The governance of forecasting content covers epidemiological models, surveillance data integration, intervention scenarios, forecasting assumptions, calibration procedures, model updates, and dissemination of forecasting outputs. The designated public health authority or coordinating institution within each implementing Member State (MS) should oversee validation of epidemiological assumptions, approval of forecasting workflows, review of intervention scenarios, interpretation of forecasting outputs, and communication of projections to decision-makers and the public. Forecasting models should be continuously updated to reflect evolving transmission dynamics, emergence of variants of concern (VOCs), changes in vaccination coverage, healthcare system adaptations, behavioural changes, and updated scientific evidence. Model recalibration procedures should be formally documented and periodically reviewed by multidisciplinary expert groups involving epidemiologists, mathematical modellers, statisticians, public health authorities, healthcare professionals, and data scientists. Version control systems and audit trails should be implemented to ensure transparency regarding model assumptions, parameter changes, calibration procedures, forecasting outputs, and software modifications. Forecasting outputs intended for operational decision-making should undergo scientific and technical validation before dissemination.
Data governance covers data acquisition, data validation, data harmonization, anonymization, secure storage, access management, interoperability, and data-sharing agreements. Each implementing MS should designate responsible authorities for surveillance data management, healthcare data integration, genomic surveillance coordination, operational reporting, and compliance with General Data Protection Regulation (GDPR) and national regulations. Data governance frameworks should define data ownership, permitted data uses, access permissions, retention policies, audit procedures, and responsibilities for data quality assurance. Secure and standardized data pipelines should be maintained to ensure reliable ingestion of incidence data, hospitalization data, intensive care unit (ICU) admissions, mortality data, vaccination coverage, genomic surveillance data, healthcare capacity indicators, and sociodemographic data.
The technical infrastructure supporting the Forecasting Tool should be managed by professional technical teams or trusted hosting providers operating on behalf of the responsible public health authority. The infrastructure includes forecasting servers, cloud or hybrid computing environments, databases, Application Programming Interfaces (APIs), dashboards, visualization systems, backup systems, and monitoring platforms. Operational responsibilities include software maintenance, server monitoring, cybersecurity updates, backup management, disaster recovery, performance optimization, and infrastructure scalability. Infrastructure governance should ensure operational continuity, high availability, secure authentication, access control, redundancy, and resilience against cyber threats. Where forecasting infrastructures are shared across multiple MS, governance agreements should clearly define hosting responsibilities, maintenance obligations, incident management procedures, and operational escalation pathways.
Continuous monitoring is necessary to ensure operational reliability, forecasting quality, data integrity, cybersecurity, model performance, and user adoption.
The availability and performance of the forecasting infrastructure should be continuously monitored using automated monitoring systems. Monitoring indicators may include system uptime, server performance, API responsiveness, database integrity, storage utilization, cybersecurity alerts, and network availability. Automated alerts should notify technical teams when service interruptions occur, computational loads exceed thresholds, data pipelines fail, abnormal activity is detected, and backup failures occur. Disaster recovery procedures should be periodically tested to ensure operational resilience.
Forecasting outputs should undergo continuous evaluation against observed epidemiological data. Performance monitoring should include predictive accuracy, calibration performance, temporal validation, spatial validation, changepoint detection, uncertainty estimation, and robustness across epidemiological phases. Model performance indicators may include forecasting error metrics, confidence interval coverage, sensitivity analyses, recalibration frequency, and scenario stability. Significant discrepancies between predicted and observed epidemiological trends should trigger model review, recalibration, parameter reassessment, and methodological updates.
Continuous data quality monitoring is essential to ensure forecasting reliability. Monitoring activities should assess completeness of datasets, reporting delays, consistency between sources, missing data patterns, anomalous observations, and harmonization quality. Automated validation checks should identify inconsistent entries, outliers, corrupted records, missing variables, and delayed reporting. Data quality reports should be periodically reviewed by responsible authorities.
The use of the Forecasting Tool by stakeholders should also be monitored to assess adoption, usability, operational relevance, and sustainability. Operational indicators may include frequency of system access, number of forecasting scenarios generated, user engagement metrics, dashboard usage, training participation, and stakeholder feedback. User feedback mechanisms should allow continuous collection of usability concerns, operational challenges, desired functionalities, and improvement suggestions.
Cybersecurity and legal compliance should be continuously monitored through security audits, vulnerability assessments, access log reviews, penetration testing, compliance assessments, and incident reporting systems. Monitoring should ensure ongoing compliance with GDPR, national health data regulations, cybersecurity frameworks, and institutional governance procedures. Security incidents should trigger immediate containment measures, incident investigations, corrective actions, and reporting procedures where legally required.
The Forecasting Tool should evolve continuously in response to new epidemiological evidence, emerging pathogens, technological developments, stakeholder feedback, healthcare system needs, and scientific advances in modelling methodologies. Periodic operational reviews should evaluate forecasting relevance, infrastructure scalability, governance effectiveness, sustainability, and public health impact. Continuous improvement processes should support model modernization, infrastructure optimization, improved interoperability, enhanced visualization tools, better communication strategies, and expanded preparedness capabilities. The operational governance structure should therefore ensure that the Forecasting Tool remains scientifically robust, operationally sustainable, technically secure, and adaptable to future public health challenges.