Abstract
Respiratory syncytial viral (RSV) infection is a leading persisting pulmonary disease-causing agent. It causes loss of lives especially among infants, old ages, and adults immunocompromised individuals. This viral pathogen infects children more especially those under the age of 2 and may lead to death. It causes 3 million hospitalizations and up to 60,000 deaths annually for under the age of 5. The most vulnerable are immunocompromised individuals and asthmatic children with suboptimal antiviral defenses. It is associated with bronchiolitis, pneumonia, and bronchopneumonia. Despite all the current interventions and clinical trials, the only available therapeutic strategies for this viral infection are palliative care. Therefore, it is imperative to understand the pathogenicity of RSV and the corresponding host immune response to depict a sort of a targeted intervention. With the increasingly cutting-edge methods in harnessing the pathogenicity of this viral infection, high throughput systems including omics technological advances are at the spotlight. For instance, the associated genes with RSV complications for the host, the set of microbiome identified as operational taxonomic unit, the upregulated or downregulated metabolites, the protein subtypes, and the small molecules can help explain the viral microenvironment. Moreover, these big data will lead to RSV patients' stratification through individualized patient profiles that will bring in targeted prevention and treatment algorithms tailored to individualized patients' profiles. Through this, the virus and host interactions based on the pathogenicity of infection will provide a strong ground for depicting the prevention, prediction, and personalized medicine (3PM) for RSV. The 3PM approach brought cutting edge functional medicine to the healthcare givers, thus conferring targeted prevention and precision medicine while observing personalized treatment as well as preventive regularities. The viral replication mechanisms against the host defense mechanisms are crucial for the development of safe and effective therapy. Integrative personal omics profiles, whose analysis is based on the combined proteomics, transcriptomics, genomics, proteoformics, metabolomics, and autoantibody profiles, are very robust for predicting the risk of RSV infection. The targeted prevention will emerge from the patient stratification when the diagnosis is accurately predicted. In addition, the personalized medical services will give an effective prognostic assessment for RSV complications.