ABSTRACT
BackgroundApplying 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. MethodsModeling 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. ResultsAs 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. ConclusionThe 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.
