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摘要:
目的 利用传染病动力学模型模拟我国早期新型冠状病毒肺炎的传播过程，分析和评估相关防控措施对疫情的影响。 方法 以2019年12月2日至2020年4月8日中国疾病预防控制中心传染病报告系统上报新型冠状病毒肺炎疫情数据为依据，建立动力学模型对疫情发展进行模拟分析。 结果 我国实施的交通管制措施，使R0从疫情发展初期的4.82［95%可信区间（CrI）：4.81～4.83］降低到1.13（95% CrI：1.11～1.16），新型冠状病毒感染者集中隔离和大规模核酸筛查等措施使R0进一步降低到0.31（95% CrI：0.30～0.32），模型估计得到无症状感染者的检出率为54.7%（95% CrI：45.7%～65.3%）。 保持防控措施实施的时间点不变，将无症状感染者检出率提高至75%、95%，分别能够避免新增3.4%、5.6%的感染者。 保持无症状感染者检出率不变，将防控措施实施的时间点整体前移1周、2周，分别能够避免新增78.2%、95.3%的感染者。 结论 交通管控措施显著降低了新型冠状病毒的传播速度。对感染者的集中隔离和大规模核酸筛查消除了病毒在人群中的传播。 我国实施的大规模核酸检测能够发现一半以上隐匿的无症状感染者。 相对于提高无症状感染者的检出率，防控措施的尽早实施能够避免更多的新增感染。 Abstract:Objective To simulate the full-spectrum dynamics of coronavirus disease 2019 (COVID-19) in China between 2 December 2019 and 8 April 2020 by using an epidemic dynamics model, and evaluate the impact of intervention measures on the epidemic spread. Methods Based on the incidence data of COVID-19 reported to the infectious disease reporting system of Chinese Center for Disease Control and Prevention, a dynamic model was established. Results The traffic control measures implemented in China reduced the R0 of COVID-19 from 4.82 (95% confidence interval ( CrI): 4.81−4.83) in the early stage of the epidemic to 1.13 (95% CrI: 1.11−1.16). Centralized isolation and mass nucleic acid testing further reduced the R0 to 0.31 (95% CrI: 0.30−0.32). The model estimated that the detection rate of asymptomatic infections was 54.7% (95% CrI: 45.7%−65.3%). Increasing the detection rate of asymptomatic infections to 75% and 95%, respectively, 3.4% and 5.6% of new infections would be prevented if the prevention and control measures continued for same time. Implementing the interventions one week and two weeks early, respectively, 78.2% and 95.3% of new infections would be prevented if the asymptomatic infection detection rate maintained. Conclusion Traffic control measures significantly reduced the speed of SARS-CoV-2. Centralized isolation and mass nucleic acid testing blocked the spread of SARS-CoV-2 in population. Mass nucleic acid testing in China can detect more than half of asymptomatic SARS-CoV-2 infections. Compared with increasing the detection rate of asymptomatic infections, early interventions can prevent more new infections. -
图 1 新型冠状病毒肺炎传播的人群转化关系
Figure 1. Schematics of the compartment model of COVID-19 transmission
图 2 马尔科夫链蒙特卡罗抽样的模型参数后验分布
Figure 2. Posterior distributions of Markov Chain Monte Carlo samples for calibrated model parameters
图 3 模型理论发病数与2019–2020年实际发病数对比图
Figure 3. Comparison between fitted and observed number of cases in 2019–2020
图 4 采用疫情发展初期参数对2020年疫情趋势的预测
Figure 4. Prediction of epidemic trend in 2020 using parameters from stage 1
图 5 采用交通管制期参数对2020年疫情趋势的预测
Figure 5. Prediction of epidemic trends in 2020 using parameters from stage 2
图 6 提高无症状检出率和提前实施抗疫措施所能避免的相对新增感染人数百分比
Figure 6. Percentage of new infections prevented by higher detection rate of asymptomatic infections and early interventions
表 1 模型参数的定义和取值
Table 1. Definition and value of parameters
参数 定 义 取值 来源 $ {\beta _1} $ 传染率（疫情发展初期） 1.598（1.595～1.601）a MCMC参数估计 $ {\beta _2} $ 传染率（交通管制期） 0.376（0.367～0.384）a MCMC参数估计 $ {\beta _3} $ 传染率（集中隔离和核酸筛查期） 0.127（0.121～0.133）a MCMC参数估计 r 发病患者占所有感染者的比例 0.2 文献［8］ θ 无症状和症状前感染者相对传染系数 0.3 文献［9］ $ {D_{\text{e}}} $ 潜隐期（d） 2.9 文献［7］ $ {D_{\text{p}}} $ 症状前感染期（d） 2.3 文献［6–7］ $ {D_{\text{i}}} $ 无症状、轻型和普通型患者感染期（d） 7 文献［12］ $\gamma_{1,2}$ 发病到入院持续时间（d）（疫情发展初期，交通管制期） 6 文献［7］ $ \gamma_3 $ 发病到入院持续时间（d）（集中隔离和核酸筛查期） 2 文献［7］ $ \alpha $ 确诊感染者的治愈时间（d） 10 文献［13］ $ \delta $ 感染者因大规模检测进入方舱的时间（d） 2 文献［14］ $ \omega $ 无症状感染者的检出率（%） 54.7（45.7～65.3）a MCMC参数估计 b 发病感染者进入方舱隔离的比例 0.096 文献［1, 11］ 注：MCMC. 马尔科夫链蒙特卡罗算法；a. 基于MCMC抽样的模型参数后验分布的95%可信区间 
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