论文标题
用于分析SARS-COV2传输动力学的建模框架
A modelling framework for the analysis of the SARS-CoV2 transmission dynamics
论文作者
论文摘要
尽管医疗数据收集的进展取得了进展，但由于病例不足，SARS-COV-2的实际负担仍未知。这在大流行的急性阶段显而易见，报告的死亡使用被指出是更可靠的信息来源，可能不太容易发生报告。由于每日死亡是由于其死亡可能性加权的过去感染而发生的，因此可以使用报告的死亡数据来推断其年龄分布的感染总数。我们采用该框架，并假设生成感染总数的动力学可以通过通过非线性普通微分方程系统表达的连续时间传输模型来描述，在这种模型中，传输速率被建模为扩散过程，允许揭示控制策略的效果和个人行为的变化。我们在Stan中开发了这种灵活的贝叶斯工具，并研究了3对欧洲国家，估计了随时间变化的繁殖数量（$ R_T $）以及受感染的人的真正累积数量。随着我们估计感染的真实数量，我们提供了$ r_t $的更准确估计。我们还提供了每日报告比率的估计，并讨论了移动性和测试变化对推断数量的影响。
Despite the progress in medical data collection the actual burden of SARS-CoV-2 remains unknown due to under-ascertainment of cases. This was apparent in the acute phase of the pandemic and the use of reported deaths has been pointed out as a more reliable source of information, likely less prone to under-reporting. Since daily deaths occur from past infections weighted by their probability of death, one may infer the total number of infections accounting for their age distribution, using the data on reported deaths. We adopt this framework and assume that the dynamics generating the total number of infections can be described by a continuous time transmission model expressed through a system of non-linear ordinary differential equations where the transmission rate is modelled as a diffusion process allowing to reveal both the effect of control strategies and the changes in individuals behavior. We develop this flexible Bayesian tool in Stan and study 3 pairs of European countries, estimating the time-varying reproduction number($R_t$) as well as the true cumulative number of infected individuals. As we estimate the true number of infections we offer a more accurate estimate of $R_t$. We also provide an estimate of the daily reporting ratio and discuss the effects of changes in mobility and testing on the inferred quantities.
