Darbon Alexandre, Colombi Davide, Valdano Eugenio, Savini Lara, Giovannini Armando, Colizza Vittoria
INSERM, Sorbonne Université, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), 75012 Paris, France.
Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona 43007, Spain.
R Soc Open Sci. 2019 Jan 16;6(1):181404. doi: 10.1098/rsos.181404. eCollection 2019 Jan.
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.
传染病的传染期是疾病传播和持续存在的关键因素。网络上的流行病模型通常假定所有个体的平均传染期相同，从而便于进行分析处理。然而，这种简化假设往往不现实，因为宿主的传染期可能不同，例如由于个体宿主与病原体的相互作用或获得治疗的机会不均一。虽然先前的研究考虑了静态网络中的这种异质性，但对于不同传染期与随时间变化的接触之间相互作用的全面理论理解仍然缺失。在此，我们考虑在具有宿主特异性平均传染期的时间网络上的易感-感染-易感流行病，并开发一个分析框架来估计流行阈值，即疾病在宿主群体中传播的临界传播率。将传播的接触数据与疫情数据及流行病学估计相结合，我们将我们的框架应用于三个真实案例研究，探索不同的流行情况——意大利南部牛结核病的持续存在、医院内医院感染的传播以及学校中甲型H1N1流感的扩散。我们发现，均匀参数化可能在评估宿主群体的流行风险时导致重要偏差。我们的方法还能够识别对疾病传播主要负责的宿主群体，这些群体可作为预防和控制的目标，有助于公共卫生干预。
