Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease variants, and the introduction of mitigation policies. Estimating such changes in transmission rates can help us better model and predict the dynamics of an epidemic, and provide insight into the efficacy of control and intervention strategies. We present a method for likelihood-based estimation of parameters in the stochastic SIR model under a time-inhomogeneous transmission rate comprised of piecewise constant components. In doing so, our method simultaneously learns change points in the transmission rate via a Markov chain Monte Carlo algorithm. The method targets the exact model posterior in a difficult missing data setting given only partially observed case counts over time. We validate performance on simulated data before applying our approach to data from an Ebola outbreak in Western Africa and COVID-19 outbreak on a university campus.
翻译:在整个流行病过程中,疾病传播速度随行为变化、新疾病变异的出现以及缓解政策的引入而不同。估计传播率的这种变化有助于我们更好地模拟和预测流行病的动态,并深入了解控制和干预战略的效力。我们提出了一个方法,根据由片段不变的不变成份组成的时间-不均匀传播率对随机SIR模型参数进行基于可能性的估计。在这样做的过程中,我们的方法同时通过Markov链条Monte Carlo算法学习传播率的变化点。该方法针对在一段时间里仅进行部分观察的病例计数的艰难缺失数据设置中精确的模型海报。我们验证了模拟数据在运用我们的方法处理西非埃博拉爆发和大学校园COVID-19爆发的数据之前的性能。