We estimate the distribution of COVID-19 mortality(measured as daily deaths)from the start of the pandemic until July 31st,2022,for six European countries and the USA.We use the Pareto,the stretched exponential,the lo...We estimate the distribution of COVID-19 mortality(measured as daily deaths)from the start of the pandemic until July 31st,2022,for six European countries and the USA.We use the Pareto,the stretched exponential,the log-normal and the log-logistic distributions as well as mixtures of the log-normal and log-logistic distributions.The main results are that the Pareto does not describe well the data and that mixture distributions tend to offer a very good fit to the data.We also compute Value-at-Risk measures as well as mortality probabilities with our estimates.We also discuss the implications of our results and findings from the point of view of public health planning and modelling.展开更多
I use extreme values theory and data on influenza mortality from the U.S.for 1900 to 2018 to estimate the tail risks of mortality.I find that the distribution for influenza mortality rates is heavy-tailed,which sugges...I use extreme values theory and data on influenza mortality from the U.S.for 1900 to 2018 to estimate the tail risks of mortality.I find that the distribution for influenza mortality rates is heavy-tailed,which suggests that the tails of the mortality distribution are more informative than the events of high frequency(i.e.,years of low mortality).I also discuss the implications of my estimates for risk management and pandemic planning.展开更多
基金supported by the Spanish Ministerio de Ciencia e Innovaciòn(PID 2020-112773 GB-I00)by Gobierno de Aragòn(ADETRE Reference GroupS39_20R).
文摘We estimate the distribution of COVID-19 mortality(measured as daily deaths)from the start of the pandemic until July 31st,2022,for six European countries and the USA.We use the Pareto,the stretched exponential,the log-normal and the log-logistic distributions as well as mixtures of the log-normal and log-logistic distributions.The main results are that the Pareto does not describe well the data and that mixture distributions tend to offer a very good fit to the data.We also compute Value-at-Risk measures as well as mortality probabilities with our estimates.We also discuss the implications of our results and findings from the point of view of public health planning and modelling.
文摘I use extreme values theory and data on influenza mortality from the U.S.for 1900 to 2018 to estimate the tail risks of mortality.I find that the distribution for influenza mortality rates is heavy-tailed,which suggests that the tails of the mortality distribution are more informative than the events of high frequency(i.e.,years of low mortality).I also discuss the implications of my estimates for risk management and pandemic planning.
