Zhou Yimin, Li Jun, Chen Zuguo, Luo Qingsong, Wu Xiangdong, Ye Lingjian, Ni Haiyang, Fei Chunnan
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
School of International Relations, Sun Yat-sen University, Guangzhou, China.
J Thorac Dis. 2020 Oct;12(10):5739-5755. doi: 10.21037/jtd-20-1315.
Since the outbreak of novel coronavirus disease (COVID-19) in Wuhan, China at the beginning of December 2019, there have been over 11,200,000 confirmed cases in the world as of the 3 July 2020, affecting over 213 countries and regions with nearly 530,000 deaths. The pandemic has been sweeping all continents, North America, Latin America, Europe, Middle East and South Asia among others at an alarming rapidity. Here, we provide an estimate of the scale of the pandemic spread under different scenarios of variation in key influencing parameters with a hybrid model.
We developed a new hybrid model of infectious disease transmission based on Cellular Automata (CA)-configured SEIR to analyse the COVID-19 outbreak and estimate its transmission pattern. A probabilistic contamination network is embedded in the pandemic transmission model to capture the randomness feature of person-to-person spread of the novel virus. We used the improved SEIR model to quantify the population contact state with isolation measures under different continuous time series contact probability via CA. We adjusted the modelling parameters to verify the model performance in accordance to the data from the reports published by the Chinese Center for Disease Control and Prevention. We simulated several scenarios by varying such key parameters as number of isolation rate, average contact times of the population, number of infected people before taking prevention and control measures, medical level and number of imported cases.
In the baseline model, we identified that the isolation control as the most influencing factor that had the largest impact on decreasing the speed of the reproductive number, accelerating the arrival of the "inflection point" of pandemic prevention and control, and the death rate reduction. We estimated that the probability of people contacts and the number of the onset infected cases before prevention measures also had significant effect on the infection rate reduction with appropriate prevention measures adoption, which partly reflects the impact of timely measure on the severity of the outbreak. We found that imported cases will risk the domestic prevention.
Our modelling results clearly indicate that early-stage preventive measures are the most effective way to contain the pandemic spread and a strong interventionist approach needs to be adopted by policymakers vis-à-vis of the highly contagious nature of the COVID-19. Human resources, intensified isolation and confinement as well as special hospital buildings should be prioritised in countries with large number of infections to constrain the global transmission of the virulent infection. To do so, internationally coordinated actions require to be taken to replicate good practices to less infected countries and regions immediately.
自2019年12月初中国武汉爆发新型冠状病毒病（COVID-19）以来，截至2020年7月3日，全球确诊病例已超过1120万例，影响了213个以上国家和地区，造成近53万人死亡。这场大流行以惊人的速度席卷了各大洲，包括北美、拉丁美洲、欧洲、中东和南亚等。在此，我们用一种混合模型对关键影响参数变化的不同情况下大流行传播的规模进行了估计。
我们基于元胞自动机（CA）配置的SEIR开发了一种新的传染病传播混合模型，以分析COVID-19疫情并估计其传播模式。在大流行传播模型中嵌入了一个概率污染网络，以捕捉新型病毒人际传播的随机性特征。我们使用改进的SEIR模型，通过CA在不同连续时间序列接触概率下量化有隔离措施时的人群接触状态。我们根据中国疾病预防控制中心发布的报告数据调整建模参数，以验证模型性能。我们通过改变隔离率、人群平均接触次数、采取防控措施前的感染人数、医疗水平和输入病例数等关键参数，模拟了几种情况。
在基线模型中，我们确定隔离控制是对降低繁殖数速度、加速疫情防控“拐点”到来以及降低死亡率影响最大的因素。我们估计，人群接触概率和采取预防措施前的发病感染病例数对通过采取适当预防措施降低感染率也有显著影响，这部分反映了及时措施对疫情严重程度的影响。我们发现输入病例会给国内防控带来风险。
我们的建模结果清楚地表明，早期预防措施是遏制大流行传播的最有效方法，鉴于COVID-19具有高度传染性，政策制定者需要采取强有力的干预措施。在感染人数众多的国家，应优先考虑人力资源、加强隔离和封闭以及特殊医院建设，以遏制这种烈性感染的全球传播。为此，需要采取国际协调行动，立即将良好做法推广到感染较少的国家和地区。
