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Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models.
Ann Epidemiol. 2007 Sep; 17(9):679-88.AE

Abstract

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 1 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.

Authors+Show Affiliations

Centre for Health Research, School of Public Health, Queensland University of Technology, Brisbane, Queensland, Australia.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

17604645

Citation

Hu, Wenbiao, et al. "Weather Variability and the Incidence of Cryptosporidiosis: Comparison of Time Series Poisson Regression and SARIMA Models." Annals of Epidemiology, vol. 17, no. 9, 2007, pp. 679-88.
Hu W, Tong S, Mengersen K, et al. Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models. Ann Epidemiol. 2007;17(9):679-88.
Hu, W., Tong, S., Mengersen, K., & Connell, D. (2007). Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models. Annals of Epidemiology, 17(9), 679-88.
Hu W, et al. Weather Variability and the Incidence of Cryptosporidiosis: Comparison of Time Series Poisson Regression and SARIMA Models. Ann Epidemiol. 2007;17(9):679-88. PubMed PMID: 17604645.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models. AU - Hu,Wenbiao, AU - Tong,Shilu, AU - Mengersen,Kerrie, AU - Connell,Des, Y1 - 2007/06/28/ PY - 2006/04/24/received PY - 2007/02/15/revised PY - 2007/03/12/accepted PY - 2007/7/3/pubmed PY - 2007/12/6/medline PY - 2007/7/3/entrez SP - 679 EP - 88 JF - Annals of epidemiology JO - Ann Epidemiol VL - 17 IS - 9 N2 - 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 1 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. SN - 1047-2797 UR - https://www.unboundmedicine.com/medline/citation/17604645/Weather_variability_and_the_incidence_of_cryptosporidiosis:_comparison_of_time_series_poisson_regression_and_SARIMA_models_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1047-2797(07)00154-8 DB - PRIME DP - Unbound Medicine ER -