School of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America.
F.I. Proctor Foundation, UCSF, San Francisco, CA, United States of America.
PLoS One. 2019 Mar 7;14(3):e0213190. doi: 10.1371/journal.pone.0213190. eCollection 2019.
As of May 27, 2018, 6 suspected, 13 probable and 35 confirmed cases of Ebola virus disease (EVD) had been reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the total outbreak size and duration with and without vaccine use. We modeled Ebola virus transmission using a stochastic branching process model that included reproduction numbers from past Ebola outbreaks and a particle filtering method to generate a probabilistic projection of the outbreak size and duration conditioned on its reported trajectory to date; modeled using high (62%), low (44%), and zero (0%) estimates of vaccination coverage (after deployment). Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize the Thiel-Sen regression model predicting the outbreak size from the number of observed cases from April 4 to May 27. We used these techniques on probable and confirmed case counts with and without inclusion of suspected cases. Probabilistic projections were scored against the actual outbreak size of 54 EVD cases, using a log-likelihood score. With the stochastic model, using high, low, and zero estimates of vaccination coverage, the median outbreak sizes for probable and confirmed cases were 82 cases (95% prediction interval [PI]: 55, 156), 104 cases (95% PI: 58, 271), and 213 cases (95% PI: 64, 1450), respectively. With the Thiel-Sen regression model, the median outbreak size was estimated to be 65.0 probable and confirmed cases (95% PI: 48.8, 119.7). Among our three mathematical models, the stochastic model with suspected cases and high vaccine coverage predicted total outbreak sizes closest to the true outcome. Relatively simple mathematical models updated in real time may inform outbreak response teams with projections of total outbreak size and duration.
截至 2018 年 5 月 27 日,刚果民主共和国赤道省报告了 6 例疑似、13 例可能和 35 例确诊埃博拉病毒病(EVD)病例。我们使用报告病例数和以前暴发的时间序列来估计总暴发规模和持续时间,同时考虑和不考虑疫苗使用情况。我们使用随机分枝过程模型来模拟埃博拉病毒的传播,该模型包括过去埃博拉暴发的繁殖数和粒子滤波方法,根据其迄今为止的报告轨迹生成暴发规模和持续时间的概率预测;模型使用高(62%)、低(44%)和零(0%)的疫苗接种覆盖率估计值(部署后)。此外,我们使用 1976 年至 2016 年期间 18 次先前埃博拉暴发的时间序列,对泰尔-塞恩回归模型进行参数化,该模型预测从 4 月 4 日至 5 月 27 日观察到的病例数得出暴发规模。我们使用包含和不包含疑似病例的可能和确诊病例的病例数对这些技术进行了评估。概率预测结果与实际的 54 例埃博拉病毒病暴发规模进行了对比,使用对数似然评分。使用随机模型,高、低和零估计的疫苗接种覆盖率,可能和确诊病例的暴发规模中位数分别为 82 例(95%预测区间[PI]:55,156)、104 例(95% PI:58,271)和 213 例(95% PI:64,1450)。使用泰尔-塞恩回归模型,估计暴发规模中位数为 65.0 例可能和确诊病例(95% PI:48.8,119.7)。在我们的三个数学模型中,包含疑似病例和高疫苗覆盖率的随机模型预测的总暴发规模最接近真实结果。相对简单的数学模型可以实时更新,为疫情应对团队提供总暴发规模和持续时间的预测。