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Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore.
Parasites & Vectors ( IF 3.2 ) Pub Date : 2020-01-17 , DOI: 10.1186/s13071-020-3898-5
Sorawat Sangkaew 1 , Li Kiang Tan 2 , Lee Ching Ng 2, 3 , Neil M Ferguson 4 , Ilaria Dorigatti 4
Affiliation  

BACKGROUND Dengue is a mosquito-borne viral disease caused by one of four serotypes (DENV1-4). Infection provides long-term homologous immunity against reinfection with the same serotype. Plaque reduction neutralization test (PRNT) is the gold standard to assess serotype-specific antibody levels. We analysed serotype-specific antibody levels obtained by PRNT in two serological surveys conducted in Singapore in 2009 and 2013 using cluster analysis, a machine learning technique that was used to identify the most common histories of DENV exposure. METHODS We explored the use of five distinct clustering methods (i.e. agglomerative hierarchical, divisive hierarchical, K-means, K-medoids and model-based clustering) with varying number (from 4 to 10) of clusters for each method. Weighted rank aggregation, an evaluating technique for a set of internal validity metrics, was adopted to determine the optimal algorithm, comprising the optimal clustering method and the optimal number of clusters. RESULTS The K-means algorithm with six clusters was selected as the algorithm with the highest weighted rank aggregation. The six clusters were characterised by (i) dominant DENV2 PRNT titres; (ii) co-dominant DENV1 and DENV2 titres with average DENV2 titre > average DENV1 titre; (iii) co-dominant DENV1 and DENV2 titres with average DENV1 titre > average DENV2 titre; (iv) low PRNT titres against DENV1-4; (v) intermediate PRNT titres against DENV1-4; and (vi) dominant DENV1-3 titres. Analyses of the relative size and age-stratification of the clusters by year of sample collection and the application of cluster analysis to the 2009 and 2013 datasets considered separately revealed the epidemic circulation of DENV2 and DENV3 between 2009 and 2013. CONCLUSION Cluster analysis is an unsupervised machine learning technique that can be applied to analyse PRNT antibody titres (without pre-established cut-off thresholds to indicate protection) to explore common patterns of DENV infection and infer the likely history of dengue exposure in a population.

中文翻译:

使用聚类分析从新加坡的横断面血清学研究中重建登革热的暴露方式。

背景技术登革热是由四种血清型(DENV1-4)之一引起的由蚊子传播的病毒性疾病。感染可提供长期的同源免疫力,以抵抗相同血清型的再感染。噬斑减少中和试验(PRNT)是评估血清型特异性抗体水平的金标准。我们在2009年和2013年在新加坡进行的两次血清学调查中,使用聚类分析对PRNT获得的血清型特异性抗体水平进行了分析。聚类分析是一种机器学习技术,用于识别最常见的DENV暴露史。方法我们探索了使用五种不同的聚类方法(即聚类的分层,分裂的分层,K均值,K medoids和基于模型的聚类),每种方法具有不同数量(从4到10)的聚类。加权等级汇总,采用一套内部有效性指标的评价技术确定最优算法,包括最优聚类方法和最优聚类数。结果选择了具有六个聚类的K-means算法作为加权加权秩最高的算法。这六个簇的特征是:(i)DENV2 PRNT的主要效价;(ii)共同主导的DENV1和DENV2滴度,平均DENV2滴度>平均DENV1滴度;(iii)共同主导的DENV1和DENV2滴度,平均DENV1滴度>平均DENV2滴度;(iv)对DENV1-4的PRNT滴度低;(v)PRNT对DENV1-4的中间效价;(vi)DENV1-3的主要效价。
更新日期:2020-01-17
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