Analysis and modeling of morbidity and mortality from coronavirus outbreak in Brazil: A case study of public health challenge using unique statistical tools

Abstract

The emerging outbreak of COVID-19 provided a valuable lesson for the global readiness with regard to the impact of the disease and management of the pandemic. The present case study reported the magnitude and public health effect based on the daily morbidity and mortality metrics from the coronavirus epidemic in Brazil as part of a series of investigations regarding the pattern and behavior of the outbreak with different geographical regions using the Pareto principle focusing on the major contributors of the worldwide pandemic. There were observable signs of multiple overlapping waves for the reported cases and deaths when using process-behavior (trending) charts. The cumulative daily census showed a significant approach to Morgan-Mercer-Flodin (MMF) model as a primary fit followed by exponential association as a secondary option with good regression. A simple and effective statistical description of the pandemic could be used as a quantitative measure for the effect of the outbreaks on the populations in a specific political region with the ability to compare between pre- and post-action events and different districts.