An application of a mixture of exponential distributions for assessing hazard rates from COVID-19
Main Article Content
Keywords
hazard rate, infectious disease, mixture of exponentials, nonstationary time series
Abstract
In the present study, we are interested in modeling repose time periods (the length of the time intervals between successive deaths) caused by a new, widespread disease called covid-19. This is useful for predicting probabilities of new deaths that occur within pre-determined time intervals. In practical applications, the choice of the statistical model is crucial for obtaining accurate estimates of death hazard rates. Based on an earlier research, we propose to use a mixture of exponential distributions; this model is simple to implement when hazard rates obtained from the components of the mixture are easily calculated, and it is adequate for dealing with nonstationary time series as those appearing in the case of this disease. The model is then applied to the example of Italy, and it appears to be also useful for comparing hazard rates along time.
References
1. Mendoza-Rosas AT, De la Cruz-Reyna S. A mix-ture of exponentials distribution for a simple and precise assessment of the volcanic hazard. Nat Hazards Earth Syst Sci 2009;9:425–31. http://dx. doi.org/10.5194/nhess-9-425-2009.
2. Giangaspero M. Covid-19 epidemic in Italy: Lesson learning. J Fam Med Dis Prev 2020;6:119. http://dx.doi.org/10.23937/2469-5793/1510119.
3. Giordano G, Blanchini F, Bruno R, et al. Modelling the covid-19 epidemic and implementation of population-wide interventions in Italy. www.nature. com/articles/s41591-020-0883-7, 2020.
4. Titterington DM, Smith AFM, Makov UE. Statistical analysis of finite mixture distributions. New York: John Wiley & Sons, 1985:74–5.
5. Conover WJ. Practical nonparametric statistics. 3rd ed. New York: Wiley Series in Probability and Statistics, 1999:430–5.
6. Indrayan A, Holt MP. Concise encyclopedia of biostatistics for medical professionals, 1st ed. Boca Raton (Florida): CRC Press, Chapman Hall, 2016:227–8.
2. Giangaspero M. Covid-19 epidemic in Italy: Lesson learning. J Fam Med Dis Prev 2020;6:119. http://dx.doi.org/10.23937/2469-5793/1510119.
3. Giordano G, Blanchini F, Bruno R, et al. Modelling the covid-19 epidemic and implementation of population-wide interventions in Italy. www.nature. com/articles/s41591-020-0883-7, 2020.
4. Titterington DM, Smith AFM, Makov UE. Statistical analysis of finite mixture distributions. New York: John Wiley & Sons, 1985:74–5.
5. Conover WJ. Practical nonparametric statistics. 3rd ed. New York: Wiley Series in Probability and Statistics, 1999:430–5.
6. Indrayan A, Holt MP. Concise encyclopedia of biostatistics for medical professionals, 1st ed. Boca Raton (Florida): CRC Press, Chapman Hall, 2016:227–8.
Article Sidebar
Published
Aug 14, 2020
Article Details
How to Cite
Polymenis, A. (2020). An application of a mixture of exponential distributions for assessing hazard rates from COVID-19. Journal of Population Therapeutics and Clinical Pharmacology, 27(SP1), e58-e63. https://doi.org/10.15586/jptcp.v27iSP1.721
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Polymenis, A. (2020). An application of a mixture of exponential distributions for assessing hazard rates from COVID-19. Journal of Population Therapeutics and Clinical Pharmacology, 27(SP1), e58-e63. https://doi.org/10.15586/jptcp.v27iSP1.721