Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty? Without this correction, raw data will often mislead by suggesting an improving situation. We present a flexible approach using a latent Gaussian process that is capable of describing the changing auto-correlation structure present in the reporting time-delay surface. This approach also yields robust estimates of uncertainty for the estimated nowcasted numbers of deaths. We test assumptions in model specification such as the choice of kernel or hyper priors, and evaluate model performance on a challenging real dataset from Brazil. Our experiments show that Gaussian process nowcasting performs favourably against both comparable methods, and against a small sample of expert human predictions. Our approach has substantial practical utility in disease modelling -- by applying our approach to COVID-19 mortality data from Brazil, where reporting delays are large, we can make informative predictions on important epidemiological quantities such as the current effective reproduction number.
翻译:由于测量过程的拖延而更新信号的观测结果,是信号处理中常见的一个常见问题,在一系列广泛的领域都有突出的例子。这个问题的一个重要例子是现在预测COVID-19死亡率:鉴于报告的每日死亡人数的一连串,我们能否纠正在报告方面的延误,以便准确描绘目前存在的不确定情况?如果没有这种纠正,原始数据往往会通过暗示情况改善而误导。我们提出了一个灵活的方法,利用潜潜伏的高斯进程,能够描述报告时间延误表面中存在的变化中的自动加速结构。这个方法还能够对现在预测的死亡人数产生可靠的不确定性估计。我们测试模型规格的假设,例如选择内核或超前核,并评估巴西具有挑战性的真实数据集的模型性能。我们的实验显示,高斯进程现在对可比较的方法和少量的专家人类预测都表现良好。我们的方法在疾病建模方面有着巨大的实际效用,我们的方法是用到巴西的COVID-19死亡率数据,因为巴西报告延误是巨大的,我们可以对目前的重要复制数量作出信息性预测。