Baseline nowcasting methods for handling delays in epidemiological data
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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. We propose an empirically motivated family of nowcasting methods and accompanying R package, baselinenowcast , which addresses these dual needs. We validated our default method specification against the baseline method that was used in the German COVID-19 Nowcast Hub and on which our approach was based. Using these data, we conducted an analysis to compare different ways of configuring the method and evaluated performance against our method’s default specification. We then used our approach on norovirus surveillance data from the United Kingdom Health Security Agency (UKHSA) and compared performance 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 performed well in a range of settings. Applied to UKHSA norovirus data, our method helped us understand performance differences for 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.
Author Summary
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. In this paper, we present a family of baseline nowcasting methods and an accompanying software package designed specifically to address these two gaps. We evaluate the performance of the default method against other methods used in previous studies and assess the performance of different method specifications. 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 more advanced methods.
