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Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics.

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Estimating the fraction of reported cases during an epidemic is challenging. A new statistical method uses transmission chain data to provide fast, accurate real-time reporting estimates for outbreak surveillance.

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Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Statistical Modeling

Background:

  • Accurate estimation of reported cases ('reporting') is crucial for epidemic modeling and outbreak response.
  • Estimating reporting is difficult because it represents unobserved epidemic data.
  • Previous methods for reporting estimation are often complex or not suitable for real-time application.

Purpose of the Study:

  • To introduce and evaluate a simple statistical method for estimating epidemic reporting using transmission chain data.
  • To assess the performance of this method across various outbreak sizes and reporting levels.
  • To provide a tool for real-time reporting estimation in epidemic settings.

Main Methods:

  • Developed a method using the proportion of investigated cases with a known, reported infector as a proxy for reporting.
  • Applied the method to simulated epidemics with varying outbreak sizes and reporting levels.
  • Evaluated the method's bias, precision, and accuracy compared to true reporting values.

Main Results:

  • The method demonstrated low bias and reasonable precision in estimating reporting.
  • Estimates were typically within 5-10% of the true value, even with sub-optimal data coverage.
  • The approach proved effective across different simulated outbreak scenarios.

Conclusions:

  • The proposed statistical method offers a simple and fast approach for real-time reporting estimation.
  • This method is particularly useful in epidemics driven by person-to-person transmission with routine case investigation.
  • The approach can enhance epidemic surveillance and response by providing timely insights into reporting completeness.