ABSTRACT
Since the emergence of the Coronavirus disease outbreak, the world has witnessed great changes that have impacted humanity. A study of the pattern of the pandemic would be of great importance to understand the trending behavior for the spreading of the disease within any country. Visualization of the outbreak progression - through accumulated records in the datasets - using statistical tools showed that the initial fast increase rate of the affected cases in the original province in China was followed by a stability period till the end of the reporting date. Hong Kong - which was next to Hubei province in the cases - showed a different surge of slow growth curve with distinct major wave levels. The remaining territories showed a much smaller magnitude of morbidities. However, investigating the similarity levels for the daily kinetics of cases showed a clustering tendency between different political regions suggesting a significant correlation. The technique would be useful for public health authorities work.
ABSTRACT
The recent Human Coronavirus 2019 (hCoV-19) pandemic has devastated the whole world and impacted all aspects of human life. One of the most comprehensively recorded data for this outbreak is the daily morbidities and mortalities record. The analysis of this dataset would provide insight into the pattern and progression of this disease. The present study focused on the quantitative investigation and descriptive statistical examination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as part of a series of evaluations for this epidemic in the primarily affected geopolitical regions. The year 2021 is worse than 2020 in terms of the recorded daily newly emerging cases and deaths, and there are no signs that there would be an improvement in 2022, as could be estimated from early warning signs, even if there could be an apparent decline in the outbreak waves. India is one of the major countries that have been adversely affected by this global pandemic. The present study addressed this nation as a detailed record of COVID-19 cases and deaths extracted from a chronologically arranged dataset for the newly emerged cases and deaths on a daily basis. Cumulative counts were calculated and logarithmically transformed. Two significant peaks - embracing multiple waves - were observed with tailing for morbidity and mortality, which were highly correlated. There were no signs of a recession in the outbreak census. However, relative calm periods between waves might be detected. There were rising trends in morbidities and mortalities with a clustering tendency upon examination of the run charts. The Morgan-Mercer-Flodin (MMF) model was found to demonstrate the best-fitting non-linear curve for the transformed cumulative database. Derivatization of the model equation demonstrated a factor that could be used in the assessment of the outbreak effect numerically to show influence on the impacted population.