# Baseline nowcasting methods for handling delays in epidemiological data

## TL;DR

This paper introduces a new nowcasting method and software to correct for reporting delays in public health data, improving accuracy and serving as a benchmark for future methods.

## Contribution

The paper introduces a family of baseline nowcasting methods and an R package that are both practical for public health and useful as a benchmark for methodological development.

## Key findings

- The baseline method improved estimates compared to unadjusted data across all case studies.
- The optimal choice of baseline method specification depends on context, but the default specification performed well in various settings.
- Applied to UKHSA norovirus data, the method helped assess the performance of current public health models.

## Abstract

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.

To address these needs, we developed a family of nowcasting methods and an accompanying R package,
baselinenowcast. We validated our method against the baseline method that was used in the German COVID-19 Nowcast Hub and on which our approach was based. Using this data, we conducted an analysis to compare different specifications of our method which were designed to address common issues in epidemiology such as weekday patterns in reporting and the ability to share estimates across different strata. We used our approach on norovirus surveillance data from the United Kingdom Health Security Agency (UKHSA) and compared the performance of three of our method specifications against three methods evaluated in a previous study.

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.

Our method and software can be used both as a straightforward nowcasting method and provides a benchmark for nowcasting model development.

Reporting delays in public health surveillance systems can create a misleading impression of declining trends in recent data. While a number of “nowcasting” methods have been developed to correct for this bias, widespread adoption in public health practice has yet to be realized. Currently, there is no simple method to perform nowcasting that both meets the needs of public health practice and can be used as a benchmark for further methodological advancement. To address these two gaps, we developed a family of baseline nowcasting methods and an accompanying software package. Using data from COVID-19 in Germany and norovirus in England, we evaluated the performance of our default method against other methods used in previous studies and assessed the performance of our different method specifications designed specifically for common problems in epidemiology such as weekday patterns in reporting and sharing estimates across different strata. Our findings indicate that our baseline methods improve performance over unadjusted data or more ad-hoc baseline nowcasting approaches, provide an interpretable and accessible nowcasting solution for public health practice, and are useful as a standard benchmark for enhanced understanding of more advanced methods.