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Baseline nowcasting methods for handling delays in epidemiological data.

Kaitlyn E Johnson1, Maria L Tang2, Emily Tyszka3

  • 1Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine Faculty of Epidemiology and Population Health, London, England, NW5 1JG, UK.

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Summary
This summary is machine-generated.

We developed a new nowcasting method and R package to improve real-time disease surveillance by correcting reporting delays. Our approach offers a benchmark for developing and evaluating nowcasting models in public health.

Keywords:
benchmarkinginfectious disease surveillancenowcastingright-truncation

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Informatics

Background:

  • Real-time disease surveillance data is crucial for public health but suffers from reporting delays, causing downward bias in recent estimates.
  • Current nowcasting methods are underutilized due to complexity, poor documentation, and technical barriers, hindering methodological advancements.
  • Standardized benchmarks are lacking for evaluating novel nowcasting methodologies.

Purpose of the Study:

  • To develop a family of nowcasting methods and an R package, `baselinenowcast`, to address underuse and improve disease surveillance.
  • To validate the developed methods against established baselines and compare different specifications for epidemiological challenges.
  • To provide a benchmark for evaluating new nowcasting models and facilitate their adoption in public health.

Main Methods:

  • Developed a family of nowcasting methods and an R package (`baselinenowcast`) based on the German COVID-19 Nowcast Hub baseline.
  • Validated methods against the German COVID-19 Nowcast Hub baseline and compared specifications addressing weekday reporting patterns and cross-strata estimation.
  • Applied the approach to United Kingdom Health Security Agency (UKHSA) norovirus surveillance data, comparing three method specifications against three previously evaluated methods.

Main Results:

  • The developed baseline nowcasting method improved estimates compared to unadjusted data across all case studies.
  • Optimal method specification varied by context, but the default specification demonstrated robust performance across diverse settings.
  • Application to UKHSA norovirus data provided insights into the performance of the current public health model.

Conclusions:

  • The `baselinenowcast` method and software offer a straightforward nowcasting solution for public health practice.
  • The package serves as a valuable benchmark for the development and evaluation of novel nowcasting models.
  • This work facilitates the wider adoption and advancement of nowcasting techniques in disease surveillance.