Publication: Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses
Issued Date
2015-01-01
Resource Type
ISSN
17486718
1748670X
1748670X
Other identifier(s)
2-s2.0-84948799649
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Mahidol University
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SCOPUS
Bibliographic Citation
Computational and Mathematical Methods in Medicine. Vol.2015, (2015)
Suggested Citation
Sudarat Chadsuthi, Sopon Iamsirithaworn, Wannapong Triampo, Charin Modchang Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses. Computational and Mathematical Methods in Medicine. Vol.2015, (2015). doi:10.1155/2015/436495 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/35618
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Title
Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses
Abstract
© 2015 Sudarat Chadsuthi et al. 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.
