Weather variability and the incidence of cryptosporidiosis: Comparison of time series Poisson regression and SARIMA models

被引:56
|
作者
Hu, Wenbiao
Tong, Shilu [1 ]
Mengersen, Kerrie
Connell, Des
机构
[1] Queensland Univ Technol, Sch Publ Hlth, Ctr Hlth Res, Kelvin Grove, Qld 4059, Australia
[2] Queensland Univ Technol, Sch Publ Hlth, Sch Math & Phys Sci, Kelvin Grove, Qld 4059, Australia
[3] Griffith Univ, Sch Publ Hlth, Nathan, Qld 4111, Australia
基金
英国医学研究理事会;
关键词
cryptosporidiosis; Poisson regression; SARIMA; time series; weather;
D O I
10.1016/j.annepidem.2007.03.020
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
PURPOSE: Few studies have examined the relationship between weather variables and cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of cryptosporidiosis and explores the possibility of developing an empirical forecast system. METHODS: Data on weather variables, notified cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of cryptosporidiosis. RESULTS: Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of I and 3 months were significantly associated with cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1 degrees C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): -12.53; Poisson regression: RMSE: 0.54, AIC: -2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted. CONCLUSIONS: The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of cryptosporidiosis. (c) 2007 Elsevier Inc. All rights reserved.
引用
收藏
页码:679 / 688
页数:10
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