This paper examines a recently developed statistical approach for evaluating the effectiveness of vaccination campaigns in terms of deaths averted.The statistical approach makes predictions by comparing death rates in...This paper examines a recently developed statistical approach for evaluating the effectiveness of vaccination campaigns in terms of deaths averted.The statistical approach makes predictions by comparing death rates in the vaccinated and unvaccinated populations.The statistical approach is preferred for its simplicity and straightforwardness,especially when compared to the difficulties involved when fitting the many parameters of a dynamic SIRD-type model,which may even be an impossible task.We compared the estimated number of deaths averted by the statistical approach to the“ground truth”number of deaths averted in a relatively simple scheme(e.g.,constant vaccination,constant R_(0),pure SIR dynamics,no age stratification)through mathematical analysis,and quantified the difference and degree of underestimation.The results indicate that the statistical approach consistently produces conservative estimates and will always underestimate the number of deaths averted by the direct effect of vaccination,and thus obviously the combined total effect(direct and indirect effect).For high R_(0)values(e.g.R_(0)8),the underestimation is relatively small as long as the vaccination level(v)remains below the herd immunity vaccination threshold.However,for low R_(0)values(e.g.R_(0)1.5),the statistical approach significantly underestimates the number of deaths averted by vaccination,with the underestimation greater than 20%.Applying an approximate correction to the statistical approach,however,can improve the accuracy of estimates for low R_(0)and low v.In conclusion,the statistical approach can provide reasonable estimates in scenarios involving high R_(0)values and low v,such as during the Omicron variant epidemic in Australia.For low R_(0)values and low v,applying an approximate correction to the statistical approach can lead to more accurate estimates,although there are caveats even for this.These results suggest that the statistical method needs to be used with caution.展开更多
In late March 2020,SARS-CoV-2 arrived in Manaus,Brazil,and rapidly developed into a large-scale epidemic that collapsed the local health system and resulted in extreme death rates.Several key studies reported that∼76...In late March 2020,SARS-CoV-2 arrived in Manaus,Brazil,and rapidly developed into a large-scale epidemic that collapsed the local health system and resulted in extreme death rates.Several key studies reported that∼76%of residents of Manaus were infected(attack rate AR≃76%)by October 2020,suggesting protective herd immunity had been reached.Despite this,an unexpected second wave of COVID-19 struck again in November and proved to be larger than the first,creating a catastrophe for the unprepared population.It has been suggested that this could be possible if the second wave was driven by reinfections.However,it is widely reported that reinfections were at a low rate(before the emergence of Omicron),and reinfections tend to be mild.Here,we use novel methods to model the epidemic from mortality data without considering reinfection-caused deaths and evaluate the impact of interventions to explain why the second wave appeared.The method fits a“flexible”reproductive numberR_(0)(t)that changes over the epidemic,and it is demonstrated that the method can successfully reconstruct R_(0)(t)from simulated data.For Manaus,the method finds AR≃34%by October 2020 for the first wave,which is far less than required for herd immunity yet in-line with seroprevalence estimates.The work is complemented by a two-strain model.Using genomic data,the model estimates transmissibility of the new P.1 virus lineage as 1.9 times higher than that of the non-P.1.Moreover,an age class model variant that considers the high mortality rates of older adults show very similar results.These models thus provide a reasonable explanation for the two-wave dynamics in Manaus without the need to rely on large reinfection rates,which until now have only been found in negligible to moderate numbers in recent surveillance efforts.展开更多
基金supported by the Australian Research Council(Grant No.:DP240102585)。
文摘This paper examines a recently developed statistical approach for evaluating the effectiveness of vaccination campaigns in terms of deaths averted.The statistical approach makes predictions by comparing death rates in the vaccinated and unvaccinated populations.The statistical approach is preferred for its simplicity and straightforwardness,especially when compared to the difficulties involved when fitting the many parameters of a dynamic SIRD-type model,which may even be an impossible task.We compared the estimated number of deaths averted by the statistical approach to the“ground truth”number of deaths averted in a relatively simple scheme(e.g.,constant vaccination,constant R_(0),pure SIR dynamics,no age stratification)through mathematical analysis,and quantified the difference and degree of underestimation.The results indicate that the statistical approach consistently produces conservative estimates and will always underestimate the number of deaths averted by the direct effect of vaccination,and thus obviously the combined total effect(direct and indirect effect).For high R_(0)values(e.g.R_(0)8),the underestimation is relatively small as long as the vaccination level(v)remains below the herd immunity vaccination threshold.However,for low R_(0)values(e.g.R_(0)1.5),the statistical approach significantly underestimates the number of deaths averted by vaccination,with the underestimation greater than 20%.Applying an approximate correction to the statistical approach,however,can improve the accuracy of estimates for low R_(0)and low v.In conclusion,the statistical approach can provide reasonable estimates in scenarios involving high R_(0)values and low v,such as during the Omicron variant epidemic in Australia.For low R_(0)values and low v,applying an approximate correction to the statistical approach can lead to more accurate estimates,although there are caveats even for this.These results suggest that the statistical method needs to be used with caution.
基金DH was supported by Hong Kong Research Grants Council Collaborative Research Fund(C5079-21G).
文摘In late March 2020,SARS-CoV-2 arrived in Manaus,Brazil,and rapidly developed into a large-scale epidemic that collapsed the local health system and resulted in extreme death rates.Several key studies reported that∼76%of residents of Manaus were infected(attack rate AR≃76%)by October 2020,suggesting protective herd immunity had been reached.Despite this,an unexpected second wave of COVID-19 struck again in November and proved to be larger than the first,creating a catastrophe for the unprepared population.It has been suggested that this could be possible if the second wave was driven by reinfections.However,it is widely reported that reinfections were at a low rate(before the emergence of Omicron),and reinfections tend to be mild.Here,we use novel methods to model the epidemic from mortality data without considering reinfection-caused deaths and evaluate the impact of interventions to explain why the second wave appeared.The method fits a“flexible”reproductive numberR_(0)(t)that changes over the epidemic,and it is demonstrated that the method can successfully reconstruct R_(0)(t)from simulated data.For Manaus,the method finds AR≃34%by October 2020 for the first wave,which is far less than required for herd immunity yet in-line with seroprevalence estimates.The work is complemented by a two-strain model.Using genomic data,the model estimates transmissibility of the new P.1 virus lineage as 1.9 times higher than that of the non-P.1.Moreover,an age class model variant that considers the high mortality rates of older adults show very similar results.These models thus provide a reasonable explanation for the two-wave dynamics in Manaus without the need to rely on large reinfection rates,which until now have only been found in negligible to moderate numbers in recent surveillance efforts.
