A mechanistic model of infection: why duration and intensity of contacts should be included in models of disease spread.
A mechanistic model of infection: why duration and intensity of contacts should be included in models of disease spread.
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DOI：
10.1186/1742-4682-6-25
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发表时间：
2009-11-17
影响因子：
--
通讯作者：
Smieszek T
中科院分区：
文献类型：
--
作者：
Smieszek T
疾病传播的数学模型和模拟通常假设每次接触传播的概率是恒定的。这一假设忽略了传播概率的异质性，例如，由于潜在传染性接触的强度和持续时间的变化。忽略这种异质性可能会导致模拟结果得出错误的结论。在这篇文章中，我们展示了疾病传播的机制模型与通常使用的每次接触传播概率恒定的假设的不同之处。我们提出了一个基于暴露的疾病传播机制模型，该模型反映了接触持续时间和强度的异质性。基于经验接触数据，我们计算了在传播率为恒定的经典假设下，(I)机制模型和(Ii)传播率恒定的传播者所诱发的预期继发病例数。对于不同的基本再现数R0，对两种方法的结果进行了比较。该机制模型的结果与假设每次接触传播概率恒定的结果有很大不同。特别是，当使用机械模型而不是通常的假设时，具有许多不同接触的病例的预期继发病例数量要低得多。这是因为在联系人数据集中，长时间密集联系人的比例随着联系人总数的增加而减少。如果假设每个接触者的传播概率不变，那么高度联系的个人，即所谓的超级传播者，对疾病传播的重要性似乎被高估了。这一点尤其适用于基本繁殖数较低的疾病。疾病传播的模拟应该根据持续时间和强度来衡量接触者的权重。
Mathematical models and simulations of disease spread often assume a constant per-contact transmission probability. This assumption ignores the heterogeneity in transmission probabilities, e.g. due to the varying intensity and duration of potentially contagious contacts. Ignoring such heterogeneities might lead to erroneous conclusions from simulation results. In this paper, we show how a mechanistic model of disease transmission differs from this commonly used assumption of a constant per-contact transmission probability. We present an exposure-based, mechanistic model of disease transmission that reflects heterogeneities in contact duration and intensity. Based on empirical contact data, we calculate the expected number of secondary cases induced by an infector (i) for the mechanistic model and (ii) under the classical assumption of a constant per-contact transmission probability. The results of both approaches are compared for different basic reproduction numbers R0. The outcomes of the mechanistic model differ significantly from those of the assumption of a constant per-contact transmission probability. In particular, cases with many different contacts have much lower expected numbers of secondary cases when using the mechanistic model instead of the common assumption. This is due to the fact that the proportion of long, intensive contacts decreases in the contact dataset with an increasing total number of contacts. The importance of highly connected individuals, so-called super-spreaders, for disease spread seems to be overestimated when a constant per-contact transmission probability is assumed. This holds particularly for diseases with low basic reproduction numbers. Simulations of disease spread should weight contacts by duration and intensity.
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