Background: Following the declaration of COVID-19 infection as a pandemic by the World Health Organisation (WHO), researchers have been working to identify diagnostic and prognostic markers while considering the accuracy and cost-effectiveness of the selected method. This study aimed to identify common COVID-19 infection clinical severity and outcome predictors in a resource-constrained setting. Method: This analytical cross-sectional study involved 91 COVID-19 infection patients diagnosed with real-time polymerase chain reaction (RT‒PCR) for SARS-CoV-2. The prognostic usefulness of several COVID-19 infection predictors was assessed using multiple logistic regression analysis and the receiver operating characteristic (ROC) curve. A P value< 0.05 is considered significant. Results: The mean age of the participants was 65.37±12.13 years, with males being predominant 51(56%). Furthermore, age, body mass index, haemoglobin, total white blood cell count, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio, serum creatinine, D-dimer, C-reactive protein, random blood sugar, spontaneous oxygen saturation and respiratory rate were found to be strongly correlated with the severity and mortality of COVID-19 infection (P values< 0.05). Among the different biomarkers studied, the most reliable predictors of COVID-19 infection severity and mortality were the neutrophil-lymphocyte ratio (for severity; aOR = 8.060, AUC = 0.81 and for mortality; aOR = 4.139, AUC = 0.85) and D-dimer level (for severity; aOR = 4.695, AUC = 0.77, and for mortality; aOR = 3.508, AUC = 0.82). Conclusions: This study identified several independent, inexpensive, simple, and important biomarkers of COVID-19 infection that can be used for patient stratification and resource allocation, especially in resource-limited settings.