Hespanha João P, Chinchilla Raphael, Costa Ramon R, Erdal Murat K, Yang Guosong
University of California, Santa Barbara, USA.
Federal University of Rio de Janeiro, Brazil.
Annu Rev Control. 2021;51:460-476. doi: 10.1016/j.arcontrol.2021.03.008. Epub 2021 Apr 8.
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.
我们通过历史数据（预测）来解决对2019年冠状病毒病（COVID-19）未来一段时间内新增病例数和死亡数的预测问题。我们使用一种基于具有时变参数的随机易感-感染-康复（SIR）模型的基于模型的方法，该模型捕捉了疾病动态响应社会行为、非药物干预和检测率变化的演变。我们表明，在存在无症状病例的情况下，这样的模型包含仅根据新增病例数和死亡数测量无法唯一确定的内部参数和状态，但这并不妨碍为这些测量的未来值构建可靠的预测。可以使用基于非线性优化求解器的迭代算法来计算此类预测及相关的置信区间，而无需进行蒙特卡罗抽样。我们的结果已在一个广泛的COVID-19数据集上得到验证，该数据集涵盖了2020年3月至12月全球144个地区的情况。
