摘要
This paper provides a mathematical model that makes it clearly visible why the underestimation of r,the fraction of asymptomatic COVID-19 carriers in the general population,may lead to a catastrophic reliance on the standard policy intervention that attempts to isolate all confirmed infectious cases.The SE(AþO)R model with infectives separated into asymptomatic and ordinary carriers,supplemented by a model of the data generation process,is calibrated to standard early pandemic datasets for two countries.It is shown that certain fundamental parameters,critically r,are unidentifiable with this data.A general analytical framework is presented that projects the impact of different types of policy intervention.It is found that the lack of parameter identifiability implies that some,but not all,potential policy interventions can be correctly predicted.In an example representing Italy in March 2020,a hypothetical optimal policy of isolating confirmed cases that aims to reduce the basic reproduction number R0 of the outbreak from 4.4 to 0.8 assuming r¼0,only achieves 3.8 if it turns out that r¼40%.
