Evaluating COVID-19 risk under the estimation of population mean using two attributes

Area of Science:

• Epidemiology
• Public Health
• Health Economics

Background:

• The COVID-19 pandemic has had profound global physical, mental, and economic impacts.
• National capacities to cope with COVID-19 vary significantly due to differences in development, resources, and immunization levels.
• Understanding socioeconomic vulnerability and coping capacity within diverse health systems is critical for pandemic response.

Purpose of the Study:

• To evaluate and estimate COVID-19 risk and the lack of coping capacity in 190 countries.
• To develop a novel statistical approach for assessing global pandemic vulnerability.
• To provide insights into disparities in national pandemic resilience.

Main Methods:

• Development of an exponential estimator utilizing two auxiliary attributes.
• Theoretical derivation and comparison of mean square error expressions with existing estimators.
• Validation of the proposed method using real-world INFORM COVID-19 risk data.

Main Results:

• The study provides quantitative estimates of COVID-19 risk and coping capacity across a large number of countries.
• The proposed exponential estimator demonstrates theoretical advantages over existing methods.
• Empirical findings underscore significant global variations in vulnerability and resilience to the pandemic.

Conclusions:

• The developed statistical framework offers a robust method for assessing national pandemic risk and coping capacity.
• Findings emphasize the need for tailored global health strategies that account for socioeconomic disparities.
• This research contributes to a better understanding of factors influencing a country's ability to manage public health crises.