COVID-19 remains a challenging global threat with ongoing waves of infections and clinical disease which have resulted millions of deaths and an enormous strain on health systems worldwide. Effective vaccines have been developed for the SARS-CoV-2 virus and administered to billions of people; however, the virus continues to circulate and evolve into new variants for which vaccines may ultimately be less effective. Non-pharmaceutical interventions, such as social distancing, wearing face coverings, and contact tracing, remain important tools, especially at the onset of an outbreak. In this paper, we assess the effectiveness of contact tracing using a non-Markovian, network-based mathematical model. To improve the reliability of the novel model, empirically determined distributions were incorporated for the transition time of model state pairs, such as from exposed to infected states. The first-order closure approximation was used to derive an expression for the epidemic threshold, which is dependent on the number of close contacts. Using survey contact data collected during the 2020 fall academic semester from a university population, we determined that even four to five contacts are sufficient to maintain viral transmission. Additionally, our model reveals that contact tracing can be an effective outbreak mitigation measure by reducing the epidemic size by more than three-fold. Increasing the reliability of epidemic models is critical for accurate public health planning and use as decision support tools. Moving toward more accurate non-Markovian models built upon empirical data is important.
翻译:COVID-19仍然是具有挑战性的全球性威胁,目前传染病和临床疾病浪潮不断造成数百万人死亡,对全世界卫生系统造成巨大压力。已经为SARS-COV-2病毒开发了有效的疫苗,并向数十亿人进行了治疗;然而,病毒继续传播并演变为新的变种,疫苗最终可能不太有效。非药物性干预措施,如社会混乱、戴面罩和追踪接触等,仍然是重要的工具,特别是在爆发爆发之初。在本文中,我们利用非马尔科维安网络的数学模型评估接触追踪的有效性。为了提高新型模型的可靠性,根据经验确定的分布被纳入到模范州配对的过渡期,例如从接触到受感染的州。第一级封闭近似用于表达流行病的临界值,这取决于密切接触的次数。使用在2020年秋季从大学人口开始的学期收集的调查接触数据,我们确定甚至4至5个接触点足以维持病毒传播。此外,我们的模型表明,为降低流行病规模而采用不精确的统计模型可以成为降低流行病流行程度的重要的可靠数据。