Household Severe Acute Respiratory Syndrome Coronavirus 2 Transmission and Children: A Network Prospective Study.

BACKGROUND: The role of children in household transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains unclear. We describe the epidemiological and clinical characteristics of children with coronavirus disease 2019 (COVID-19) in Catalonia, Spain, and investigate the household transmission dynamics. METHODS: A prospective, observational, multicenter study was performed during summer and school periods (1 July 2020-31 October 2020) to analyze epidemiological and clinical features and viral household transmission dynamics in COVID-19 patients aged <16 years. A pediatric index case was established when a child was the first individual infected. Secondary cases were defined when another household member tested positive for SARS-CoV-2 before the child. The secondary attack rate (SAR) was calculated, and logistic regression was used to assess associations between transmission risk factors and SARS-CoV-2 infection. RESULTS: The study included 1040 COVID-19 patients. Almost half (47.2%) were asymptomatic, 10.8% had comorbidities, and 2.6% required hospitalization. No deaths were reported. Viral transmission was common among household members (62.3%). More than 70% (756/1040) of pediatric cases were secondary to an adult, whereas 7.7% (80/1040) were index cases. The SAR was significantly lower in households with COVID-19 pediatric index cases during the school period relative to summer (P = .02) and compared to adults (P = .006). No individual or environmental risk factors associated with the SAR. CONCLUSIONS: Children are unlikely to cause household COVID-19 clusters or be major drivers of the pandemic, even if attending school. Interventions aimed at children are expected to have a small impact on reducing SARS-CoV-2 transmission.

Symptom-Based Predictive Model of COVID-19 Disease in Children.

BACKGROUND: Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms. METHODS: Epidemiological and clinical data were obtained from the REDCap® registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 November 2020 and 31 March 2021, 784 were positive (17.68%). We pre-processed the data to be suitable for a machine learning (ML) algorithm, balancing the positive-negative rate and preparing subsets of data by age. We trained several models and chose those with the best performance for each subset. RESULTS: The use of ML demonstrated an AUROC of 0.65 to predict a COVID-19 diagnosis in children. The absence of high-grade fever was the major predictor of COVID-19 in younger children, whereas loss of taste or smell was the most determinant symptom in older children. CONCLUSIONS: Although the accuracy of the models was lower than expected, they can be used to provide a diagnosis when epidemiological data on the risk of exposure to COVID-19 is unknown.