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<p>The effective reproduction number, <i>R</i>, is a predominant statistic for tracking infectious disease spread and informing health policies. An estimated <i>R</i> = <i>1</i> is universally interpreted as a stability threshold distinguishing epidemic growth (<i>R</i> &gt; <i>1</i>) from control (<i>R</i> &lt; <i>1</i>). We demonstrate that this interpretation frequently fails because <i>R</i> typically averages over groups with heterogeneous characteristics. We find that <i>R</i> = <i>1</i> conceals valuable early-warning signals of resurgence and misclassifies complex dynamics as noise, generating false positive stability thresholds that diminish predictive and policymaking value. We further illustrate that a popular alternative transmissibility definition (using next-generation matrices) overcorrects this issue, producing false negative stability signals by amplifying stochastic variation. We address these limitations by adapting a recently developed statistic, <i>E</i>, derived from <i>R</i> using experimental design theory. We show that <i>E</i> tightly constrains the set of scenarios consistent with stability, while remaining robust to noise and establish <i>E</i> = <i>1</i> as a more practical and meaningful real-time threshold.</p>

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The R = 1 threshold can misclassify epidemic stability

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Kris V. Parag,
  • Mauricio Santillana,
  • Anne Cori,
  • Uri Obolski

摘要

The effective reproduction number, R, is a predominant statistic for tracking infectious disease spread and informing health policies. An estimated R = 1 is universally interpreted as a stability threshold distinguishing epidemic growth (R > 1) from control (R < 1). We demonstrate that this interpretation frequently fails because R typically averages over groups with heterogeneous characteristics. We find that R = 1 conceals valuable early-warning signals of resurgence and misclassifies complex dynamics as noise, generating false positive stability thresholds that diminish predictive and policymaking value. We further illustrate that a popular alternative transmissibility definition (using next-generation matrices) overcorrects this issue, producing false negative stability signals by amplifying stochastic variation. We address these limitations by adapting a recently developed statistic, E, derived from R using experimental design theory. We show that E tightly constrains the set of scenarios consistent with stability, while remaining robust to noise and establish E = 1 as a more practical and meaningful real-time threshold.