Baseline nowcasting methods for handling delays in epidemiological data.

Background: Up-to-date real-time disease surveillance data can provide critical public health insights, however reporting delays can create downward bias in the latest data. Nowcasting methods designed to correct for this bias remain underused in public health practice due to their complexity, lack of tailored documentation, or technical barriers. Methodological advances in nowcasting are also hampered by the absence of standardised benchmarks for evaluating new methods. Methods: To address these needs, we developed a family of nowcasting methods and an accompanying R package, Results: Our baseline method improved estimates compared to unadjusted data across all case studies. We found that the optimal choice of baseline method specification depends on context but that our default method specification performed well in a range of settings. Applied to UKHSA norovirus data, our method helped us understand the performance of the model currently used in public health practice. Conclusions: Our method and software can be used both as a straightforward nowcasting method and provides a benchmark for nowcasting model development.