基于临床获得的传染性与潜伏期时间过程的流行病学动力学
Epidemiological dynamics with clinically-derived infectiousness and incubation time courses
AI总结：
提出融合临床病毒载量、排毒及症状可检测时间过程的延迟方程节点模型，证明接触者追踪与即时检测及严格及时隔离可降低传播、医疗负荷与病死率。
摘要：
为了更好地预测诸如 COVID-19 等流行病的动力学，不仅需要研究本地和长距离传染性接触网络，还必须理解传染性与可检测症状的时间动态。在此，我们提出了一个在充分混合人群中的感染传播模型，该人群通常对应于大规模流行病学网络中的一个节点。该模型采用延迟方程，考虑了感染持续时间，并基于实验获得的病毒载量和排毒时间过程，以及症状的可检测性。我们表明，由于据报道传染性的早期发作与可检测症状的发作同步甚至更早，因此对与已检测感染者有过接触的所有人进行追踪和立即检测，可减少流行病传播、医院负荷和病死率。我们还研究了感染者隔离的严格性和及时性如何影响流行病结果。我们希望这些更精确的节点动力学能够被纳入复杂的大规模流行病学模型，以提高预测的准确性和可信度。
英文摘要：
To better predict the dynamics of epidemics such as COVID-19, it is important not only to investigate the network of local and long-range contagious contacts but also to understand the temporal dynamics of infectiousness and detectable symptoms. Here, we present a model of infection spread in a well-mixed group of individuals, which usually corresponds to a node in large-scale epidemiological networks. The model uses delay equations that take into account the duration of infection and are based on experimentally derived time courses of viral load and shedding, as well as the detectability of symptoms. We show that due to an early onset of infectiousness, which is reported to be synchronous or even precede the onset of detectable symptoms, the tracing and immediate testing of all who came in contact with the detected infected individual reduce the spread of epidemics, hospital load, and fatality rate. We also investigate how the strictness and promptness of the isolation of infected individuals affect the outcome of epidemics. We hope that these more precise node dynamics could be incorporated into complex large-scale epidemiological models to improve the accuracy and credibility of predictions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
