Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae

Abstract

Ongoing symptoms might follow acute COVID-19. Using electronic health information, we compared pre‒ and post‒COVID-19 diagnostic codes to identify symptoms that had higher encounter incidence in the post‒COVID-19 period as sequelae. This method can be used for hypothesis generation and ongoing monitoring of sequelae of COVID-19 and future emerging diseases.

Keywords

• COVID-19
• coronavirus disease
• severe acute respiratory syndrome coronavirus 2
• SARS-CoV-2
• coronaviruses
• viruses