This Is AuburnElectronic Theses and Dissertations

Understanding Long Covid-19 Patterns in Pediatric Patients using Network Analytics




Hogu, Ornela

Type of Degree

Master's Thesis


Computer Science and Software Engineering


COVID-19 has had a long-term impact on the quality of life, work, and society by manifesting in the form of Long COVID. Long COVID is relatively less understood condition due to its recency. The challenge is even greater among pediatric population, which represents 19% of overall Long COVID cases, due to lack of research and ample data. We study three main research questions in the study. First, what are the most frequently occurring chronic conditions among pediatric patients suffering from Long COVID at different time periods? Second, what are the most frequent non-chronic conditions among pediatric patients suffering from long-Covid across different age segments? Third, what are various clusters of chronic and non-chronic conditions that exist among pediatric patients diagnosed with Long-COVID. Using N3C (National COVID Cohort Collaboration) data, we analyze health records of ~500K pediatric patients suffering from long COVID across 72 different sites. We apply network analytics approaches to model various chronic and non-chronic conditions that pre-exists in patients diagnosed with Long COVID. In the first part, we model two network types to capture the chronic and non-chronic diseases in pre and long Covid. In the second part, created bipartite graphs and its projections to generate network clusters of pre-existing diseases and coexisting disease network for Long COVID. We then applied two community detection algorithms, Louvain and Leiden algorithms, on these projections to identify clustering patterns of diseases. We analyzed and interpreted top clusters and observed a high dominance on the conditions related to the pregnancy, neoplasm(cancer), infectious, parasitic diseases and other categories. To develop insights into co-existing non-chronic conditions, we segmented the data across three pediatric age groups (0-4, 5-11, 12-17 years). Our findings suggest that Long COVID co-exists with four highly frequent chronic conditions, namely, asthma, anxiety, obesity, and lipoprotein metabolism disorders. For all pediatric patients suffering from Long COVID, we found five dominant non-chronic co-existing conditions: acute upper respiratory infections, fever, Acute pharyngitis, deficit hyperactivity disorders, and cough. However, we observed some unique conditions when segmented across different age groups. For example, sleep disorders and severe stress were dominant across 11-17 age group. Using Louvain Community detection algorithm, we identified five key clusters. For example, cluster one (approx. 14% of data) had higher levels of teen pregnancies, infectious or parasitic diseases, and relatively lower levels of mental or behavioral disorders while cluster two (approx. 5.5%) had higher instances of neoplasms, and infectious or parasitic diseases. Our findings have important implications for pediatric care providers and researchers. Using network analytic approaches, we identified various clusters of chronic and non-chronic conditions that exist with Long COVID diagnosis among pediatric population. Such an understanding could provide early insights into the nature of pediatric patients who are likely to develop Long COVID from COVID-19.