000163925 001__ 163925
000163925 005__ 20251113160752.0
000163925 0247_ $$2doi$$a10.1017/dap.2021.25
000163925 0248_ $$2sideral$$a146084
000163925 037__ $$aART-2021-146084
000163925 041__ $$aeng
000163925 100__ $$aStarnini, Michele
000163925 245__ $$aImpact of data accuracy on the evaluation of COVID-19 mitigation policies
000163925 260__ $$c2021
000163925 5060_ $$aAccess copy available to the general public$$fUnrestricted
000163925 5203_ $$aEvaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number$ R(t) $, a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of$ R(t) $could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.
000163925 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E36-20R$$9info:eu-repo/grantAgreement/EC/H2020/101003688/EU/Epidemic intelligence to minimize 2019-nCoV’s public health, economic and social impact in Europe/EpiPose$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101003688-EpiPose$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/FIS2017-87519-P
000163925 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000163925 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000163925 700__ $$0(orcid)0000-0002-1192-8707$$aAleta, Alberto
000163925 700__ $$aTizzoni, Michele
000163925 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000163925 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000163925 773__ $$g3 (2021), e28 [10 pp.]$$tData & policy
000163925 8564_ $$s523805$$uhttps://zaguan.unizar.es/record/163925/files/texto_completo.pdf$$yVersión publicada
000163925 8564_ $$s1894585$$uhttps://zaguan.unizar.es/record/163925/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000163925 909CO $$ooai:zaguan.unizar.es:163925$$particulos$$pdriver
000163925 951__ $$a2025-11-13-14:57:58
000163925 980__ $$aARTICLE