The use of ZIP and CART to model cryptosporidiosis in relation to climatic variables |
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| Authors: | Wenbiao Hu Kerrie Mengersen Shiu-Yun Fu Shilu Tong |
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| Affiliation: | (1) School of Mathematical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, 4059, Australia;(2) School of Nursing, College of Medicine, Fu Jen Catholic University, Taipei Country, Taiwan;(3) Centre for Health Research, School of Public Health, Queensland University of Technology, Brisbane, Australia;(4) School of Population Health, University of Queensland, Brisbane, Australia |
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| Abstract: | This research assesses the potential impact of weekly weather variability on the incidence of cryptosporidiosis disease using time series zero-inflated Poisson (ZIP) and classification and regression tree (CART) models. 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, respectively. Both time series ZIP and CART models show a clear association between weather variables (maximum temperature, relative humidity, rainfall and wind speed) and cryptosporidiosis disease. The time series CART models indicated that, when weekly maximum temperature exceeded 31°C and relative humidity was less than 63%, the relative risk of cryptosporidiosis rose by 13.64 (expected morbidity: 39.4; 95% confidence interval: 30.9–47.9). These findings may have applications as a decision support tool in planning disease control and risk-management programs for cryptosporidiosis disease. |
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