The Interdisplinary Research Center for Mathematics and Life Sciences, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China; Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, M3J 1P3, Canada.
The Interdisplinary Research Center for Mathematics and Life Sciences, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China; School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.
Int J Infect Dis. 2020 Jun;95:288-293. doi: 10.1016/j.ijid.2020.03.018. Epub 2020 Apr 17.
Since January 23rd 2020, stringent measures for controlling the novel coronavirus epidemics have been gradually enforced and strengthened in mainland China. The detection and diagnosis have been improved as well. However, the daily reported cases staying in a high level make the epidemics trend prediction difficult.
Since the traditional SEIR model does not evaluate the effectiveness of control strategies, a novel model in line with the current epidemics process and control measures was proposed, utilizing multisource datasets including cumulative number of reported, death, quarantined and suspected cases.
Results show that the trend of the epidemics mainly depends on quarantined and suspected cases. The predicted cumulative numbers of quarantined and suspected cases nearly reached static states and their inflection points have already been achieved, with the epidemics peak coming soon. The estimated effective reproduction numbers using model-free and model-based methods are decreasing, as well as new infections, while new reported cases are increasing. Most infected cases have been quarantined or put in suspected class, which has been ignored in existing models.
The uncertainty analyses reveal that the epidemics is still uncertain and it is important to continue enhancing the quarantine and isolation strategy and improving the detection rate in mainland China.
自 2020 年 1 月 23 日以来，中国大陆逐步加强了新型冠状病毒疫情的控制措施，检测和诊断能力也有所提高。然而，每日报告的病例居高不下，使得疫情趋势预测变得困难。
由于传统的 SEIR 模型不能评估控制策略的效果，因此提出了一种新的模型，该模型符合当前疫情的发展过程和控制措施，利用包括报告病例、死亡病例、隔离病例和疑似病例在内的多源数据集。
结果表明，疫情趋势主要取决于隔离和疑似病例。预测的隔离和疑似病例累计数量几乎达到了静态状态，拐点已经出现，疫情高峰即将到来。使用无模型和基于模型的方法估计的有效繁殖数都在减少，而新的感染和新的报告病例都在增加。大多数感染病例已经被隔离或归入疑似病例，这在现有模型中被忽略了。
不确定性分析表明，疫情仍不确定，继续加强隔离和隔离策略以及提高中国大陆的检测率非常重要。
