Predictive Modelling of COVID-19 Diagnosis: Logistic Regression Analysis of SARI and RdRp Testing Parameters

The logistic regression analysis in the present study explores the relationship between COVID-19 test outcomes and some of the principal clinical indicators. The main point was made upon SARI and RdRp confirmatory testing. It also considered patients across five discrete categories, age-stratified according to WHO criteria, into four groups. The methodology used a two-stage analytical approach: first, Chi-Square tests to establish associations between sociodemographic variables and the COVID-19 test results, followed by logistic regression to develop predictive models. Statistical analysis revealed distinct patterns in both SARI and RdRp patient groups. In SARI patients, gender was not significantly related to COVID-19 test results. However, RdRp analysis revealed high correlations with age, and some demographic factors are statistically insignificant. The obtained results were a basis for creating the equations of the predictive models through logistic regression. This work is within the research scope on COVID-19 testing dynamics and characteristics of patients. This study identifies key indicators of a positive test result, helping doctors assess risk and manage patients. This research may aid clinical decision-making and resource allocation in COVID-19 testing facilities with predictive models.