As the world faces the possibility of recurring waves of the current novel coronavirus pandemic, it is critical to identify patterns and dynamics that could be leveraged to decrease future transmission, infection, and death rates. At this stage in the pandemic, data on disease patterns and dynamics are emerging from almost all countries in the world. Variations across countries with respect to coronavirus infection rates, public health policies, social structure, norms, health conditions, environmental policy, climate, and other factors provide us with the data to investigate the impact of different underlying factors and governmental policies on COVID-19 transmission, infection, and death rates.
Despite the fact that millions have been infected and hundreds of thousands have died from COVID-19, the available information is still insufficient for reaching precise inferences and predictions. This is because the available data on each patient are very limited, the variables of interest are highly correlated, and great uncertainty surrounds the underlying process. In addition, though the death rate from COVID-19 is high relative to other infectious diseases, from an inferential point of view, it is still very small since the number of deaths relative to those who did not die is extremely small. As a result, the observations are in the tail of the survival probability distribution. In short, the available data for analysis of COVID-19 are complex, constantly evolving, and ill-behaved. Inferring and modeling with such data results in a continuum of explanations and predictions. We need to use a modeling and inferential approach that will yield the least biased