RT journal article
T1 Detecting statistical anomalies in COVID-19 surveillance database
A1 Conde, Inma
A1 Silva Ramírez, Esther Lydia
A1 Gómez Tovar, M. Belén
A1 Ortiz, Angel L.
A1 Cumbrera, F.L.
A2 Ingeniería Informática
K1 Benford’s law
K1 Anomalies
K1 Data reliability
K1 COVID-19
K1 metric distances
AB The unexpected unfolding of the COVID-19 pandemic involved the rapid gathering, integration, processing, and dissemination of large volumes of epidemiological data worldwide. In this context, heterogeneous reporting practices and data-integration issues may introduce certain statistical anomalies that can affect the scientific reliability of downstream analyses. Because global surveillance systems continuously aggregate multi-country time series, anomaly screening also becomes a computational problem requiring automated, scalable, and potentially near-real-time analytical workflows, which is relevant to large-scale data processing and high-performance computing environments. The purpose of the present study is to perform a quantitative analysis of COVID-19 surveillance data using Newcomb–Benford’s law and calibrated supervised datasets. As a benchmark, daily new deaths from ten countries, two from each continent, were studied, with a total of 738 records per country. The proposed framework moves beyond a binary conforming/non-conforming diagnosis by estimating the magnitude of deviation through calibrated descriptors and principal-component-based quantification. The comparison between High Income and Low Income aggregates yielded anomaly estimates of 15.27% and 20.55%, respectively, with an uncertaintyof 5.2%. Overall, the results indicate that several datasets exhibit non-negligible statistical deviations from the expected digit distribution, interpreted simply as anomaly signals that may support large-scale epidemiological data validation.
PB Springer Nature
SN 1573-0484
YR 2026
FD 2026-06-12
LK http://hdl.handle.net/10498/39765
UL http://hdl.handle.net/10498/39765
LA eng
NO Disponible en acceso abierto temporalmente en: https://rdcu.be/fn4Mo
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026