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dc.contributor.authorHu, Wenbiao
dc.contributor.authorTong, Shilu
dc.contributor.authorMengersen, Kerrie
dc.contributor.authorConnell, Des
dc.date.accessioned2017-05-03T12:46:24Z
dc.date.available2017-05-03T12:46:24Z
dc.date.issued2007
dc.identifier.issn1047-2797
dc.identifier.doi10.1016/j.annepidem.2007.03.020
dc.identifier.urihttp://hdl.handle.net/10072/18660
dc.description.abstractPurpose 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 1àmaximum 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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom679
dc.relation.ispartofpageto688
dc.relation.ispartofissue9
dc.relation.ispartofjournalAnnals of Epidemiology
dc.relation.ispartofvolume17
dc.rights.retentionY
dc.subject.fieldofresearchBiomedical and clinical sciences
dc.subject.fieldofresearchHistory, heritage and archaeology
dc.subject.fieldofresearchcode32
dc.subject.fieldofresearchcode43
dc.titleWeather variability and the incidence of cryptosporidiosis: Comparison of time series Poisson regression and SARIMA models
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2015-02-06T01:35:40Z
gro.hasfulltextNo Full Text
gro.griffith.authorConnell, Des W.


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