| dc.contributor.author | Ochola, C. G. | |
| dc.contributor.author | Agnes, K. | |
| dc.contributor.author | Ivan, B. | |
| dc.date.accessioned | 2025-12-18T07:59:36Z | |
| dc.date.available | 2025-12-18T07:59:36Z | |
| dc.date.issued | 2025 | |
| dc.identifier.uri | https://aphrc.org/document/inspire-2-0-launch-report/ | |
| dc.identifier.uri | http://knowhub.aphrc.org/handle/123456789/2855 | |
| dc.description.abstract | The INSPIRE 2.0 Launch Report details the transition and expansion of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), a collaborative initiative led by the African Population and Health Research Center (APHRC). Originally conceptualized in 2019, INSPIRE 2.0 aims to modernize Health and Demographic Surveillance Systems (HDSS) across Africa by implementing standardized methodologies for data collection, harmonization, and visualization. The report highlights the integration of Artificial Intelligence (AI), machine learning, and the Observational Medical Outcomes Partnership (OMOP) Common Data Model to enhance data interoperability. It serves as a strategic roadmap for establishing a sustainable, pan-African data infrastructure that empowers researchers and policymakers with high-quality longitudinal insights to address significant global health challenges and inform evidence-based decision-making. | |
| dc.publisher | APHRC | |
| dc.subject | Data Science and Management | |
| dc.subject | Longitudinal Population Studies | |
| dc.subject | Health and Demographic Surveillance Systems (HDSS) | |
| dc.subject | Data Harmonization and Standardization | |
| dc.subject | Artificial Intelligence in Public Health | |
| dc.subject | Research Capacity Strengthening | |
| dc.subject | FAIR Data Principles | |
| dc.subject | African Health Policy | |
| dc.subject | Big Data Analytics | |
| dc.subject | Interoperability and Common Data Models (OMOP) | |
| dc.title | Inspire 2.0. Launch Report | |
