Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
Interuniversity Institute of Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium.
PLoS Comput Biol. 2021 Mar 29;17(3):e1008892. doi: 10.1371/journal.pcbi.1008892. eCollection 2021 Mar.
The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.
SARS-CoV-2 病原体目前正在全球范围内传播，其具有症状前和无症状传播的倾向，使得其难以控制。几个国家采取的控制措施旨在一旦确诊就对个体进行隔离，限制他们的社交互动，从而降低其传播概率。这些干预措施对疾病动态具有强烈影响，可能会影响对流行病学数量的推断。我们首先对非药物干预措施对平均序列和世代间隔的影响提出了理论解释。然后，在模拟研究中，我们改变了控制措施的假设效果，并量化了对实际世代间隔和序列间隔的平均值和方差的影响。模拟结果表明，实际序列间隔和世代间隔都取决于控制措施，并且根据干预策略的效果收缩。有趣的是，平均序列间隔与平均世代间隔不同。这两个值之间的偏差取决于两个因素。首先，未确诊的感染个体数量。其次，传染性、症状出现和隔离时间之间的关系。同样，实际序列和世代间隔的标准差也不重合，前者平均比后者短。本研究的结果与当前 COVID-19 大流行所进行的估计直接相关。特别是，有效繁殖数通常使用每日发病率数据和世代间隔进行推断。如果不考虑序列间隔的收缩或错误指定，可能会导致对有效繁殖数的有偏差估计。因此，这可能会影响决策者在根据数量值决定应用哪种控制措施时做出的选择。
