Multi-chain Fudan-CCDC model for COVID-19 -- a revisit to Singapore's case

Pan, Hanshuang; Shao, Nian; Yan, Yue; Luo, Xinyue; Wang, Shufen; Ye, Ling; Cheng, Jin; Chen, Wenbin

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 [8] 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 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 25% fluctuation from simulation results. It is predicted by multi-chain models that Singapore are experiencing a nonnegligible risk of explosive outbreak, thus stronger measures are urgently needed to contain the epidemic. 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 in those countries where the single-chain model shows deviation from the data.