Department of Biomedical Engineering, University of Connecticut Health, Farmington, CT, USA.
Center for Cancer Systems Biology @ Yale, West Haven, CT, USA.
BMC Med Res Methodol. 2021 Jan 7;21(1):11. doi: 10.1186/s12874-020-01196-4.
The disease burden of SARS-CoV-2 as measured by tests from various localities, and at different time points present varying estimates of infection and fatality rates. Models based on these acquired data may suffer from systematic errors and large estimation variances due to the biases associated with testing. An unbiased randomized testing to estimate the true fatality rate is still missing.
Here, we characterize the effect of incidental sampling bias in the estimation of epidemic dynamics. Towards this, we explicitly modeled for sampling bias in an augmented compartment model to predict epidemic dynamics. We further calculate the bias from differences in disease prediction from biased, and randomized sampling, proposing a strategy to obtain unbiased estimates.
Our simulations demonstrate that sampling biases in favor of patients with higher disease manifestation could significantly affect direct estimates of infection and fatality rates calculated from the numbers of confirmed cases and deaths, and serological testing can partially mitigate these biased estimates.
The augmented compartmental model allows the explicit modeling of different testing policies and their effects on disease estimates. Our calculations for the dependence of expected confidence on a randomized sample sizes, show that relatively small sample sizes can provide statistically significant estimates for SARS-CoV-2 related death rates.
由不同地区和不同时间点的各种检测方法衡量的 SARS-CoV-2 疾病负担，对感染率和死亡率的估计各不相同。基于这些获得的数据的模型可能会由于与检测相关的偏差而存在系统误差和较大的估计方差。仍然缺乏一种无偏的随机检测方法来估计真实的死亡率。
在这里，我们描述了在估计传染病动力学中偶然抽样偏差的影响。为此，我们在增强的隔间模型中明确建模了抽样偏差，以预测传染病动力学。我们进一步计算了来自偏差和随机抽样的疾病预测差异的偏差，并提出了一种获得无偏估计的策略。
我们的模拟表明，偏向表现出更高疾病症状的患者的抽样偏差可能会严重影响从确诊病例和死亡人数计算得出的感染率和死亡率的直接估计值，血清学检测可以部分减轻这些有偏差的估计值。
增强的隔室模型允许对不同的检测策略及其对疾病估计的影响进行明确建模。我们对随机样本量的期望置信度的依赖性的计算表明，相对较小的样本量可以为 SARS-CoV-2 相关死亡率提供具有统计学意义的估计值。
