{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T06:04:22Z","timestamp":1780639462665,"version":"3.54.1"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Viral infectious diseases are highly prevalent in pediatric populations, and accurate prediction of viral load is critical for clinical intervention and public health management. Existing time-series models poorly handle long-range dependencies, multi-scale features, noise, and incomplete clinical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose DTR-Former, a Dynamic Temporal Partitioning-enhanced Transformer. It uses wavelet packet decomposition for adaptive temporal partitioning and multi-scale feature extraction, adopts sparse self-attention to reduce redundancy and enhance long-sequence modeling, and employs a residual convolutional decoder with a gating mechanism to refine features and suppress residual noise.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      On dbEBV dataset, DTR-Former achieves MSE = 0.16, MAE = 0.27,\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.88; on NCBI dataset, MSE = 0.18, MAE = 0.30,\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.86. It shows strong multi-step stability and robustness under missing-data conditions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DTR-Former outperforms state-of-the-art methods in accuracy, stability, and efficiency, offering an effective solution for pediatric viral load forecasting and related time-series tasks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1829861","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:37:26Z","timestamp":1780637846000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic temporal partitioning enhanced transformer for pediatric viral load forecasting"],"prefix":"10.3389","volume":"14","author":[{"given":"Sai","family":"Li","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengqiu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sitian","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeshu","family":"Ning","sequence":"additional","affiliation":[{"name":"Department of Neurology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junkai","family":"Ren","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaozhou","family":"He","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for 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