Abstract:
In epidemiology, it is often the case that two or more correlated
count response variables are encountered. Under this scenario, it is
more efficient to model the data using a joint model. Besides, if one of
these count variables has an excess of zeros (spike at zero) the log link
cannot be used in general. The situation is more complicated when
the data is grouped into clusters. A Generalized Linear Mixed Model
(GLMM) is used to accommodate this cluster covariance. The objective
of this research is to develop a new modeling approach that can
handle this situation. The method is illustrated on a global data set of
Covid 19 patients. The important conclusions are that the new model
was successfully implemented both in theory and practice. A plot of
the residuals indicated a well-fitting model to the data
