Leung Tiffany, Ferrari Matthew
Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, Pennsylvania, United States of America.
PLoS One. 2025 Aug 22;20(8):e0330568. doi: 10.1371/journal.pone.0330568. eCollection 2025.
The time-series Susceptible-Infectious-Recovered (TSIR) model has been a standard tool for studying the non-linear dynamics of acute, immunizing infectious diseases. The standard assumption of the TSIR model, that vaccination is equivalent to a reduction in the recruitment of susceptible individuals, or the birth rate, can lead to a bias in the estimate of the reporting fraction and of the total incidence. We show that this bias increases with the level of vaccination due to a double counting of individuals who are infected prior to the age of vaccination. We present a simple correction for this bias by discounting the observed number of cases by the product of the number that occur prior to the average age of vaccination and the vaccination coverage during the initial susceptible reconstruction step of the TSIR model fitting. We generate a time series of measles cases using an age-structured SIR transmission model with vaccination after birth (at 9 months of age) and illustrate the bias with the standard TSIR fitting method. We then illustrate that our proposed correction eliminates the bias in the estimated reporting fraction and total incidence. We note further that this bias does not impact the estimates of the seasonality of transmission.
时间序列易感-感染-康复(TSIR)模型一直是研究急性免疫性传染病非线性动力学的标准工具。TSIR模型的标准假设是,疫苗接种等同于易感个体招募量或出生率的降低,这可能导致报告率和总发病率估计值出现偏差。我们表明,由于对接种疫苗年龄之前感染的个体进行了重复计算,这种偏差会随着疫苗接种水平的提高而增加。我们通过在TSIR模型拟合的初始易感重建步骤中,用接种疫苗平均年龄之前发生的病例数与疫苗接种覆盖率的乘积对观察到的病例数进行折扣,提出了一种针对这种偏差的简单校正方法。我们使用出生后(9个月大时)接种疫苗的年龄结构SIR传播模型生成麻疹病例的时间序列,并用标准TSIR拟合方法说明了偏差情况。然后我们说明,我们提出的校正方法消除了估计报告率和总发病率中的偏差。我们进一步指出,这种偏差不会影响传播季节性的估计。