We introduce a Bayesian sequential data assimilation method for COVID-19 forecasting. It is assumed that suitable transmission, epidemic and observation models are available and previously validated and the transmission and epidemic models are coded into a dynamical system. The observation model depends on the dynamical system state variables and parameters, and is cast as a likelihood function. We elicit prior distributions of the effective population size, the dynamical system initial conditions and infectious contact rate, and use Markov Chain Monte Carlo sampling to make inference and prediction of quantities of interest (QoI) at the onset of the epidemic outbreak. The forecast is sequentially updated over a sliding window of epidemic records as new data becomes available. Prior distributions for the state variables at the new forecasting time are assembled using the dynamical system, calibrated for the previous forecast. Moreover, changes in the contact rate and effective population size are naturally introduced through auto-regressive models on the corresponding parameters. We show our forecasting method's performance using a SEIR type model and COVID-19 data from several Mexican localities.

翻译：我们为COVID-19的预测采用了巴耶斯相继数据同化方法,假定有适当的传播、流行和观察模型,并事先得到验证,传播和流行模式编码成动态系统,观察模型取决于动态系统状态变量和参数,并作为一种可能的功能。我们事先收集有效人口规模、动态系统初始条件和传染接触率的分布情况,并利用Markov 链子蒙特卡洛取样来推断和预测流行病爆发时的感兴趣数量(QoI)。随着新的数据的出现,预报会通过一个移动的流行病记录窗口相继更新。以前在新预测时使用动态系统对状态变量的分布进行整理,并按以前的预测加以校准。此外,接触率和有效人口规模的变化是通过相应参数的自动反向模型自然引入的。我们用SEIR型模型和几个墨西哥地方的COVID-19数据来显示我们的预测方法的性能。