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oai:figshare.com:article/34048435 2026-10-01T17:33:28Z category_15 category_734 category_931 category_811 category_64 category_132 portal_5 item_type_1 month_year_10_2026

<p>Search term clustering, infection trends, and modeling approach for the 2009 H1N1 and COVID-19 pandemics.</p> Sara Mesquita (17691590) João Loureiro (480403) Cláudio Haupt Vieira (25157779) Lília Perfeito (20553240) Joana Gonçalves-Sá (3173976) Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Cancer Infectious Diseases xlink "> disease integrating digital epidemiology consistently outperforming models combines model evaluation casting increasingly relies apply simple supervised feature curation informed feature selection unsupervised methods unstable performance training periods traditional data results suggest online activity often noisy offer fast method offers inexpensive insights increased stability improve prediction hybrid approach frequently suffered different flu curated subset behavioral noise behavioral insights behavioral insight behavioral confounders adaptable framework 2009 h1n1 19 datasets <p>(a, e) Hierarchical clustering of search terms into three clusters (C1, C2, C3) based on temporal search volume patterns. (b, f) Cluster centroids with variance indicated by shaded areas. (c, g) Reported flu (c) and COVID-19 (g) cases over time, with key pandemic milestones highlighted. Shaded areas indicate the timeframe used for clustering. (d, h) Training and testing periods for now-casting models. For H1N1 (d), post-pandemic flu seasons were used for train and test splits. For COVID-19 (h), a rolling window approach adapted models across pandemic phases.</p> 2026-10-01T17:32:53Z Image Figure 10.1371/journal.pone.0358063.g001 https://figshare.com/articles/figure/_p_Search_term_clustering_infection_trends_and_modeling_approach_for_the_2009_H1N1_and_COVID-19_pandemics_p_/34048435 CC BY 4.0