Qiong Wu

Contributions to Public Health

• Data integration methodologies and applications: My methodological research focuses on multi-source data integration, including federated learning and transfer learning, within large, distributed research networks. Focusing on rare outcomes in pharmacoepidemiological studies, I developed federated learning methods for evidence synthesis when addressing privacy constraints in sharing patient data (a). To enhance learning in specific target datasets, I developed transfer learning approaches that leverage external source samples while accounting for both within-sample and between-sample heterogeneity (b).
  • Wu Q, Schuemie MJ, Suchard MA, Ryan P, Hripcsak GM, Rohde CA, Chen Y. Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes. J Biomed Inform. 2023 Sep;145:104476. doi: 10.1016/j.jbi.2023.104476. Epub 2023 Aug 19. PubMed PMID: 37598737; PubMed Central PMCID: PMC11056245.
  • Wu Q, Wang C, Chen Y. Heterogeneous latent transfer learning in Gaussian graphical models. Biometrics. 2024 Jul 1;80(3):ujae096. doi: 10.1093/biomtc/ujae096. PMID: 39302138; PMCID: PMC11413907.
• Clinical evidence generation in COVID-19 and pediatric studies: My research focuses on generating clinical evidence for COVID-19 and Long COVID in the understudied pediatric population. I led a study using electronic health records to evaluate the effectiveness and durability of BNT162b2 against SARS-CoV-2 infection and severe disease, addressing bias from under-reported vaccine data (a). To investigate vaccine protection against Long COVID, we conducted a causal mediation analysis to separate vaccine’s direct and indirect effects (b).
  • Wu Q, Zhang B, Tong J, Bailey LC, Bunnell HT, Chen J, Chrischilles EA, Christakis DA, Downs SM, Hirabayashi K, Mishkin AD, Mosa ASM, Pajor NM, Rao S, Razzaghi H, Schwenk HT, Sills MR, Wang H, Wang L, Wang Y, Zhang D, Zhou T, Jhaveri R, Tchetgen Tchetgen EJ, Morris JS, Forrest CB, Chen Y; RECOVER Consortium. Real-world effectiveness and causal mediation study of BNT162b2 on long COVID risks in children and adolescents. EClinicalMedicine. 2024 Dec 6;79:102962. doi: 10.1016/j.eclinm.2024.102962. PMID: 39720603; PMCID: PMC11667630.
  • Wu Q, Tong J, Zhang B, Zhang D, Chen J, Lei Y, Lu Y, Wang Y, Li L, Shen Y, Xu J, Bailey LC, Bian J, Christakis DA, Fitzgerald ML, Hirabayashi K, Jhaveri R, Khaitan A, Lyu T, Rao S, Razzaghi H, Schwenk HT, Wang F, Gage Witvliet MI, Tchetgen Tchetgen EJ, Morris JS, Forrest CB, Chen Y. Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents. Ann Intern Med. 2024 Feb;177(2):165-176. doi: 10.7326/M23-1754. Epub 2024 Jan 9. PMID: 38190711.
• Statistical modeling and inference for high-dimensional data with complex structures: My methodological work develops statistical modeling and inference frameworks for high-dimensional data by integrating graph theory into large-scale inference. Motivated by challenges in analyzing biomedical data such as neuroimaging data, addressing the issue of significant false-positive noise that obscures true signals, and enhancing research reproducibility and replicability, we developed statistical inference procedures that leverage the latent structures inherent in biomedical entities (a, b).
  • Wu Q, Huang X, Culbreth AJ, Waltz JA, Hong LE, Chen S. Extracting brain disease-related connectome subgraphs by adaptive dense subgraph discovery. Biometrics. 2022 Dec;78(4):1566-1578. doi: 10.1111/biom.13537. Epub 2021 Aug 22. PMID: 34374075; PMCID: PMC10396394.
  • Wu Q, Zhang Y, Huang X, Ma T, Hong LE, Kochunov P, Chen S. A multivariate to multivariate approach for voxel-wise genome-wide association analysis. Stat Med. 2024 Sep 10;43(20):3862-3880. doi: 10.1002/sim.10101. Epub 2024 Jun 24. PMID: 38922949.