COVID-19: average time from infection to death in Poland, USA, India and Germany

被引:2
作者
Wiliński A. [1 ]
Kupracz Ł. [2 ]
Senejko A. [1 ]
Chrząstek G. [1 ]
机构
[1] WSB University, Gdansk
[2] Koszalin University of Technology, Koszalin
关键词
Bootstrapping; Confirmed infection cases; Correlation; Covid-19; Time series;
D O I
10.1007/s11135-022-01340-w
中图分类号
学科分类号
摘要
There are many discussions in the media about an interval (delay) from the time of the infections to deaths. Apart from the curiosity of the researchers, defining this time interval may, under certain circumstances, be of great organizational and economic importance. The study considers an attempt to determine this difference through the correlations of shifted time series and a specific bootstrapping that allows finding the distance between local maxima on the series under consideration. We consider data from Poland, the USA, India and Germany. The median of the difference’s distribution is quite consistent for such diverse countries. The main conclusion of our research is that the searched interval has rather a multimodal form than unambiguously determined. © 2022, The Author(s).
引用
收藏
页码:4729 / 4746
页数:17
相关论文
共 29 条
  • [11] Maleki M., Mahmoudi M.R., Heydari M.H., Pho K.H., Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models, Chaos, Solitons Fractals, 140, (2020)
  • [12] Maleki M., Mahmoudi M.R., Wraith D., Pho K.H., Time series modelling to forecast the confirmed and recovered cases of COVID-19, Travel Med. Infect. Dis., 37, (2020)
  • [13] Medeiros M., Street A., Valladao D., Vasconcelos G., Zilberman E., Short-Term Covid-19 Forecast for Latecomers, Arxiv Preprint, (2020)
  • [14] Munoz-Fernandez G.A., Seoane J.M., Seoane-Sepulveda J.B., A SIR-type model describing the successive waves of COVID–19, Chaos, Solitons Fractals, 144, (2021)
  • [15] Nakamura G., Grammaticos B., Deroulers C., Badoual M., Effective epidemic model for COVID-19 using accumulated deaths, Chaos, Solitons Fractals, 144, (2020)
  • [16] Oliveira T.D.P., Moral R.D.A., Global Short-Term Forecasting of Covid-19 Cases, (2020)
  • [17] Pileggi S.F., Life before COVID-19: how was the World actually performing?, Qual. Quant., 55, 5, pp. 1871-1888, (2021)
  • [18] Preacher K.J., Selig J.P., Advantages of Monte Carlo confidence intervals for indirect effects, Commun. Methods Meas., 6, 2, pp. 77-98, (2012)
  • [19] Salgotra R., Gandomi M., Gandomi A.H., Time series analysis and forecast of the COVID-19 pandemic in India using genetic programming, Chaos, Solitons Fractals, 138, (2020)
  • [20] Scortichini M., Dos Santos R.S., De'Donato F., De Sario M., Michelozzi P., Davoli M., Gasparrini A., Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time-series analysis, Int. J. Epidemiol., 49, 6, pp. 1909-1917, (2020)