2026-10-12T17:01:19Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34048408 2026-10-01T17:33:17Z category_15 category_734 category_931 category_811 category_64 category_132 portal_5 item_type_6 month_year_10_2026

<p>Bias–Variance trade-off for COVID-19.</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 <div><p>Disease now-casting increasingly relies on non-traditional data, such as online activity, which offer fast, inexpensive insights but are often noisy, unstructured, and influenced by behavioral confounders. Models using search engine data have frequently suffered from overfitting and unstable performance. However, pandemics may help distinguish true disease signals from behavioral noise, as both tend to surge during outbreaks. We propose a hybrid approach to improve prediction that combines model evaluation with behavioral insight. We apply simple supervised and unsupervised methods to different flu and COVID-19 datasets, using the 2009 H1N1 and 2020 COVID-19 pandemics as training periods. In both cases, selecting a curated subset of search terms improved model performance, reduced overfitting, and increased stability, consistently outperforming models that used search terms indiscriminately. These results suggest that feature curation informed by behavioral insights can enhance now-casting beyond purely statistical approaches. Our method offers a scalable and adaptable framework for integrating digital epidemiology into public health surveillance.</p></div> 2026-10-01T17:32:53Z Text Journal contribution 10.1371/journal.pone.0358063.s020 https://figshare.com/articles/journal_contribution/_p_Bias_Variance_trade-off_for_COVID-19_p_/34048408 CC BY 4.0