Long COVID, also referred to as post-acute sequelae of COVID-19 (PASC), is a substantial global health concern estimated to have affected over 145 million individuals worldwide. Characterized by persistent and new symptoms extending beyond four weeks from the initial infection–such as fatigue, breathlessness, and cognitive impairments–Long COVID poses significant challenges to healthcare systems due to its chronic nature and diverse clinical manifestations. An urgent need exists to elucidate its underlying mechanisms to facilitate the development of effective diagnostic tools and targeted treatments. This paper provides a comprehensive overview of the current datasets and computational methods to investigate the causes, risk factors, and potential treatments for Long COVID. To better understand the molecular processes driving this condition, we examine various omics data sources, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics datasets. Moreover, we discuss how integrating multi-omics data and using advanced computational techniques–such as machine learning and network analysis–can enhance Long COVID diagnosis, prognosis, and therapeutic strategies. We also emphasize the importance of larger, more diverse cohorts, longitudinal research designs, and cross-cohort validations to strengthen the reliability and applicability of findings. By addressing these crucial aspects, this review aims to advance the development of effective interventions for Long COVID, ultimately improving patient outcomes and alleviating the disease burden.

The authors have declared no competing interest.

This study did not receive any funding.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes