Abstract
Since the spark of the recent Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), public
health concerns have motivated the accumulation of a vast amount of data about the Coronavirus
Disease 2019 (COVID-19). The most important metrics for the pandemic progression are the recorded
cases and reported deaths datasets which were comprehensively collected pertaining to the outbreak.
The reliance on the census of morbidity and mortality lists solely appeared to be inadequate to assess
or forecast the disease. It is proposed that a significant extension of this data should be amended to be
much more useful for public health authorities and official organizations. It would be plausible to adopt
a practical use of quantitative metrics that could be easily understandable and applied for measuring
such a catastrophic pandemic. Three parameters that might be observed primarily involve assessing
the outbreak magnitude, rate of change with time and the degree of stability of the difference in the rate
of morbidities and mortalities at different intervals. In addition, empirical modeling implementation using
the curve-fitting approach could be conducted to describe the pattern of the epidemic according to the
cumulative daily datasets
Publisher
Bulent Evcevit University