MODEL FOR COVID-19 PANDEMIC DISEASE

ABSTRACT

This research endeavours to develop a COVID-19 Skepticism Logistic Regression model (CSLRM) aimed at mitigating the spread of COVID-19 pandemic. The primary objective of this research is to assess comprehensively the multifaceted influences on the spread of COVID-19, particularly in the context of socio-economic factors, skepticism, and awareness levels, utilizing data mining techniques. The study addresses the need to understand the extent to which these factors contribute to the transmission of COVID-19. To achieve this objective, a logistic regression data mining algorithm was adopted. Logistic regression enables the analysis of the probability of COVID-19 spread as a binary outcome (presence or absence of cases) based on various predictor variables, including socio-economic factors, skepticism, and awareness levels. Data for this study were primarily sourced from an electronic questionnaire administered with the assistance of medical personnel to patients visiting teaching hospitals in the South-South region of Nigeria with a sample size of 480 patients. The findings of the logistic regression model suggest that skepticism plays a significant role in influencing the spread of COVID-19 in Nigeria. Specifically, the research indicates that higher levels of skepticism correlate with an increased number of reported COVID-19 cases. The development of predictive models, such as the CSLRM, can play a vital role in combating skepticism and facilitating more effective public health interventions in Nigeria.

Keywords: Pandemic, Skepticism, logistic regression, COVID-19, Data mining techniques