Climate variations and salmonellosis transmission in Adelaide,South Australia: a comparison between regression models |
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Authors: | Ying Zhang Peng Bi Janet Hiller |
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Institution: | (1) Discipline of Public Health, University of Adelaide, Adelaide, SA, Australia;(2) Discipline of Public Health, University of Adelaide, Level 9, 10 Pulteney Street, Adelaide, 5005, SA, Australia |
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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. |
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Keywords: | Climate Multiple linear regression Poisson Salmonellosis SARIMA Time-series |
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