Estimating seroconversion rates accounting for repeated infections by approximate Bayesian computation.
Estimating seroconversion rates accounting for repeated infections by approximate Bayesian computation.
复制标题
通过近似贝叶斯计算估计重复感染的血清转化率。
DOI：
10.1002/sim.9906
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发表时间：
2023
影响因子：
2
通讯作者：
Aerts,Marc
中科院分区：
文献类型：
--
作者：
Teunis,PeterFM;Wang,Yuke;Aiemjoy,Kristen;Kretzschmar,Mirjam;Aerts,Marc
这项研究提出了一种通过采用血清抗体反应的定量模型来推断感染发生率的新方法。目前的方法常常忽视个体感染史的累积效应，因此很难获得抗体浓度的边际分布。我们提出的方法利用近似贝叶斯计算来模拟横截面抗体反应，并将其与观察到的数据进行比较，同时考虑重复感染的影响。然后，我们利用柯尔莫哥洛夫偏差评估模拟和观察到的抗体数据的经验分布函数，从而纳入拟合优度检查。这种新方法不仅与之前基于似然分析的计算效率相匹配，而且还有助于抗体噪声参数的联合估计。结果证实，我们的宿主内模型生成的预测与从特征良好的群体的横截面样本中观察到的分布密切相关。我们的研究结果反映了低感染压力情况下基于可能性的方法的结果，例如欧洲百日咳的传播。然而，我们的模拟表明，在感染压力较高的情况下，基于可能性的方法往往会低估感染的力量。因此，我们的新方法在估计感染发病率方面取得了重大进展，从而增强了我们对流行病学领域疾病动态的理解。
This study presents a novel approach for inferring the incidence of infections by employing a quantitative model of the serum antibody response. Current methodologies often overlook the cumulative effect of an individual's infection history, making it challenging to obtain a marginal distribution for antibody concentrations. Our proposed approach leverages approximate Bayesian computation to simulate cross‐sectional antibody responses and compare these to observed data, factoring in the impact of repeated infections. We then assess the empirical distribution functions of the simulated and observed antibody data utilizing Kolmogorov deviance, thereby incorporating a goodness‐of‐fit check. This new method not only matches the computational efficiency of preceding likelihood‐based analyses but also facilitates the joint estimation of antibody noise parameters. The results affirm that the predictions generated by our within‐host model closely align with the observed distributions from cross‐sectional samples of a well‐characterized population. Our findings mirror those of likelihood‐based methodologies in scenarios of low infection pressure, such as the transmission of pertussis in Europe. However, our simulations reveal that in settings of higher infection pressure, likelihood‐based approaches tend to underestimate the force of infection. Thus, our novel methodology presents significant advancements in estimating infection incidence, thereby enhancing our understanding of disease dynamics in the field of epidemiology.