Predicting the mortality due to covid-19 by the next month for Italy, Iran and South Korea; a simulation study
Gastroenterology and Hepatology from Bed to Bench,
20 September 2020
We have read with great interest the study by Shojaee et al. (1) entitled “predicting the mortality due to covid-19 by the next month for Italy, Iran and South Korea; a simulation study”. During this study, the authors precisely estimated the number of laboratory confirmed covid-19 patients and the rate of death among three countries in the next month using a “Poisson” distribution.
Poisson distribution is the common discrete probability distribution for the modeling of counts data. This type of distribution assumes an equi-dispersion of data (2). The equi-dispersion results when the variance and the mean are equal.
However, in the real life, the populations are usually non-uniform (heterogeneous) and for several count data, the mean is not necessarily equal to that variance. This phenomenon is called as a problem of over-dispersion (3). Endo et al. (4) findings indicated that the distribution of COVID-19 infection is highly over-dispersed.
It has been suggested that the most appropriate approach to deal with the problem of over-dispersion is using the “negative binomial” (NB) distribution. The NB distribution can be utilized for over-dispersed count data, when the conditional variance is higher than the conditional mean (2). If the distribution of the outcome variable is over-dispersed, then the confidence intervals (CIs) for the negative binomial distribution are probably to be narrower than those derived from a Poisson distribution.
In conclusion, we suggest to simulate the mortality rate in COVID-19 studies using the “negative binomial” distribution. This distribution could provide more flexible functional forms than Poison distribution to accommodate over-dispersion.
- Poisson distribution
- Negative binomial distribution
Shojaee S, Pourhoseingholi MA, Ashtari S, Vahedian-Azimi A, Asadzadeh-Aghdaei H, Zali MR. Predicting the mortality due to Covid-19 by the next month for Italy, Iran and South Korea; a simulation study. Gastroenterol Hepatol Bed Bench 2020; 13(2): 177-179.
Cameron AC, Trivedi PK. Regression analysis of count data. 2nd edition. 2013; New York: Cambridge University Press.
Benlagha N. Modeling the declared new cases of COVID-19 trend using advanced statistical approaches. Available from: https://www.researchgate.net/publication/340298857_Modeling_the_Declared_New_Cases_of_COVID-19_Trend_Using_Advanced_Statistical_Approaches.
Endo A, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Abbott S, Kucharski AJ, Funk S. Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China. Wellcome Open Res 2020; 5: 67.
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