PDF(Pubmed)
Abstract：
The presence of heterogeneity in susceptibility, differences between hosts in their likelihood of becoming infected, can fundamentally alter disease dynamics and public health responses, for example, by changing the final epidemic size, the duration of an epidemic, and even the vaccination threshold required to achieve herd immunity. Yet, heterogeneity in susceptibility is notoriously difficult to detect and measure, especially early in an epidemic. Here we develop a method that can be used to detect and estimate heterogeneity in susceptibility given contact by using contact tracing data, which are typically collected early in the course of an outbreak. This approach provides the capability, given sufficient data, to estimate and account for the effects of this heterogeneity before they become apparent during an epidemic. It additionally provides the capability to analyze the wealth of contact tracing data available for previous epidemics and estimate heterogeneity in susceptibility for disease systems in which it has never been estimated previously. The premise of our approach is that highly susceptible individuals become infected more often than less susceptible individuals, and so individuals not infected after appearing in contact networks should be less susceptible than average. This change in susceptibility can be detected and quantified when individuals show up in a second contact network after not being infected in the first. To develop our method, we simulated contact tracing data from artificial populations with known levels of heterogeneity in susceptibility according to underlying discrete or continuous distributions of susceptibilities. We analyzed these data to determine the parameter space under which we are able to detect heterogeneity and the accuracy with which we are able to estimate it. We found that our power to detect heterogeneity increases with larger sample sizes, greater heterogeneity, and intermediate fractions of contacts becoming infected in the discrete case or greater fractions of contacts becoming infected in the continuous case. We also found that we are able to reliably estimate heterogeneity and disease dynamics. Ultimately, this means that contact tracing data alone are sufficient to detect and quantify heterogeneity in susceptibility.
摘要：
易感性存在异质性，宿主之间被感染的可能性存在差异，可以从根本上改变疾病动态和公共卫生反应，例如,通过改变最终的流行病规模，流行病的持续时间，甚至达到群体免疫所需的疫苗接种阈值。然而，众所周知，易感性的异质性很难检测和测量，尤其是在流行病的早期。在这里，我们开发了一种方法，可以通过使用接触追踪数据来检测和估计给定接触的敏感性的异质性，这通常是在爆发过程中的早期收集的。这种方法提供了能力，如果有足够的数据，在这种异质性在流行期间变得明显之前，估计和解释它们的影响。它还提供了分析先前流行病可用的大量接触者追踪数据的能力，并估计以前从未估计过的疾病系统易感性的异质性。我们方法的前提是，高度易感的个体比不那么易感的个体更容易被感染，因此，在接触网络中出现后未被感染的个人应该比平均水平更不容易受到影响。当个体在第一接触网络中未被感染后出现在第二接触网络中时,可以检测和量化这种易感性的变化。为了发展我们的方法，我们根据潜在的离散或连续敏感性分布，模拟了具有已知敏感性异质性水平的人工种群的接触追踪数据。我们分析了这些数据，以确定我们能够检测异质性的参数空间以及我们能够估计它的准确性。我们发现，我们检测异质性的能力随着样本量的增加而增加，更大的异质性，以及在离散病例中感染的接触者的中间部分或在连续病例中感染的接触者的更多部分。我们还发现，我们能够可靠地估计异质性和疾病动态。最终,这意味着仅接触者追踪数据就足以检测和量化易感性的异质性.
