Abstract
The dynamics of viral loads among COVID-19 patients in Changzhou, China were evaluated using dynamic random effects models. The models were estimated by maximum likelihood methods allowing for between and within patient variations. Statistical criteria were developed for focusing on viral RNAs for clinical decision making. The empirical results showed that inflammation among patients were significant predictors of cycle threshold values for ORF1ab and N RNAs. Moreover, within subject variations were higher in Ct values of ORF1ab RNA indicating that assessment of N RNA may be adequate in resource-poor settings. The inter-relationships between ORF1ab and N RNAs were investigated and the need for developing comprehensive models for viral load dynamics is emphasized.