Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China.
Am J Trop Med Hyg. 2024 May 21;111(1):73-79. doi: 10.4269/ajtmh.23-0751. Print 2024 Jul 3.
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.
世界卫生组织（WHO）2030 年路线图旨在消除血吸虫病这一公共卫生问题，目标是减少重度感染。然而，以前的研究主要使用流行率作为血吸虫病的主要指标。我们引入了几种机器学习（ML）算法，使用发病率来预测感染强度类别，目的是评估世卫组织提出的非洲消除血吸虫病的情况。我们从扩大消除被忽视热带病特别计划中获得了发病率和感染强度数据，该计划涵盖了撒哈拉以南非洲的 12 个国家。然后，我们使用一系列 ML 算法，根据发病率和来自遥感来源的几种相关环境和人口统计学协变量，预测曼氏血吸虫和埃及血吸虫感染强度类别的流行率。最优模型具有较高的准确性和稳定性；它在预测曼氏血吸虫重度感染流行率方面的平均绝对误差（MAE）为 0.02，均方根误差（RMSE）为 0.05，决定系数（R2）为 0.84；在预测埃及血吸虫方面，MAE 为 0.02，RMSE 为 0.04，R2 值为 0.81。基于这个最优模型，我们发现调查国家的大多数地区尚未达到 2030 年世卫组织路线图的目标。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表现出较高的总体预测能力，我们的方法为评估 2030 年世卫组织路线图中非洲设定的疾病目标提供了一种低成本、有效的方法。
