目的 建立隐孢子虫感染率预测模型,预测广西壮族自治区（广西）宾阳县农村居民隐孢子虫感染率,为制定防控措施提供参考依据。方法 2014、2016、2017和2018年分别采用分层整群随机抽样的方法抽取宾阳县农村居民,收集符合条件居民的粪样进行隐孢子虫免疫试纸条检测,获得该县各年农村居民隐孢子虫感染率。根据宾阳县2014、2016、2017和2018年隐孢子虫感染率,建立非等间距灰色模型GM(1,1)以预测2019年和2020年该县农村居民隐孢子虫感染率。模型检验采用残差检验、关联度检验和后验差检验。结果 广西宾阳县2014、2016、2017和2018年农村居民隐孢子虫感染率分别为2.9%（57/2 000）、1.5%（15/1 030）、1.1%（11/1 027）和0.6%（6/1 004）。隐孢子虫感染率预测模型为：^（0）(k_{i + 1}) = -(1 - e^{0.289 1Δki + 1})e^{-0.289 1Δki + 1}。应用该模型获得2014、2016、2017和2018年宾阳县农村居民隐孢子虫感染率的拟合值分别为2.9%、1.5%、1.0%和0.7%,预测模型的绝对误差分别为0.000 0、-0.001 6、0.136 8和-0.121 4,相对误差分别为0.000 0、0.001 1、0.124 4和0.202 3。关联度r = 0.667 7,后验方差比C = 0.106 8,后验概率P = 1.00。2019和2020年宾阳县农村居民隐孢子虫感染率的预测值分别为0.5%和0.4%。结论 建立了隐孢子虫感染率的非等间距灰色模型GM(1,1),该模型预测精度较高,拟合效果较好。预测宾阳县农村居民隐孢子虫感染率呈下降趋势。

Objective To establish a prediction model for Cryptosporidium spp. infection in rural residents of Binyang County, Guangxi Zhuang Autonomous Region (Guangxi) so as to provide reference for the development of prevention and control measures. Methods In 2014, 2016, 2017 and 2018, the rural residents of Binyang County were selected by stratified cluster random sampling, and the fecal samples were collected from selected participants for detecting Cryptosporidium spp. infection using an immunological test strip method. The non-equidistant grey model GM(1,1) was established to predict the prevalence of Cryptosporidium spp. infection in 2019 and 2020 in this region based on the prevalence data collected from the survey in 2014, 2016, 2017 and 2018. The model effect was tested using residual test, correlation test and the posterior deviation test. Results The prevalence of Cryptosporidium spp. infection in the selected rural residents of Binyang County was 2.9% (57/2 000) in 2014, 1.5% (15/1 030) in 2016, 1.1% (11/1 027) in 2017 and 0.6% (6/1 004) in 2018. The prediction model for prevalence of Cryptosporidium spp. infection in this region was established accordingly as: ^（0）（k_{i + 1}） = -（1 - e^{0.289 1Δki + 1}）e^{-0.289 1Δki+ 1}. Based on this model the fitted prevalence of Cryptosporidium spp. infection in this region in 2014, 2016, 2017 and 2018 were 2.9%, 1.5%, 1.0% and 0.7%, respectively, which are closed correlated with the actual prevalence values (r = 0.667 7) with absolute errors of 0.000 0, -0.001 6, 0.136 8 and -0.121 4 and the relative errors of 0.000 0, 0.001 1, 0.124 4 and 0.202 3, respectively, with the posterior variance ratio C = 0.106 8 and the posterior probability P = 1.00. Based on this model, the prevalence of Cryptosporidium spp. infection in Binyang County are predicted as 0.5% in 2019 and 0.4% in 2020. Conclusion A non-equidistant grey model GM(1,1) is established to predict the prevalence of Cryptosporidium spp. infection. The model has high prediction accuracy and good fitting effect. The predicted results show that the infection rate of Cryptosporidium spp. in rural residents of Binyang County is decreasing.