Bureau of Communicable Disease, New York City Department of Health and Mental Hygiene, Long Island City, NY, United States.
Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States.
JMIR Public Health Surveill. 2021 Jan 15;7(1):e25538. doi: 10.2196/25538.
Nowcasting approaches enhance the utility of reportable disease data for trend monitoring by correcting for delays, but implementation details affect accuracy.
To support real-time COVID-19 situational awareness, the New York City Department of Health and Mental Hygiene used nowcasting to account for testing and reporting delays. We conducted an evaluation to determine which implementation details would yield the most accurate estimated case counts.
A time-correlated Bayesian approach called Nowcasting by Bayesian Smoothing (NobBS) was applied in real time to line lists of reportable disease surveillance data, accounting for the delay from diagnosis to reporting and the shape of the epidemic curve. We retrospectively evaluated nowcasting performance for confirmed case counts among residents diagnosed during the period from March to May 2020, a period when the median reporting delay was 2 days.
Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days when the nowcasts were conducted, with Mondays having the lowest mean absolute error of 183 cases in the context of an average daily weekday case count of 2914.
Nowcasting using NobBS can effectively support COVID-19 trend monitoring. Accounting for overdispersion, shortening the moving window, and suppressing diagnoses on weekends-when fewer patients submitted specimens for testing-improved the accuracy of estimated case counts. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported officials in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.
实时预测方法通过校正延迟来提高报告疾病数据的趋势监测实用性，但实施细节会影响准确性。
为支持实时 COVID-19 态势感知，纽约市卫生局使用实时预测来计算检测和报告延迟，以对报告病例数进行修正。我们进行了一项评估，以确定哪些实施细节可以产生最准确的估计病例数。
实时应用一种称为贝叶斯平滑实时预测（NobBS）的时间相关贝叶斯方法，对报告疾病监测数据的行列表进行实时预测，考虑从诊断到报告的延迟以及流行曲线的形状。我们对 2020 年 3 月至 5 月期间确诊居民的实时预测表现进行了回顾性评估，在此期间报告的中位数延迟为 2 天。
具有 2 周移动窗口和负二项式分布的实时预测的平均绝对误差、相对均方根误差较低，95%预测区间覆盖率较高，而具有 3 周移动窗口或泊松分布的实时预测则较低。由于周末诊断的患者较少，且缺乏工作日调整，因此周末后几天的实时预测优于周末前几天的实时预测。当仅对工作日进行病例数估计时，在进行实时预测的日子中，各项指标相似，在平均每日工作日病例数为 2914 例的情况下，星期一的平均绝对误差最低，为 183 例。
使用 NobBS 的实时预测可以有效地支持 COVID-19 趋势监测。考虑到过离散度、缩短移动窗口以及抑制周末的诊断（此时提交检测的患者较少）可以提高估计病例数的准确性。实时预测确保了对观察到的病例数减少的正确解释，避免将其误解为真实下降，并支持官员预测住院和死亡的规模和时间，并在地理上分配资源。
