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您的检索式：作者名="Michael Y.Li"
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| 1 | Why is it difficult to accurately predict the COVID-19 epidemic?显示文摘Since the COVID-19 outbreak in Wuhan City in December of 2019,numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported.These model predictions have shown a wide range of variations.In our study,we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations.Using the Akaike Information Criterion(AIC)for model selection,we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data.This indicates that predictions using more complex models may not be more reliable compared to using a simpler model.We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23,2020.We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic,and modeling the potential of a second outbreak after the return-to-work in the city. | Weston C.Roda Marie B.Varughese Donglin Han Michael Y.Li | 2020 | Infectious Disease Modelling2020，5，1： | 5 |
| 2 | Nonpharmaceutical interventions contribute to the control of COVID-19 in China based on a pairwise model显示文摘Nonpharmaceutical interventions(NPIs),particularly contact tracing isolation and household quarantine,play a vital role in effectively bringing the Coronavirus Disease 2019(COVID-19)under control in China.The pairwise model,has an inherent advantage in characterizing those two NPIs than the classical well-mixed models.Therefore,in this paper,we devised a pairwise epidemic model with NPIs to analyze COVID-19 outbreak in China by using confirmed cases during February 3rde22nd,2020.By explicitly incorporating contact tracing isolation and family clusters caused by household quarantine,our model provided a good fit to the trajectory of COVID-19 infections.We calculated the reproduction number R=1.345(95%CI:1.230-1.460)for Hubei province and R=1.217(95%CI:1.207-1.227)for China(except Hubei).We also estimated the peak time of infections,the epidemic duration and the final size,which are basically consistent with real observation.We indicated by simulation that the traced high-risk contacts from incubated to susceptible decrease under NPIs,regardless of infected cases.The sensitivity analysis showed that reducing the exposure of the susceptible and increasing the clustering coefficient bolster COVID-19 control.With the enforcement of household quarantine,the reproduction number R and the epidemic prevalence declined effectively.Furthermore,we obtained the resumption time of work and production in China(except Hubei)on 10th March and in Hubei at the end of April 2020,respectively,which is broadly in line with the actual time.Our results may provide some potential lessons from China on the control of COVID-19 for other parts of the world. | Xiao-Feng Luo Shanshan Feng Junyuan Yang Xiao-Long Peng Xiaochun Cao Juping Zhang Meiping Yao Huaiping Zhu Michael Y.Li Hao Wang Zhen Jin | 2021 | Infectious Disease Modelling2021，6，1： | 2 |
| 3 | Mathematical modeling of the dynamics of COVID-19 variants of concern:Asymptotic and finite-time perspectives显示文摘The COVID-19 pandemic has seen multiple waves,in part due to the implementation and relaxation of social distancing measures by the public health authorities around the world,and also caused by the emergence of new variants of concern(VOCs)of the SARS-Cov-2 virus.As the COVID-19 pandemic is expected to transition into an endemic state,how to manage outbreaks caused by newly emerging VOCs has become one of the primary public health issues.Using mathematical modeling tools,we investigated the dynamics of VOCs,both in a general theoretical framework and based on observations from public health data of past COVID-19 waves,with the objective of understanding key factors that determine the dominance and coexistence of VOCs.Our results show that the transmissibility advantage of a new VOC is a main factor for it to become dominant.Additionally,our modeling study indicates that the initial number of people infected with the new VOC plays an important role in determining the size of the epidemic.Our results also support the evidence that public health measures targeting the newly emerging VOC taken in the early phase of its spread can limit the size of the epidemic caused by the new VOC(Wu et al.,2139Wu,Scarabel,Majeed,Bragazzi,&Orbinski,Wu et al.,2021). | Adriana-Stefania Ciupeanu Marie Varughese Weston C.Roda Donglin Han Qun Cheng Michael Y.Li | 2022 | Infectious Disease Modelling2022，7，4： | 0 |
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