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Type: Journal article
Title: Climate variations and salmonellosis transmission in Adelaide South Australia: a comparison between regression models
Author: Zhang, Y.
Bi, P.
Hiller, J.
Citation: International Journal of Biometeorology, 2008; 52(3):179-187
Publisher: Springer-Verlag
Issue Date: 2008
ISSN: 0020-7128
Statement of
Ying Zhang, Peng Bi and Janet Hiller
Abstract: This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990–2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.
Keywords: Climate; Multiple linear regression; Poisson; Salmonellosis; SARIMA; Time-series
Description: Published online: 11 July 2007
RMID: 0020070792
DOI: 10.1007/s00484-007-0109-4
Appears in Collections:Public Health publications
Environment Institute publications

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