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 va...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).展开更多
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 o...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.展开更多
基金funded in part by NSERC Alliance COVID-19 grant(ALLRP 555037-20)NSERC Discovery grant(RGPIN-2020-04134 Li)the CIHR funded Mathematical Modelling of COVID-19 Task Force,and the NSERC-PHAC EIDM Network“Mathematics for Public Health(MfPH)”.
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).
基金Research of MYL is supported in part by the Natural Science and Engineering Research Council(NSERC)of Canada and Canada Foundation for Innovation(CFI).
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.
