PDF(Pubmed)
Abstract：
The World Health Organization (WHO) 2030 Roadmap aims to eliminate schistosomiasis as a public health issue, targeting reductions in the heavy intensity of infections. Previous studies, however, have predominantly used prevalence as the primary indicator of schistosomiasis. We introduce several machine learning (ML) algorithms to predict infection intensity categories, using morbidity prevalence, with the aim of assessing the elimination of schistosomiasis in Africa, as outlined by the WHO. We obtained morbidity prevalence and infection intensity data from the Expanded Special Project to Eliminate Neglected Tropical Diseases, which spans 12 countries in sub-Saharan Africa. We then used a series of ML algorithms to predict the prevalence of infection intensity categories for Schistosoma haematobium and Schistosoma mansoni, with morbidity prevalence and several relevant environmental and demographic covariates from remote-sensing sources. The optimal model had high accuracy and stability; it achieved a mean absolute error (MAE) of 0.02, a root mean square error (RMSE) of 0.05, and a coefficient of determination (R2) of 0.84 in predicting heavy-intensity prevalence for S. mansoni; and an MAE of 0.02, an RMSE of 0.04, and an R2 value of 0.81 for S. haematobium. Based on this optimal model, we found that most areas in the surveyed countries have not achieved the target of the WHO road map for 2030. The ML algorithms used in our analysis showed a high overall predictive power in estimating infection intensity for each species, and our methods provided a low-cost, effective approach to evaluating the disease target in Africa set in the WHO road map for 2030.
摘要：
世界卫生组织（WHO）2030年路线图旨在消除血吸虫病作为一个公共卫生问题，靶向减少严重的感染。以前的研究，然而,主要使用流行率作为血吸虫病的主要指标。我们介绍了几种机器学习（ML）算法来预测感染强度类别，使用发病率，为了评估非洲血吸虫病的消除情况，正如世卫组织概述的那样。我们从扩大的消除被忽视的热带病特别项目中获得了发病率和感染强度数据，横跨撒哈拉以南非洲的12个国家。然后，我们使用了一系列ML算法来预测血吸虫和曼氏血吸虫的感染强度类别的患病率，具有发病率和遥感来源的几个相关环境和人口协变量。最佳模型具有很高的准确性和稳定性；在预测曼氏病重强度患病率时，它的平均绝对误差（MAE）为0.02，均方根误差（RMSE）为0.05，确定系数（R2）为0.84；血链球菌的MAE为0.02，RMSE为0.04，R2值为0.81。基于这个最优模型，我们发现,在接受调查的国家中,大多数地区尚未实现世卫组织2030年路线图的目标.我们分析中使用的ML算法在估计每个物种的感染强度方面显示出较高的总体预测能力，我们的方法提供了一种低成本的，世卫组织2030年路线图中设定的评估非洲疾病目标的有效方法。
