Evaluation of Nowcasting for Real-Time COVID-19 Tracking - New York City, March-May 2020.

ABSTRACT: To account for delays between specimen collection and report, the New York City Department of Health and Mental Hygiene used a time-correlated Bayesian nowcasting approach to support real-time COVID-19 situational awareness. We retrospectively evaluated nowcasting performance for case counts among residents diagnosed during March-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

SUBMITTER: Greene SK 

PROVIDER: S-EPMC7587834 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature