Abstract
BackgroundIt remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia.MethodsData on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.ResultsThe results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models.ConclusionsThe results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.
Highlights
It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease
We considered a suite of three spatiotemporal classification and regression tree (CART) models: 1) fitting a tree to data categorised as binary: incidence/no incidence; 2) fitting a tree to just the incidences
About 45% of cryptosporidiosis cases occurred in children under 4 year old in 2001
Summary
It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. Cryptosporidiosis can be transmitted via contaminated food, contact between people, or contact between people and animals It is considered a drinking-waterborne disease because the largest outbreaks of Spatiotemporal analyses of disease have played a major role in environmental epidemiology. Polyclass and generalised linear models have been widely used to study relationships between diseases and various environmental risk factors These statistical techniques perform relatively poorly with high dimensionality in spatiotemporal context [5,6,7]. Infectious disease incidence data at finer spatial scales often have a preponderance of zero counts In this case, the use of the conventionally employed standard statistical models may result in poor estimates and prediction [9,10,11]. The integrated use of spatial statistics and CARTs at a variety of spatial scales has provided new insights into ecology and environmental epidemiology [13,14,15]
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