Data integration and synthesis for pandemic and epidemic intelligence
Abstract The COVID-19 pandemic highlighted substantial obstacles in real-time data generation and management needed for clinical research and epidemiological analysis. Three years after the pandemic, reflection on the difficulties of data integration offers potential to improve emergency preparednes...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | BMC Proceedings |
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| Online Access: | https://doi.org/10.1186/s12919-025-00321-9 |
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| author | Barbara Tornimbene Zoila Beatriz Leiva Rioja Manoel Barral-Netto Carlos Castillo-Salgado Irena Djordjevic Moritz Kraemer Martina McMenamin Oliver Morgan |
| author_facet | Barbara Tornimbene Zoila Beatriz Leiva Rioja Manoel Barral-Netto Carlos Castillo-Salgado Irena Djordjevic Moritz Kraemer Martina McMenamin Oliver Morgan |
| author_sort | Barbara Tornimbene |
| collection | DOAJ |
| description | Abstract The COVID-19 pandemic highlighted substantial obstacles in real-time data generation and management needed for clinical research and epidemiological analysis. Three years after the pandemic, reflection on the difficulties of data integration offers potential to improve emergency preparedness. The fourth session of the WHO Pandemic and Epidemic Intelligence Forum sought to report the experiences of key global institutions in data integration and synthesis, with the aim of identifying solutions for effective integration. Data integration, defined as the combination of heterogeneous sources into a cohesive system, allows for combining epidemiological data with contextual elements such as socioeconomic determinants to create a more complete picture of disease patterns. The approach is critical for predicting outbreaks, determining disease burden, and evaluating interventions. The use of contextual information improves real-time intelligence and risk assessments, allowing for faster outbreak responses. This report captures the growing acknowledgment of data integration importance in boosting public health intelligence and readiness and show examples of how global institutions are strengthening initiatives to respond to this need. However, obstacles persist, including interoperability, data standardization, and ethical considerations. The success of future data integration efforts will be determined by the development of a common technical and legal framework, the promotion of global collaboration, and the protection of sensitive data. Ultimately, effective data integration can potentially transform public health intelligence and our way to successfully respond to future pandemics. |
| format | Article |
| id | doaj-art-fd9958927bde4293932287ef3c2aba21 |
| institution | OA Journals |
| issn | 1753-6561 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Proceedings |
| spelling | doaj-art-fd9958927bde4293932287ef3c2aba212025-08-20T02:30:20ZengBMCBMC Proceedings1753-65612025-04-0119S41810.1186/s12919-025-00321-9Data integration and synthesis for pandemic and epidemic intelligenceBarbara Tornimbene0Zoila Beatriz Leiva Rioja1Manoel Barral-Netto2Carlos Castillo-Salgado3Irena Djordjevic4Moritz Kraemer5Martina McMenamin6Oliver Morgan7World Health Organization Hub for Pandemic and Epidemic IntelligenceCPC AnalyticsOswaldo Cruz Foundation (FIOCRUZ)Johns Hopkins University (JHU)World Health Organization Head QuarterUniversity of OxfordWorld Health Organization Head QuarterWorld Health Organization Hub for Pandemic and Epidemic IntelligenceAbstract The COVID-19 pandemic highlighted substantial obstacles in real-time data generation and management needed for clinical research and epidemiological analysis. Three years after the pandemic, reflection on the difficulties of data integration offers potential to improve emergency preparedness. The fourth session of the WHO Pandemic and Epidemic Intelligence Forum sought to report the experiences of key global institutions in data integration and synthesis, with the aim of identifying solutions for effective integration. Data integration, defined as the combination of heterogeneous sources into a cohesive system, allows for combining epidemiological data with contextual elements such as socioeconomic determinants to create a more complete picture of disease patterns. The approach is critical for predicting outbreaks, determining disease burden, and evaluating interventions. The use of contextual information improves real-time intelligence and risk assessments, allowing for faster outbreak responses. This report captures the growing acknowledgment of data integration importance in boosting public health intelligence and readiness and show examples of how global institutions are strengthening initiatives to respond to this need. However, obstacles persist, including interoperability, data standardization, and ethical considerations. The success of future data integration efforts will be determined by the development of a common technical and legal framework, the promotion of global collaboration, and the protection of sensitive data. Ultimately, effective data integration can potentially transform public health intelligence and our way to successfully respond to future pandemics.https://doi.org/10.1186/s12919-025-00321-9Data integrationPandemic intelligenceEpidemic preparednessPublic health surveillanceData interoperabilityHealth informatics |
| spellingShingle | Barbara Tornimbene Zoila Beatriz Leiva Rioja Manoel Barral-Netto Carlos Castillo-Salgado Irena Djordjevic Moritz Kraemer Martina McMenamin Oliver Morgan Data integration and synthesis for pandemic and epidemic intelligence BMC Proceedings Data integration Pandemic intelligence Epidemic preparedness Public health surveillance Data interoperability Health informatics |
| title | Data integration and synthesis for pandemic and epidemic intelligence |
| title_full | Data integration and synthesis for pandemic and epidemic intelligence |
| title_fullStr | Data integration and synthesis for pandemic and epidemic intelligence |
| title_full_unstemmed | Data integration and synthesis for pandemic and epidemic intelligence |
| title_short | Data integration and synthesis for pandemic and epidemic intelligence |
| title_sort | data integration and synthesis for pandemic and epidemic intelligence |
| topic | Data integration Pandemic intelligence Epidemic preparedness Public health surveillance Data interoperability Health informatics |
| url | https://doi.org/10.1186/s12919-025-00321-9 |
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