Analysing pneumococcal invasiveness using Bayesian models of pathogen progression rates.
Analysing pneumococcal invasiveness using Bayesian models of pathogen progression rates.
复制标题
使用病原体进展率的贝叶斯模型来分析肺炎球菌的侵袭性。
DOI：
10.1371/journal.pcbi.1009389
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发表时间：
2022-03
影响因子：
4.3
通讯作者：
Croucher NJ
中科院分区：
文献类型：
--
作者：
Løchen A;Truscott JE;Croucher NJ
可归因于机会性病原体的疾病负担取决于它们在无症状殖民中的盛行率以及它们进展到引起症状性疾病的速度。由共生菌引起的感染增加可能是“高侵袭性”菌株出现的结果。这种病原体可以通过使用来自疾病病例和健康携带者的匹配的分型微生物样本来量化进展率来识别。这项研究描述了用于分析这些数据集的贝叶斯模型，该模型在RStan包(https://github.com/nickjcroucher/progressionEstimation).中实现这些模型在稳定的拟合上收敛，准确地再现了肺炎链球菌数据集的荟萃分析的观察结果。每年每个携带者病例的侵袭性估计，即从携带者到侵袭性疾病的进展率，与样本量大时的优势比荟萃分析的无量纲值有很强的相关性。在样本量较小的情况下，贝叶斯模型得出的估计值更具信息量。这确定了历史上罕见但高风险的肺炎链球菌血清型，这些血清型在疫苗相关的细菌种群破坏后可能会有问题。该方案允许通过与贝叶斯因素的模型比较进行假设检验。应用于可获得肺炎链球菌菌株和血清型信息的数据集，发现菌株内和血清型内差异在侵袭性中的重要证据。因此，这些基因型的不同地理分布很可能导致不同地点接种疫苗的影响存在差异。因此，对机会性病原体的基因组监测对于量化公共卫生干预措施的有效性至关重要，并使正在进行的荟萃分析能够识别新的、高度侵入性的变种。机会性病原体是通常由健康宿主携带的微生物，但偶尔会导致严重疾病。进展率量化了这种病原体从无害的共生转变为引起症状性感染的风险。随着“高侵袭性”菌株的出现，由机会性病原体引起的感染的发生率可能会上升，这种菌株的进展率很高。因此，利用监测数据计算不同病原体菌株进展率的方法对于快速识别新出现的传染病威胁至关重要。现有的方法通常测量相对于种群中微生物总体组合的进展率，但这些种群可能因地点和时间而有很大差异，这使得在不同研究之间结合输出具有挑战性。这项工作提出了一种新的方法，可以从监测数据中估计进展率，该数据可以生成对病原体种群建模有用的值，即使是从相对较小的样本量。
The disease burden attributable to opportunistic pathogens depends on their prevalence in asymptomatic colonisation and the rate at which they progress to cause symptomatic disease. Increases in infections caused by commensals can result from the emergence of “hyperinvasive” strains. Such pathogens can be identified through quantifying progression rates using matched samples of typed microbes from disease cases and healthy carriers. This study describes Bayesian models for analysing such datasets, implemented in an RStan package (https://github.com/nickjcroucher/progressionEstimation). The models converged on stable fits that accurately reproduced observations from meta-analyses of Streptococcus pneumoniae datasets. The estimates of invasiveness, the progression rate from carriage to invasive disease, in cases per carrier per year correlated strongly with the dimensionless values from meta-analysis of odds ratios when sample sizes were large. At smaller sample sizes, the Bayesian models produced more informative estimates. This identified historically rare but high-risk S. pneumoniae serotypes that could be problematic following vaccine-associated disruption of the bacterial population. The package allows for hypothesis testing through model comparisons with Bayes factors. Application to datasets in which strain and serotype information were available for S. pneumoniae found significant evidence for within-strain and within-serotype variation in invasiveness. The heterogeneous geographical distribution of these genotypes is therefore likely to contribute to differences in the impact of vaccination in between locations. Hence genomic surveillance of opportunistic pathogens is crucial for quantifying the effectiveness of public health interventions, and enabling ongoing meta-analyses that can identify new, highly invasive variants. Opportunistic pathogens are microbes that are commonly carried by healthy hosts, but can occasionally cause severe disease. The progression rate quantifies the risk of such a pathogen transitioning from a harmless commensal to causing a symptomatic infection. The incidence of infections caused by opportunistic pathogens can rise with the emergence of “hyperinvasive” strains, which have high progression rates. Therefore methods for calculating progression rates of different pathogen strains using surveillance data are crucial for rapidly identifying emerging infectious disease threats. Existing methods typically measure progression rates relative to the overall mix of microbes in the population, but these populations can vary substantially between locations and times, making the outputs challenging to combine across studies. This work presents a new method for estimating progression rates from surveillance data that generates values useful for modelling pathogen populations, even from relatively small sample sizes.
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影响因子：
4.5
作者：
Croucher NJ;Kagedan L;Thompson CM;Parkhill J;Bentley SD;Finkelstein JA;Lipsitch M;Hanage WP
通讯作者：
Hanage WP
影响因子：
5.5
作者：
Devine, Vanessa T.;Cleary, David W.;Clarke, Stuart C.
通讯作者：
Clarke, Stuart C.
影响因子：
15.8
作者：
Feikin, Daniel R.;Kagucia, Eunice W.;Moore, Matthew R.
通讯作者：
Moore, Matthew R.
影响因子：
11.8
作者：
Cremers, Amelieke J. H.;Mobegi, Fredrick M.;de Jonge, Marien I.
通讯作者：
de Jonge, Marien I.
影响因子：
24.3
作者：
Browall, Sarah;Backhaus, Erik;Henriques-Normark, Birgitta
通讯作者：
Henriques-Normark, Birgitta