School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
BMC Infect Dis. 2010 Oct 28;10:311. doi: 10.1186/1471-2334-10-311.
It 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.
Data 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.
The 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.
The 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.
目前尚不清楚是否有可能建立隐孢子虫病的时空流行预测模型。本文研究了社会经济和气象因素对隐孢子虫病的影响，并利用澳大利亚昆士兰州的社会经济和气象数据探讨了建立此类模型的可能性。
气象变量、通知的隐孢子虫病病例和社会经济因素的数据分别由澳大利亚气象局、昆士兰州卫生部和澳大利亚统计局提供。采用三阶段时空分类回归树（CART）模型，研究澳大利亚昆士兰州社会经济和气象因素与隐孢子虫病月发病率之间的关系。时空 CART 模型用于预测澳大利亚昆士兰州隐孢子虫病的暴发。
分类树模型（以发病率定义为存在/不存在）的结果表明，如果一个地方政府区域（LGA）的社会经济指数（SEIFA）超过 1021，则该区域发生隐孢子虫病的可能性为 87%；而回归树模型（基于非零发病率）的结果表明，当 SEIFA 在 892 至 945 之间，且温度超过 32°C 时，隐孢子虫病的相对风险（RR）为 3.9（平均发病率：390.6/100,000，标准差（SD）：310.5），与隐孢子虫病的月平均发病率相比。当 SEIFA 小于 892 时，隐孢子虫病的 RR 为 4.3（平均发病率：426.8/100,000，SD：319.2）。根据时空 CART 模型的输出，制作了隐孢子虫病暴发的预测图。
本研究结果表明，基于社会经济和气象变量的时空 CART 模型可用于预测澳大利亚昆士兰州隐孢子虫病的暴发。
