The points to be checked are the security of data ingestion pipelines, the integrity and completeness of imported datasets, the harmonization and standardization of epidemiological data, the reliability of automated data update procedures, and the robustness of database storage systems. The security of data transfers between implementing Member States (MS), healthcare institutions, and forecasting servers should be verified through secure communication protocols, authentication mechanisms, encryption procedures, and access control policies. Verification should include organizational audits, cybersecurity assessments, penetration testing, validation of user access privileges, and review of data governance procedures. Automated validation procedures should verify completeness of datasets, absence of corrupted records, consistency across data sources, correct variable formatting, and temporal consistency of reporting. Special attention should be paid to surveillance data, hospitalization data, intensive care unit (ICU) admission data, mortality data, vaccination coverage, and genomic surveillance data regarding variants of concern (VOCs). Where automated data pipelines are implemented, the reliability of scheduled synchronization and update procedures should be tested under both normal and failure conditions.
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The points to be checked are correctness of model implementation, reproducibility of simulations, calibration procedures, forecasting robustness, handling of uncertainty, and scalability of simulations. Verification of epidemiological models should include internal code review, validation against published methodologies, independent replication of results where possible, sensitivity analyses, and stress testing across multiple epidemiological scenarios. The implementation of the models should be verified to ensure that equations are correctly implemented, parameters are appropriately calibrated, intervention effects are consistently applied, assumptions are documented, and simulations remain reproducible. Calibration procedures should be verified using historical epidemiological data, temporal validation, spatial validation, rolling forecast evaluation, and changepoint assessment. Forecasting outputs should be evaluated against observed epidemiological trends, including infection incidence, hospitalization rates, ICU admissions, mortality, and healthcare resource utilization. Verification should also assess confidence interval coverage, stability of forecasts, robustness to missing data, robustness to delayed reporting, and robustness to epidemiological transitions.
The points to be checked are predictive accuracy, temporal consistency, robustness across epidemiological phases, responsiveness to changing outbreak conditions, and recalibration performance. Forecasting performance should be evaluated continuously using retrospective validation, prospective validation, out-of-sample testing, rolling forecast windows, and scenario comparison analyses. Performance indicators may include mean absolute error (MAE), root mean square error (RMSE), calibration metrics, prediction interval coverage, and trend detection accuracy. Particular attention should be given to forecasting performance during emergence of VOCs, intervention changes, behavioural shifts, seasonal transitions, and healthcare system saturation periods. If substantial discrepancies between predicted and observed epidemiological data are identified, recalibration procedures should be triggered and verified.
The points to be checked are server availability, infrastructure scalability, API responsiveness, redundancy systems, disaster recovery mechanisms, and cybersecurity protections. The forecasting infrastructure should undergo load testing, performance testing, failover testing, backup restoration testing, and cybersecurity assessments. Simple redundancy mechanisms such as load balancing, mirrored servers, cloud redundancy, and distributed backups should be implemented to reduce the risk of operational interruption. If cloud or external hosting providers are used, their uptime performance, security certifications, operational reliability, and disaster recovery capabilities should be independently assessed. Infrastructure verification should also ensure that software dependencies are stable, updates do not compromise functionality, APIs remain interoperable, and dashboards remain responsive under high usage loads.
For each user interface and dashboard, the verification process should include correctness of displayed forecasting outputs, integrity of visualizations, consistency of epidemiological indicators, usability, accessibility, and multilingual compatibility where applicable. Verification should ensure that forecasts displayed in dashboards match backend calculations, visualizations update correctly after recalibration, intervention scenarios are correctly represented, uncertainty intervals are appropriately visualized, and exported reports remain accurate. User interface testing should involve public health professionals, epidemiologists, healthcare planners, policymakers, and technical users. Accessibility verification should ensure compatibility with different devices, various operating systems, accessibility standards, and secure authentication workflows.
The points to be checked are General Data Protection Regulation (GDPR) compliance, cybersecurity protections, access control systems, audit trails, and incident response mechanisms. Verification procedures should include penetration testing, vulnerability scanning, access privilege reviews, audit log validation, and encryption testing. Compliance verification should confirm that personal data handling follows applicable regulations, anonymization procedures are effective, data retention policies are enforced, and data-sharing agreements are respected. Security incident simulations should also verify incident detection, containment procedures, recovery workflows, and notification mechanisms.
Before release into production, operational readiness verification should ensure staff training completion, availability of technical support, existence of operational Standard Operating Procedures (SOPs), incident escalation procedures, governance workflows, and communication protocols. Simulation exercises and pilot deployments may be used to verify real-world usability, operational coordination, reporting workflows, stakeholder interaction, and emergency response integration. Operational verification should confirm that the Forecasting Tool can remain scientifically robust, operationally reliable, technically secure, scalable, and sustainable, under routine surveillance conditions as well as during public health emergencies.