首页> 外文期刊>BMC Infectious Diseases >Estimating effects of intervention measures on COVID-19 outbreak in Wuhan taking account of improving diagnostic capabilities using a modelling approach
【24h】

Estimating effects of intervention measures on COVID-19 outbreak in Wuhan taking account of improving diagnostic capabilities using a modelling approach

机译:干预措施对武汉Covid-19爆发的估算效应考虑了使用建模方法改进诊断能力

获取原文
           

摘要

Although by late February 2020 the COVID-19 epidemic was effectively controlled in Wuhan, China, estimating the effects of interventions, such as transportation restrictions and quarantine measures, on the early COVID-19 transmission dynamics in Wuhan is critical for guiding future virus containment strategies. Since the exact number of infected cases is unknown, the number of documented cases was used by many disease transmission models to infer epidemiological parameters. This means that it was possible to produce biased estimates of epidemiological parameters and hence of?the effects of intervention measures, because the percentage of all cases that were documented changed during the first 2?months of the epidemic, as a consequence of a gradually improving?diagnostic capability. To overcome these limitations, we constructed a stochastic susceptible-exposed-infected-quarantined-recovered (SEIQR) model, accounting for intervention measures and temporal changes in the proportion of new documented infections out of total new infections, to characterize the transmission dynamics of COVID-19 in Wuhan across different stages of the outbreak. Pre-symptomatic transmission was taken into account in our model, and all epidemiological parameters were estimated using the?Particle Markov-chain Monte Carlo (PMCMC) method. Our model captured the local Wuhan epidemic pattern as two-peak transmission dynamics, with one peak on February 4 and the other on February 12, 2020. The impact of intervention measures determined the timing of the first peak, leading to an 86% drop in the Re from 3.23 (95% CI, 2.22 to 4.20) to 0.45 (95% CI, 0.20 to 0.69). The?improved diagnostic capability led to the second peak and a higher proportion of documented infections. Our estimated proportion of new documented infections out of the total new infections increased from 11% (95% CI 1–43%) to 28% (95% CI 4–62%) after January 26 when more detection kits were released. After the introduction of a new diagnostic criterion (case definition) on February 12, a higher proportion of daily infected cases were documented (49% (95% CI 7–79%)). Transportation restrictions and quarantine measures together in Wuhan were able to contain local epidemic growth.
机译:虽然到2月20日期2020年,Covid-19流行病在中国武汉有效控制,估算干预措施的影响,如运输限制检疫措施,就武汉的早期Covid-19传播动态而言,对指导未来的病毒遏制策略至关重要。 。由于感染病例的确切数量未知,因此许多疾病传输模型使用记录病例的数量来推断出流行病学参数。这意味着有可能产生流行病学参数的偏见估计,从而产生干预措施的影响,因为所有案件的百分比因逐步改善而被记录在第一个2?几个月内发生变化?诊断能力。为了克服这些限制,我们构建了随机敏感暴露的暴露感染隔离(SEIQR)模型,占干扰措施和新的记录感染比例的时间变化,以表征Covid的传动动力学-19在武汉的爆发不同阶段。在我们的模型中考虑了前症状传输,并使用?粒子马尔可夫链蒙特卡罗(PMCMC)方法估算所有流行病学参数。我们的型号捕获了当地武汉流行模式作为两峰传输动态,2月4日的一个峰值,另一个峰,另一个峰值在2020年2月12日。干预措施的影响确定了第一个峰的时序,导致86%的阶段下降从3.23(95%CI,2.22至4.20)至0.45(95%CI,0.20至0.69)。该诊断能力改善导致第二峰值和更高比例的记录感染。当释放更多的检测试剂盒时,我们在新感染总量的新感染的估计比例从新的感染总量增加到28%(95%CI 1-43%)至28%(95%CI 4-62%)。在2月12日引入新的诊断标准(案例定义)后,记录了每日感染病例的比例较高(49%(95%CI 7-79%))。武汉在一起的运输限制和检疫措施能够遏制当地的流行病。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号