Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

IF 3 3区 医学 Q2 INFECTIOUS DISEASES
Thayer L. Anderson , Anjalika Nande , Carter Merenstein , Brinkley Raynor , Anisha Oommen , Brendan J. Kelly , Michael Z. Levy , Alison L. Hill
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引用次数: 0

Abstract

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. “Superspreading,” or “over-dispersion,” is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

通过家庭研究量化传染性和易感性的个体水平异质性。
与许多其他病原体一样,严重急性呼吸系统综合征冠状病毒2型的传播是由异质性决定的。“过度传播”或“过度分散”是传播的一个重要因素,但很难量化。由于检测到大量病例的可能性增加,接触者追踪数据的估计容易产生潜在的偏差,并且可能比生物异质性更能反映接触行为的变化。相比之下,每个接触者的平均二次感染人数通常是根据家庭调查估计的,这些研究可以通过对一个家庭的所有成员进行检测来最大限度地减少偏差。然而,用于分析家庭传播数据的模型通常假设,所有个体的传染性和易感性都是相同的,或者只是随着年龄等预先确定的特征而变化。在这里,我们开发并应用了一种组合的正向模拟和推理方法,从家庭调查中对感染分布的观察中量化传染性和易感性的个体间变化程度。首先,通过分析模拟数据,我们表明,如果数据来自足够大的家庭样本,我们的方法可以可靠地确定这些异质性的存在、类型和数量。然后,我们分析了一系列来自世界各地不同环境的新冠肺炎家庭研究,发现了个人传染性和易感性存在巨大异质性的有力证据。我们的研究结果还为改进研究设计提供了一个框架,以在个体之间存在现实异质性的情况下评估家庭干预措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Epidemics
Epidemics INFECTIOUS DISEASES-
CiteScore
6.00
自引率
7.90%
发文量
92
审稿时长
140 days
期刊介绍: Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.
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