinal Study before and during the Coronavirus Disease (COVID-19) Pandemic.

Saita S, Maeakhian S, Silawan T Trop Med Infect Dis. 2022; 7(8).

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Temporal trends and prediction of bovine tuberculosis: a time series analysis in the North-East of Iran.

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Identification of significant climatic risk factors and machine learning models in dengue outbreak prediction.

Yavari Nejad F, Varathan K BMC Med Inform Decis Mak. 2021; 21(1):141.

PMID: 33931058 PMC: 8086151. DOI: 10.1186/s12911-021-01493-y.