Dexamethasone can reduce mortality in hospitalised COVID-19 patients needing oxygen and ventilation by 18% and 36%, respectively. Here, we estimate the potential number of lives saved and life years gained if this treatment were to be rolled out in the UK and globally, as well as the cost-effectiveness of implementing this intervention. Assuming SARS-CoV-2 exposure levels of 5% to 15%, we estimate that, for the UK, approximately 12,000 (4,250 - 27,000) lives could be saved between July and December 2020. Assuming that dexamethasone has a similar effect size in settings where access to oxygen therapies is limited, this would translate into approximately 650,000 (240,000 - 1,400,000) lives saved globally over the same time period. If dexamethasone acts differently in these settings, the impact could be less than half of this value. To estimate the full potential of dexamethasone in the global fight against COVID-19, it is essential to perform clinical research in settings with limited access to oxygen and/or ventilators, for example in low- and middle-income countries.

Background COVID-19 is the most informative pandemic in history. These unprecedented recorded data give rise to some novel concepts, discussions, and models. Macroscopic modeling of the period of hospitalization is one of these new issues. Methods Modeling of the lag between diagnosis and death is done by using two classes of macroscopic analytical methods: the correlation-based methods based on Pearson, Spearman, and Kendall correlation coefficients, and the logarithmic methods of two types. Also, we apply eight weighted average methods to smooth the time series before calculating the distance. We consider five lags with the least distance. All the computations are conducted on Matlab R2015b. Results The length of hospitalization for the fatal cases in the USA, Italy, and Germany are 2–10, 1–6, and 5–19 days, respectively. Overall, this length in the USA is two days more than in Italy and five days less than in Germany. Conclusion We take the distance between the diagnosis and death as the length of hospitalization. There is a negative association between the length of hospitalization and the case fatality rate. Therefore, the estimation of the length of hospitalization by using these macroscopic mathematical methods can be introduced as an indicator to scale the success of the countries fighting the ongoing pandemic.

In this paper, we propose a new class of bivariate Farlie-Gumbel-Morgenstern (FGM) copula. This class includes some known extensions of FGM copulas. Some general formulas for well-known association measures of this class are obtained, and various properties of the proposed model are studied. The tail dependence range of the new class is 0 to 1, and its correlation range is more efficient. We apply some sub-families of the proposed new class to model a dataset of medical science to show the superiority of our approach in comparison with the presented generalized FGM family in the literature. We also present a method to simulate from our generalized FGM copula, and validate our method and its accuracy using the simulation results to recover the same dependency structure of the original data.

In this paper, we propose an asymmetric class of bivariate copulas.This class is obtained through limiting properties of the extended copula introduced by Bekrizadeh, et al. (2015), and includes some of known copulas. Some general formulas for well-known association measures and concepts of dependence of the proposed model are obtained. This paper highlights the usefulness of this new bivariate copula for modeling the interested variables whose marginal distribution effect on joint distribution isn't identical. We apply some sub-families of this new class to model a dataset of medical science to show the superiority of presented model in comparison with the known copulas. These results will be investigated using simulation.

Background Applying recent advances in medical instruments, information technology, and unprecedented data sharing into COVID-19 research revolutionized medical sciences, and causes some unprecedented analyses, discussions, and models. Methods Modeling of this dependency is done using four classes of copulas: Clayton, Frank, Gumbel, and FGM. The estimation of the parameters of the copulas is obtained using the maximum likelihood method. To evaluate the goodness of fit of the copulas, we calculate AIC. All computations are conducted on Matlab R2015b, R 4.0.3, Maple 2018a, and EasyFit 5.6, and the plots are created on software Matlab R2015b and R 4.0.3. Results As time passes, the number of tests increases, and the positivity rate becomes lower. The epidemic peaks are occasions that violate the stated general rule –due to the early growth of the number of tests. If we divide data of each country into peaks and otherwise, about both of them, the rising number of tests is accompanied by decreasing the positivity rate. Conclusion The positivity rate can be considered a representative of the level of the spreading. Approaching zero positivity rate is a good criterion to scale the success of a health care system in fighting against an epidemic. We expect that if the number of tests is great enough, the positivity rate does not depend on the number of tests. Accordingly, the number and accuracy of tests can play a vital role in the quality level of epidemic data. Key messages - In a country, increasing the positivity rate is more representative than increasing the number of tests to warn about an epidemic peak. - Approaching zero positivity rate is a good criterion to scale the success of a health care system in fighting against an epidemic. - Except for the first half of the epidemic peaks, in a country, the higher number of tests is associated with a lower positivity rate. - In countries with high test per million, there is no significant dependency between the number of tests and positivity rate.