non-parametric generalized additive models with application incovid-19 data

In order to determine the model governing the variation of a random variableover time according to the variables affecting it, in recent decades, efficient methodshave been developed. one of these methods is the generalized additive model(gam). in this paper, we intend to express this method in parametric and non- parametricfashion and compare it with the segmented regression model. moreover, we willshow the efficiency of this method by providing an example of covid-19 effects.