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The number of hospitalization of COVID-19 patients with one or more comorbid diseases is highly alarming. Despite the lack of large clinical data and incomplete understanding of virus pathology, identification of the COVID-19 associated diseases with clinical precision are highly limited. In this regard, our text mining of 6238 PubMed abstracts (as on 23 April 2020) successfully identified broad spectrum of COVID-19 comorbid diseases/disorders (54), and their prevalence on the basis of the number of occurrence of disease terms in the abstracts. The disease ontology based semantic similarity network analysis revealed the six highly comorbid diseases of COVID-19 namely Viral Pneumonia, Pulmonary Fibrosis, Pulmonary Edema, Acute Respiratory Distress Syndrome (ARDS), Chronic Obstructive Pulmonary Disease (COPD) and Asthma. The disease gene bipartite network revealed 15 genes that were strongly associated with several viral pathways including the corona viruses may involve in the manifestation (mild to critical) of COVID-19. Our tripartite network- based repurposing of the approved drugs in the world market revealed six promising drugs namely resveratrol, dexamethasone, acetyl cysteine, Tretinoin, simvastatin and aspirin to treat comorbid symptoms of COVID-19 patients. Our animal studies in rats and literatures strongly supported that resveratrol is the most promising drug to possibly reduce several comorbid symptoms associated with COVID-19 including the severe hypoxemia induced vascular leakage. Overall, the anti-viral properties of resveratrol against corona virus could be readily exploited to effectively control the viral load at early stage of COVID-19 infection through nasal administration.