A review of recent nowcasting methods compensating for vital statistics reporting lags

Abstract

Producing timely, accurate, and granular estimates of public health outcomes is essential for effective public health surveillance and mitigation efforts. However, the production of these estimates is potentially delayed by reporting lags, which many times occur naturally in a data collection cycle. In this article, we review published nowcasting methods (i.e., predicting current events while accounting for reporting lags) with a focus on methods relevant for nowcasting vital statistics. Our aim is to classify the underlying modeling strategies and contexts, identify certain advantages and limitations, and present ideas for further research and development using promising statistical modeling techniques that, to the best of our knowledge, have not yet been utilized in any vital statistics nowcasting approaches.