论文标题
一种修改的易感感染的模型，用于观察到的未报告的发病率数据
A modified Susceptible-Infected-Recovered model for observed under-reported incidence data
论文作者
论文摘要
当并非所有受感染的个体报告时，拟合易感感染的被感染的（SIR）模型与发病率数据是有问题的。假设未观察到新感染的个体的固定分数时，假设本文恢复了一般但已知分布的一般但已知分布的分布，则在观察到的发病率数据中得出了隐含的差分综合方程。可测量的微分方程系统的参数可识别。使用这些微分方程，我们开发了一个随机模型，用于鉴于报告病例的整个历史，当前疾病发病率的条件分布。我们使用后部分布的贝叶斯马尔可夫链蒙特卡洛采样估计模型参数。我们使用我们的模型来估计当前的冠状病毒2019年爆发的无症状个体的传播率和分数：美国，巴西，墨西哥，阿根廷，智利，哥伦比亚，哥伦比亚，秘鲁，秘鲁和巴拿马，从2020年1月至2021年5月到2021年，我们的分析始终如一地揭示了40-60％的爆发。这两个例外是墨西哥和秘鲁，在墨西哥有严重的报告。
Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that consistently, about 40-60% of the infections were not observed in the American outbreaks. The two exception are Mexico and Peru, with acute under-reporting in Mexico.
