用于新冠肺炎的多链复旦-中国疾控中心模型——对新加坡病例的再探讨
Multi-chain Fudan-CCDC model for COVID-19-a revisit to Singapore's case.
作者信息
Pan Hanshuang, Shao Nian, Yan Yue, Luo Xinyue, Wang Shufen, Ye Ling, Cheng Jin, Chen Wenbin
机构信息
School of Mathematical Sciences, Fudan University, Shanghai, 200433 China.
School of Mathematics, Shanghai University of Finance and Economics, Shanghai, 200433 China.
出版信息
Quant Biol. 2020;8(4):325-335. doi: 10.1007/s40484-020-0224-3. Epub 2020 Nov 23.
BACKGROUND
COVID-19 has been impacting on the whole world critically and constantly since late December 2019. Rapidly increasing infections has raised intense worldwide attention. How to model the evolution of COVID-19 effectively and efficiently is of great significance for prevention and control.
METHODS
We propose the multi-chain Fudan-CCDC model based on the original single-chain model in [Shao et al. 2020] to describe the evolution of COVID-19 in Singapore. Multi-chains can be considered as the superposition of several single chains with different characteristics. We identify the parameters of models by minimizing the penalty function.
RESULTS
The numerical simulation results exhibit the multi-chain model performs well on data fitting. Though unsteady the increments are, they could still fall within the range of _30% fluctuation from simulation results.
CONCLUSION
The multi-chain Fudan-CCDC model provides an effective way to early detect the appearance of imported infectors and super spreaders and forecast a second outbreak. It can also explain the data from those countries where the single-chain model shows deviation from the data.
背景
自2019年12月下旬以来，新型冠状病毒肺炎（COVID-19）一直在严重且持续地影响着全球。感染人数的迅速增加引起了全球的高度关注。如何有效且高效地对COVID-19的演变进行建模对于防控具有重要意义。
方法
我们基于[Shao等人，2020]中的原始单链模型提出了多链复旦-中国疾病预防控制中心（Fudan-CCDC）模型，以描述新加坡COVID-19的演变。多链可被视为具有不同特征的若干单链的叠加。我们通过最小化惩罚函数来确定模型的参数。
结果
数值模拟结果表明多链模型在数据拟合方面表现良好。尽管增量不稳定，但仍可落在模拟结果波动±30%的范围内。
结论
多链复旦-CCDC模型为早期发现输入感染者和超级传播者的出现以及预测二次爆发提供了一种有效方法。它还可以解释单链模型与数据出现偏差的那些国家的数据。
Zotero 插件