Offering epidemiological forecasting as a service to public health authorities, healthcare systems, policymakers, and operational users implies several stages: epidemiological and healthcare data must be available and accessible, data pipelines must securely integrate heterogeneous data sources, forecasting models must be continuously calibrated and updated, forecasting outputs must be distributed through secure interfaces, and stakeholders must have access to visualization and decision-support tools. The architecture of the Forecasting Tool is therefore composed of data sources, data integration pipelines, epidemiological modelling engines, databases, Application Programming Interfaces (APIs), dashboards and visualization layers, monitoring systems, and operational governance components.
More in detail, the forecasting tool should be implemented according to a reference layered architecture, independent of specific technologies, comprising at least the following functional layers:
This layered structure supports interoperability, scalability and long-term maintainability, and allows components to be replaced or upgraded without redesigning the full system.
At minimum, the architecture should support standardised data exchange mechanisms (e.g., well-documented Application Programming Interfaces (APIs) and structured data formats), enabling traceable integration with external surveillance systems, healthcare capacity monitoring tools, and reporting platforms, where such systems exist at national or European level.
The choice between cloud-based and on-premises deployment should be explicitly justified, taking into account legal and ethical requirements; as well as data sovereignty, privacy, confidentiality, cybersecurity policies, and national technical capacities.
Deployments should comply with applicable national cybersecurity requirements and implement role-based access control, audit logging, and secure backup policies.
The forecasting infrastructure depends on the availability of multiple heterogeneous datasets originating from surveillance systems, hospitals, laboratories, vaccination registries, genomic surveillance programmes, demographic databases, and public health reporting systems. The online availability and interoperability of these datasets may vary substantially between implementing Member States (MS). Relevant datasets include infection incidence, hospitalization data, intensive care unit (ICU) admissions, mortality, vaccination coverage, healthcare capacity indicators, demographic structures, mobility data, non-pharmaceutical intervention (NPI) timelines, and genomic surveillance data regarding variants of concern (VOCs). Next-generation data sources include wastewater surveillance data, digital epidemiology signals, participatory surveillance platforms, wearable device data, and other novel data streams, which may complement traditional surveillance systems and enhance forecasting capabilities. Data may exist in structured databases, APIs, spreadsheets, surveillance repositories, statistical reports, public dashboards, and institutional data warehouses. The Forecasting Tool therefore focuses on mechanisms for secure ingestion, harmonization, integration, synchronization, and validation, rather than on a single centralized repository.
A forecasting infrastructure may involve local health jurisdictions, regional health authorities, national public health institutes, and supranational organizations. Each jurisdiction may maintain local datasets, define local intervention strategies, use different surveillance standards, and operate under distinct healthcare constraints. The architecture therefore requires mechanisms capable of integrating heterogeneous data sources, supporting local customization, propagating updates across systems, and maintaining interoperability between jurisdictions. Indirect dependencies may exist between surveillance providers, genomic repositories, healthcare systems, and external modelling services. Changes in upstream datasets or epidemiological assumptions should therefore propagate efficiently throughout the forecasting infrastructure.
A forecasting infrastructure must curate epidemiological datasets, healthcare indicators, sociodemographic information, intervention timelines, calibrated model parameters, forecasting outputs, and metadata and documentation. These repositories should exist in machine-readable formats, standardized schemas, interoperable structures. The repositories should support automated synchronization, version control, audit trails, reproducibility, and secure access management. The machine-readable forecasting infrastructure may include structured databases, APIs, configuration files, metadata registries, and model parameter repositories. Version control systems should track data updates, model recalibrations, software changes, intervention assumptions, and forecasting revisions. Within EUVABECO-like infrastructures, open and interoperable formats may include JSON, CSV, API-based standards, and interoperable epidemiological schemas.
The forecasting architecture requires secure and automated pipelines capable of ingesting heterogeneous data, harmonizing variables, validating datasets, anonymizing sensitive information, synchronizing updates, and feeding epidemiological models. The data integration layer should support batch synchronization, scheduled updates, near real-time ingestion, API interoperability, and audit logging. The architecture should also support error detection, data validation, anomaly monitoring, rollback mechanisms, and redundancy procedures. Where multiple MS contribute data, harmonization layers should ensure standardized definitions, consistent coding systems, aligned temporal resolutions, and interoperable geographic identifiers.
The modelling layer represents the computational core of the Forecasting Tool. The architecture should support compartmental models, stochastic simulations, hybrid approaches, scenario analyses, and uncertainty quantification.
The implementation workflow should be conceived as an iterative process, integrating continuous feedback between data quality assessment, model calibration, scientific verification and technical testing. Therefore, the modelling engine should allow periodic recalibration, integration of new epidemiological evidence, adaptation to VOCs, incorporation of behavioural changes, intervention simulations, and healthcare capacity constraints.
In this framework, verification refers to ensuring that the system complies with its technical specifications and is suitable for release, while validation refers to assessing whether the tool effectively meets the needs of its users in real-world decision contexts.
Model development, calibration and initial validation using historical data should precede the integration of models into user-facing systems.
Model verification must be formalised as an ongoing activity, with explicit acceptance criteria including:
1. minimum predictive performance against historical data;
2. robustness to data uncertainty and variation;
3. interpretability of results by non-technical decision-makers.
The modelling infrastructure may rely on open-source programming environments, containerized deployments, distributed computing, and scalable computational frameworks. The modelling engine should remain modular to facilitate methodological updates, integration of new pathogens, scalability, and interoperability.
The forecasting architecture should expose APIs supporting retrieval of forecasting outputs, scenario submission, dashboard synchronization, interoperability with external systems, and automated reporting workflows. APIs should support secure authentication, encrypted communication, standardized query structures, and machine-readable outputs. Interoperability mechanisms should facilitate integration with national surveillance systems, healthcare infrastructures, immunization information systems, hospital management systems, public dashboards, and emergency preparedness platforms.
Stakeholders require access to visualization tools capable of presenting: epidemic curves, hospitalization forecasts, ICU projections, mortality trends, intervention scenarios, uncertainty intervals, geographic heatmaps, and healthcare system indicators. Visualization systems may support web-based dashboards, mobile interfaces, reporting systems, and exportable figures and reports. Dashboards should be designed for public health authorities, healthcare planners, epidemiologists, policymakers, and operational response teams. Where applicable, multilingual interfaces and accessibility standards should be supported.
Continuous operational monitoring is required to supervise server availability, computational performance, API responsiveness, data synchronization, forecasting execution, and cybersecurity events. Monitoring systems should include automated alerts, performance dashboards, incident management tools, audit logging, and disaster recovery mechanisms. Redundancy mechanisms may include mirrored servers, cloud failover systems, distributed backups, and load balancing. Finally, the architecture should remain resilient to infrastructure failures, cyberattacks, data synchronization interruptions, and high computational loads.
###