Disease persistence on temporal contact networks accounting for heterogeneous infectious periods
Disease persistence on temporal contact networks accounting for heterogeneous infectious periods
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DOI：
10.1098/rsos.181404
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发表时间：
2019-01-01
影响因子：
3.5
通讯作者：
Colizza, Vittoria
中科院分区：
文献类型：
--
作者：
Darbon, Alexandre;Colombi, Davide;Colizza, Vittoria
传染病的传染期是疾病传播和持续的关键因素。网络上的流行病模型通常假设所有个体的平均传染期相同，因此允许分析处理。然而，这种简化的假设往往是不现实的，因为宿主可能具有不同的感染期，例如，由于个体宿主-病原体相互作用或获得治疗的不均匀性。虽然以前的工作占这种异质性的静态网络，一个完整的理论理解的相互作用，不同的传染期和时间演变的接触仍然是失踪。在这里，我们考虑了一个时间网络上的一个不确定的传染病易感的流行病与主机特定的平均传染期，并开发了一个分析框架，以估计流行病的阈值，即疾病传播的临界传染率在主机人口。整合接触数据的传播与爆发数据和流行病学的估计，我们的框架应用于三个现实世界的案例研究，探讨不同的流行背景下，牛结核病在意大利南部的持久性，医院感染的传播，在一所学校的大流行性流感的扩散。我们发现，同质参数化可能会导致重要的偏差，在评估的流行病风险的主机人口。我们的方法还能够确定主要负责疾病传播的宿主群体，这些宿主可能是预防和控制的目标，有助于公共卫生干预。
The infectious period of a transmissible disease is a key factor for disease spread and persistence. Epidemic models on networks typically assume an identical average infectious period for all individuals, thus allowing an analytical treatment. This simplifying assumption is, however, often unrealistic, as hosts may have different infectious periods, due, for instance, to individual host-pathogen interactions or inhomogeneous access to treatment. While previous work accounted for this heterogeneity in static networks, a full theoretical understanding of the interplay of varying infectious periods and time-evolving contacts is still missing. Here, we consider a susceptible-infectious-susceptible epidemic on a temporal network with host-specific average infectious periods, and develop an analytical framework to estimate the epidemic threshold, i.e. the critical transmissibility for disease spread in the host population. Integrating contact data for transmission with outbreak data and epidemiological estimates, we apply our framework to three real-world case studies exploring different epidemic contexts-the persistence of bovine tuberculosis in southern Italy, the spread of nosocomial infections in a hospital, and the diffusion of pandemic influenza in a school. We find that the homogeneous parametrization may cause important biases in the assessment of the epidemic risk of the host population. Our approach is also able to identify groups of hosts mostly responsible for disease diffusion who may be targeted for prevention and control, aiding public health interventions.