Statistical model in Estimation and Monitoring of Covid 19

Abstract

Background: In the last two decades, the world has seen several viral outbreaks, notably the H1N1 in 2009 and Nipah virus in 2018. The COVID-19 pandemic, declared by WHO in early 2020, presented unprecedented challenges due to its high contagion and potential for asymptomatic spread. Early data often underestimated the pandemic's extent, emphasizing the need for comprehensive health crisis management. Statistical models have been crucial in understanding and responding to COVID-19, from tracing its origins in Wuhan, China, to its global spread. These models have informed public health strategies, adapting to regional differences in the pandemic's impact. The significance of this study lies in using statistical models to estimate and monitor COVID-19, aiding policymakers and healthcare professionals. It sheds light on the virus's transmission and future trends, guiding targeted actions and preparedness for future pandemics. The research underscores the importance of current data over historical records, considering behavioural changes, new virus strains, and varying testing/reporting methods. Objectives: The objectives of this study are to establish a model for the COVID-19 pandemic and provide effective methods, enabling planners and healthcare providers to take appropriate actions for the management, containment, and control of the pandemic.