Pediatric COVID-19 Clinical Characteristics and Outcomes

The clinical presentation of COVID-19 in children spans a wide spectrum, from asymptomatic infection through mild upper respiratory symptoms to severe multisystem inflammatory sequelae. Most paediatric cases exhibit mild disease, with fever, cough or gastrointestinal disturbance predominating and a low rate of hospitalisation. Younger age (<5 years), underlying medical or developmental conditions and social determinants such as ethnicity and deprivation have been associated with higher admission rates. A small proportion of children progress to require critical care, often in the context of paediatric multisystem inflammatory syndrome, cardiac involvement or respiratory failure. Longitudinal follow-up indicates that residual symptoms and laboratory abnormalities may persist beyond acute infection, underscoring the need for monitoring of convalescent children. Recent large-scale data have enabled the identification of risk factors for severe outcomes and informed stratified public health measures, vaccination strategies and therapeutic guidelines. Attention has also turned to the impact of emerging viral variants on disease severity and transmission dynamics among younger populations.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Pediatric COVID-19 Clinical Characteristics and Outcomes publication trend

The graph below shows the total number of articles in pediatric covid-19 clinical characteristics and outcomes across all publications each year (not limited to Nature Index journals).

Technical terms

Paediatric multisystem inflammatory syndrome (MIS-C): A hyperinflammatory condition following SARS-CoV-2 infection, marked by fever and dysfunction of multiple organ systems.

Phenotype: A set of observable clinical characteristics used to classify patients into subgroups sharing similar symptom patterns and prognoses.

Paediatric complex chronic condition: An underlying health condition in a child expected to last at least 12 months and requiring specialised care or affecting multiple organ systems.

Unsupervised machine learning: An analytical approach that identifies intrinsic patterns within data without predefined labels, often used to uncover novel patient subgroups.

References

1. Hospital admissions linked to SARS-CoV-2 infection in children and adolescents: cohort study of 3.2 million first ascertained infections in England. The BMJ (2023).
2. Six clinical phenotypes with prognostic implications were identified by unsupervised machine learning in children and adolescents with SARS-CoV-2 infection: results from a German nationwide registry. Respiratory Research (2024).
3. Characteristics, Outcomes, and Severity Risk Factors Associated With SARS-CoV-2 Infection Among Children in the US National COVID Cohort Collaborative. JAMA Network Open (2022).