Structural Zero Data of COVID-19 Discovers Exodus Probabilities

Area of Science:

• Statistics
• Epidemiology
• Social Sciences

Background:

• COVID-19 pandemic management presents challenges for healthcare professionals and governing bodies.
• Surveys on public perception, knowledge, and communication regarding COVID-19 are crucial.
• Structural zero categories, arising from mutually exclusive survey questions, pose analytical challenges.

Purpose of the Study:

• To introduce a novel probability-based method for analyzing structural zero data.
• To determine the proportion of data within mutually exclusive categories.
• To address the unaddressed issue of data migration from eliminated categories in surveys.

Main Methods:

• Data collected from a survey among Malaysians on COVID-19 perceptions, knowledge, and communication.
• Development of a new probability method to quantify proportions in structural zero categories.
• Application of the method to analyze data with mutually exclusive subquestions.

Main Results:

• The study identified and named a new probability concept: "Exodus probabilities."
• Demonstrated the utility of Exodus probabilities with an application in COVID-19 data.
• The methodology effectively addresses structural zeros caused by mutually exclusive survey designs.

Conclusions:

• The discovery of Exodus probabilities offers an innovative approach to handling structural zero data.
• This method provides a way to understand how data proportions shift when categories are eliminated.
• The Exodus probabilities methodology has broad applicability beyond COVID-19 research, including engineering and marketing.