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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 436495, 8 pages
http://dx.doi.org/10.1155/2015/436495
Research Article

Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses

1Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand
2Department of Disease Control, Ministry of Public Health, Tivanond Road, Nonthaburi 11000, Thailand
3Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
4Institute for Innovative Learning, Mahidol University, Nakhon Pathom 73170, Thailand
5Centre of Excellence in Mathematics (CHE), 328 Si Ayutthaya Road, Bangkok 10400, Thailand
6ThEP Center, CHE, 328 Si Ayutthaya Road, Bangkok 10400, Thailand

Received 27 July 2015; Revised 31 October 2015; Accepted 1 November 2015

Academic Editor: Chung-Min Liao

Copyright © 2015 Sudarat Chadsuthi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.