Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.
Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data.
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DOI：
10.1371/journal.pcbi.1001021
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发表时间：
2010-12-02
影响因子：
4.3
通讯作者：
Manfredi P
中科院分区：
文献类型：
--
作者：
Iozzi F;Trusiano F;Chinazzi M;Billari FC;Zagheni E;Merler S;Ajelli M;Del Fava E;Manfredi P
了解社会接触模式仍然是了解直接传播感染传播的最关键步骤。然而，获取有关社交接触模式的数据的成本很高。那么一个主要问题是合成社会的模拟是否有助于可靠地重建这些数据。在本文中，我们通过模拟一个简单的基于个人的模型（IBM）来计算各种合成的特定年龄接触矩阵。该模型基于意大利时间使用数据和常规社会人口统计数据（例如学校和工作场所出勤率、家庭结构等）。该模型被命名为“小意大利”，因为每个人工智能代理都是真人的克隆。换句话说，每个代理人的日常日记都是在意大利时间使用调查中抽样的相应真实个体中观察到的。我们还根据意大利 IBM 用于大流行预测的社会人口统计模型生成了接触矩阵。然后根据最近收集的意大利水痘病毒 (VZV) 和细小病毒 (B19) 血清学数据对这些合成基质进行验证。将它们在拟合血清概况方面的性能与意大利可用的其他矩阵（例如 Polymod 矩阵）进行了比较。合成矩阵显示出与抽样调查估计的相同的定性特征：例如，强烈的分类性以及与父母和孩子之间的接触相关的上对角线和下对角线条纹的存在。经过血清学数据验证后，Little Italy 矩阵对于 VZV 的拟合度比 Polymod 矩阵差，但对于 B19 的拟合度却比并行矩阵好。这是首次将合成接触矩阵与真实接触矩阵进行系统比较，并根据流行病学数据进行验证。结果表明，简单、精心设计的合成矩阵可以为基于问卷的矩阵提供富有成效的补充方法。该论文还支持这样一种观点，即根据感染的传播程度，不同接触者的数量或反复接触可能是传播的关键因素。有关社会接触模式的数据对于为直接传播的传染病（从流感大流行到肺结核，再到儿童疾病的反复流行）制定适当的控制政策至关重要。世界上大多数国家都不会处理此类数据。我们提出了一种通过模拟人工社会来生成合成联系数据的方法，该人工社会将常规可用的社会人口统计数据（例如家庭构成或学校参与数据）与越来越多的可用时间使用数据相结合。然后，我们根据水痘和细小病毒的真实流行病学数据验证随后的模拟接触数据。结果表明，该方法可能是一种非常富有成效的方法，并为密切接触传染病传播的生物学提供了一些见解。
Knowledge of social contact patterns still represents the most critical step for understanding the spread of directly transmitted infections. Data on social contact patterns are, however, expensive to obtain. A major issue is then whether the simulation of synthetic societies might be helpful to reliably reconstruct such data. In this paper, we compute a variety of synthetic age-specific contact matrices through simulation of a simple individual-based model (IBM). The model is informed by Italian Time Use data and routine socio-demographic data (e.g., school and workplace attendance, household structure, etc.). The model is named “Little Italy” because each artificial agent is a clone of a real person. In other words, each agent's daily diary is the one observed in a corresponding real individual sampled in the Italian Time Use Survey. We also generated contact matrices from the socio-demographic model underlying the Italian IBM for pandemic prediction. These synthetic matrices are then validated against recently collected Italian serological data for Varicella (VZV) and ParvoVirus (B19). Their performance in fitting sero-profiles are compared with other matrices available for Italy, such as the Polymod matrix. Synthetic matrices show the same qualitative features of the ones estimated from sample surveys: for example, strong assortativeness and the presence of super- and sub-diagonal stripes related to contacts between parents and children. Once validated against serological data, Little Italy matrices fit worse than the Polymod one for VZV, but better than concurrent matrices for B19. This is the first occasion where synthetic contact matrices are systematically compared with real ones, and validated against epidemiological data. The results suggest that simple, carefully designed, synthetic matrices can provide a fruitful complementary approach to questionnaire-based matrices. The paper also supports the idea that, depending on the transmissibility level of the infection, either the number of different contacts, or repeated exposure, may be the key factor for transmission. Data on social contact patterns are fundamental to design adequate control policies for directly transmissible infectious diseases, ranging from a flu pandemic to tuberculosis, to recurrent epidemics of childhood diseases. Most countries in the world do not dispose of such data. We propose an approach to generate synthetic contact data by simulating an artificial society that integrates routinely available socio-demographic data, such as data on household composition or on school participation, with Time Use data, which are increasingly available. We then validate the ensuing simulated contact data against real epidemiological data for varicella and parvo-virus. The results suggest that the approach is potentially a very fruitful one, and provide some insights on the biology of transmission of close-contact infectious diseases.
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影响因子：
3.7
作者：
Kretzschmar M;Mikolajczyk RT
通讯作者：
Mikolajczyk RT
DOI：
10.1073/pnas.0706849105
发表时间：
2008-03-25
影响因子：
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作者：
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通讯作者：
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DOI：
10.1098/rspb.1997.0131
发表时间：
1997-07-22
影响因子：
4.7
作者：
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通讯作者：
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影响因子：
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作者：
Eilers, PHC;Marx, BD
通讯作者：
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影响因子：
3.8
作者：
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通讯作者：
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