The uncertainty in effective reproduction number estimation from heterogeneous case reporting

The effective reproduction number ( R t ) is a pivotal metric for tracking epidemic dynamics and informing public health interventions. However, its estimation is often compromised by heterogeneous case reporting, where factors such as limited testing capacity and surveillance delays cause reported cases to systematically deviate from true infection incidence. This study quantifies the uncertainty in R t estimation introduced by such heterogeneity across varying detection scenarios. We demonstrate that compared to the benchmark R t values from actual infections, estimates derived from reported data are: biased downward during the ascending phase of an outbreak; biased upward during the descending phase; and unbiased during stable periods. Our findings provide a framework for assessing the reliability of R t estimates and underscore the need for robust reporting systems.

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Publication Details

Journal
Journal of Safety Science and Resilience
Published
2026-01-01
DOI
https://doi.org/10.1016/j.jnlssr.2026.100289
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
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article

The uncertainty in effective reproduction number estimation from heterogeneous case reporting

Shunjiang Ni, Shuting Yu, Luyang Wang
Journal of Safety Science and Resilience
COVID-19 epidemiological studies
article

The uncertainty in effective reproduction number estimation from heterogeneous case reporting

Shunjiang Ni, Shuting Yu, Luyang Wang
article en

Abstract

The effective reproduction number ( R t ) is a pivotal metric for tracking epidemic dynamics and informing public health interventions. However, its estimation is often compromised by heterogeneous case reporting, where factors such as limited testing capacity and surveillance delays cause reported cases to systematically deviate from true infection incidence. This study quantifies the uncertainty in R t estimation introduced by such heterogeneity across varying detection scenarios. We demonstrate that compared to the benchmark R t values from actual infections, estimates derived from reported data are: biased downward during the ascending phase of an outbreak; biased upward during the descending phase; and unbiased during stable periods. Our findings provide a framework for assessing the reliability of R t estimates and underscore the need for robust reporting systems.

Journal of Safety Science and Resilience
Tsinghua University (CN)
Good health and well-being
Openalex Percentile: Top 92%
COVID-19 epidemiological studies
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