论文标题
具有精细时间分辨率的流行病学动力学
Epidemiological dynamics with fine temporal resolution
论文作者
论文摘要
为了更好地预测Covid-19流行病的传播动力学，不仅要研究局部和长期传染性接触的网络，而且还要了解传染性和可检测症状的时间动态。在这里，我们提出了一种传播的感染模型，这通常与大规模流行病学网络中的节点相对应。该模型使用延迟方程，考虑了感染的持续时间，并基于症状的病毒载荷，病毒脱落，严重性和可检测性的实验衍生的时间课程。我们表明，由于感染性的早期发作，据报道，这种传染性是同步的，甚至是在可检测症状的发作之前，对与被发现的受感染者接触的每个人的追踪和立即测试减少了流行病，医院负荷和致命率的传播。我们希望这种更精确的节点动态可以纳入复杂的大规模流行病学模型，以提高预测的准确性和信誉。
To better predict the dynamics of spread of COVID-19 epidemics, 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 is based on experimentally-derived time courses of viral load, virus shedding, severity and detectability of symptoms. We show that because of 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 everyone who came in contact with the detected infected individual reduces the spread of epidemics, hospital load, and fatality rate. We hope that this more precise node dynamics could be incorporated into complex large-scale epidemiological models to improve the accuracy and credibility of predictions.
