UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton, QLD, 4343, Australia.
Children Health and Environment Program, Child Health Research Centre, The University of Queensland, South Brisbane, QLD, 4101, Australia.
Parasit Vectors. 2020 Mar 16;13(1):138. doi: 10.1186/s13071-020-04016-2.
Schistosomiasis and infection by soil-transmitted helminths are some of the world's most prevalent neglected tropical diseases. Infection by more than one parasite (co-infection) is common and can contribute to clinical morbidity in children. Geostatistical analyses of parasite infection data are key for developing mass drug administration strategies, yet most methods ignore co-infections when estimating risk. Infection status for multiple parasites can act as a useful proxy for data-poor individual-level or environmental risk factors while avoiding regression dilution bias. Conditional random fields (CRF) is a multivariate graphical network method that opens new doors in parasite risk mapping by (i) predicting co-infections with high accuracy; (ii) isolating associations among parasites; and (iii) quantifying how these associations change across landscapes.
We built a spatial CRF to estimate infection risks for Ascaris lumbricoides, Trichuris trichiura, hookworms (Ancylostoma duodenale and Necator americanus) and Schistosoma mansoni using data from a national survey of Rwandan schoolchildren. We used an ensemble learning approach to generate spatial predictions by simulating from the CRF's posterior distribution with a multivariate boosted regression tree that captured non-linear relationships between predictors and covariance in infection risks. This CRF ensemble was compared against single parasite gradient boosted machines to assess each model's performance and prediction uncertainty.
Parasite co-infections were common, with 19.57% of children infected with at least two parasites. The CRF ensemble achieved higher predictive power than single-parasite models by improving estimates of co-infection prevalence at the individual level and classifying schools into World Health Organization treatment categories with greater accuracy. The CRF uncovered important environmental and demographic predictors of parasite infection probabilities. Yet even after capturing demographic and environmental risk factors, the presences or absences of other parasites were strong predictors of individual-level infection risk. Spatial predictions delineated high-risk regions in need of anthelminthic treatment interventions, including areas with higher than expected co-infection prevalence.
Monitoring studies routinely screen for multiple parasites, yet statistical models generally ignore this multivariate data when assessing risk factors and designing treatment guidelines. Multivariate approaches can be instrumental in the global effort to reduce and eventually eliminate neglected helminth infections in developing countries.
血吸虫病和土壤传播性蠕虫感染是世界上最普遍的被忽视的热带病之一。同时感染一种以上寄生虫(合并感染)很常见,并且会导致儿童临床发病。寄生虫感染数据的地质统计学分析是制定大规模药物治疗策略的关键,但大多数方法在估计风险时忽略了合并感染。多种寄生虫的感染状况可以作为个体水平或环境风险因素数据不足的有用替代指标,同时避免回归稀释偏差。条件随机场 (CRF) 是一种多变量图形网络方法,通过以下方式为寄生虫风险测绘开辟新途径:(i) 准确预测合并感染;(ii) 分离寄生虫之间的关联;(iii) 量化这些关联在景观中的变化。
我们使用来自卢旺达全国学童调查的数据,构建了一个空间 CRF,以估计蛔虫、鞭虫、钩虫(十二指肠钩口线虫和美洲板口线虫)和曼氏血吸虫的感染风险。我们使用集成学习方法,通过从 CRF 的后验分布中进行模拟,利用多元增强回归树生成空间预测,该树捕捉了预测因子之间的非线性关系和感染风险的协方差。该 CRF 集成与单寄生虫梯度提升机进行了比较,以评估每个模型的性能和预测不确定性。
寄生虫合并感染很常见,有 19.57%的儿童感染了至少两种寄生虫。CRF 集成通过提高个体水平合并感染的预测率,并更准确地将学校分类为世界卫生组织的治疗类别,从而实现了比单寄生虫模型更高的预测能力。CRF 揭示了寄生虫感染概率的重要环境和人口统计学预测因子。然而,即使在捕捉了人口统计学和环境风险因素之后,其他寄生虫的存在与否仍然是个体感染风险的有力预测因子。空间预测描绘了需要驱虫治疗干预的高风险区域,包括感染合并率高于预期的区域。
监测研究通常会同时筛查多种寄生虫,但在评估风险因素和制定治疗指南时,统计模型通常会忽略这种多变量数据。在全球减少和最终消除发展中国家被忽视的蠕虫感染的努力中,多变量方法可以起到重要作用。