We address the prediction of the number of new cases and deaths for the coronavirus disease 2019 (COVID-19) over a future horizon from historical data (forecasting). We use a model-based approach based on a stochastic Susceptible–Infections–Removed (SIR) model with time-varying parameters, which captures the evolution of the disease dynamics in response to changes in social behavior, non-pharmaceutical interventions, and testing rates. We show that, in the presence of asymptomatic cases, such model includes internal parameters and states that cannot be uniquely identified solely on the basis of measurements of new cases and deaths, but this does not preclude the construction of reliable forecasts for future values of these measurements. Such forecasts and associated confidence intervals can be computed using an iterative algorithm based on nonlinear optimization solvers, without the need for Monte Carlo sampling. Our results have been validated on an extensive COVID-19 dataset covering the period from March through December 2020 on 144 regions around the globe.

我们根据历史数据（预测）预测未来范围内 2019 年冠状病毒病 (COVID-19) 的新病例和死亡人数。我们使用基于模型的方法，该方法基于具有时变参数的随机易感感染去除 (SIR) 模型，该模型捕获疾病动态的演变以响应社会行为、非药物干预和检测率的变化. 我们表明，在存在无症状病例的情况下，这种模型包括内部参数和状态，这些参数和状态不能仅根据新病例和死亡的测量来唯一识别，但这并不排除对这些未来值的可靠预测的构建测量。可以使用基于非线性优化求解器的迭代算法计算此类预测和相关置信区间，而无需蒙特卡洛采样。我们的结果已在涵盖全球 144 个地区的 2020 年 3 月至 2020 年 12 月期间的广泛 COVID-19 数据集上得到验证。