Real COVID-19 incidence rate estimate in Spain

Keywords: COVID-19, nowcasting, epidemiological models

Abstract

Introduction: Epidemiological models have proven to be crucial in supporting the decision-making of health authorities during the COVID-19 pandemic as well as raising awareness among the general public of the different measures adopted by authorities (social distancing, mask usage, vaccination, etc.). Objectives: This work describes the methodology to integrate different data sources to generate a single time series that provides real incidence rates of COVID-19 in Spain. Methodology: This series considers both reported and non-notified cases, that is, those that have not been registered by health authorities. Results: This work also describes how the information generated in this project has been treated and stored, it presents the estimated real incidence data obtained, as well as the organizations and research teams that use it, and the different communication channels that have been used to disseminate it (webpage, sharing results with health authorities, and repository). Conclusion: This work integrates information from multiple data sources for the analysis and prediction of the incidence of COVID-19. Through a multidisciplinary approach, it has been possible to propose a response to the problem of estimating the real incidence of COVID-19 cases.

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References

Instituto de Salud Carlos III. (2022a). Evolución pandemia. https://cnecovid.isciii.es/covid19/#evoluci%C3%B3n-pandemia

Instituto de Salud Carlos III. (2022b). Metodología SiVIRA. sistemas y fuentes de información. https://www.isciii.es/QueHacemos/Servicios/VigilanciaSaludPublicaRENAVE/EnfermedadesTransmisibles/Documents/GRIPE/Protocolos/Metodolog%C3%ADa%20SiVIRA,%20sistemas%20y%20fuentes%20de%20informaci%C3%B3n.%20Temporada%202022-23.pdf

Martínez, A. C., Organero, M. M., Moriña, D., Barroso, D. G., & Singh, D. E. (2023). COVID-19 incidence estimates and forecast by metaprediction for the comunidad de Madrid. Technical report, Universidad Carlos III de Madrid.

Universidad Carlos III de Madrid. (2022a). Epigraph, an agent-based epidemiological simulator http://epigraph.uc3m.es.

Universidad Carlos III de Madrid (2022b). Multi-source and multi-method prediction to support COVID-19 policy decision making (PredCov). http://www.uc3m.es/ss/Satellite/GruposInvestigacion/es/TextoDosColumnas/1371351563042

Universidad Carlos III de Madrid. (2022c). Repositorio de datos de incidencia de la COVID-19 para España y la Comunidad de Madrid. https://github.com/epigraph-forecast/IncidenceDataSpain

World Health Organization. (2022). End-to-end integration of SARS-CoV-2 and influenza sentinel surveillance: revised interim guidance. Geneva. https://www.who.int/publications/i/item/WHO-2019-nCoV-Integrated_sentinel_surveillance-2022

Published
2024-01-06
How to Cite
Cublier Martínez, A., Gómez-Barroso, D., Delgado-Sanz, C., Monge, S., Cascajo, A., Marinescu, M. C., Larrauri, A., Carretero, J., & E. Singh, D. (2024). Real COVID-19 incidence rate estimate in Spain. Revista Española De Comunicación En Salud (RECS), 7-14. https://doi.org/10.20318/recs.2024.7970
Section
Artículos

Funding data

  • European Commission
    Grant numbers Financiado con los recursos REACT-UE del fondo europeo de desarrollo regional y el proyecto BCV-2022-1-0005 de la Red Española de Supercomputación