AI-enabled mpox surveillance in Africa

For public health leaders, the promise of AI-enabled mpox surveillance is not simply predictive models but a coordinated operational architecture that integrates reporting, laboratory confirmation, and field response. Deployed thoughtfully, machine learning and rules-based decision support can improve timeliness and fidelity of case detection, prioritize contact tracing, and optimize allocation of scarce supplies. Yet algorithms are only as useful as the data pipelines, governance, and human workflows around them. In practice, success hinges on clarity of use cases, quality standards, and the institutional arrangements that authorize data access and define accountability. This requires alignment from national disease control programs through district teams to community-based networks.

Policy context and strategic alignment

AI for mpox must embed within existing Integrated Disease Surveillance and Response structures, avoiding parallel systems and duplication. Strategic alignment with Africa CDC frameworks and WHO recommendations clarifies minimum data sets, reporting cadence, and escalation thresholds. Policies should articulate how biosurveillance signals inform risk assessment, incident management, and cross-border information exchange. Critically, national strategies need explicit positions on open standards, public interest data sharing, and ethical review pathways for algorithmic tools. When governments set these parameters early, partners can tailor investments to reduce fragmentation and promote sustainability.

Core capabilities: data acquisition, analytics, and decision support

Operationalizing AI begins with consistent case definitions, structured forms, and validation routines for both clinical and community reporting. Analytics layers should support descriptive, inferential, and predictive tasks that are auditable and explainable to field teams. Early warning functions, including outbreak analytics, can flag anomalies in space and time while quantifying uncertainty. Decision support must present interpretable outputs that map to concrete actions, such as triggering specimen transport, prepositioning PPE, or mobilizing risk communication. High-performing systems privilege clarity, traceability, and operational usefulness over opaque complexity, with continuous feedback loops from end users to data scientists.

Ethical and legal foundations: privacy, consent, and trust

Public trust is foundational to any digital intervention that processes personal or sensitive data. Policies should define consent models, data minimization, pseudonymization, and clear retention schedules that reflect the least data necessary for public health purposes. Transparent risk assessments of AI tools, including bias and harms, support legitimate interests while guarding against misuse. Community engagement, particularly with affected populations, can surface practical concerns about identifiability, stigma, and use of geolocation. Independent oversight and transparent documentation of model purpose, training data, and performance help maintain legitimacy and enable proportionate safeguards.

Building the infrastructure for scale

Scaling from pilots to routine use requires infrastructure that is robust to network variability, power interruptions, and heterogeneous devices. Ministries should specify reference architectures that support online and offline data capture, automated synchronization, and version control for forms and models. Sustainable architectures also define security baselines, including encryption and access controls tuned to role-based permissions. Procurement should favor modularity so that components can evolve without system-wide rewrites. Above all, investment plans should tie infrastructure choices to prioritized use cases rather than technology for its own sake.

Interoperability and data standards

Interoperability is the backbone of multi-partner response, enabling laboratory results, case reports, and logistics data to flow across systems. Adoption of open, widely-implemented standards and terminologies, combined with pragmatic integration patterns, reduces vendor lock-in. Where national electronic health records or registries exist, surveillance feeds should be harmonized through common identifiers and metadata. Standardized geocoding, timestamps, and facility codes enable geospatial analyses and cross-border reconciliation. Consistent semantics and data quality rules are essential so that models trained in one province remain valid when deployed elsewhere.

Workforce capacity and operational readiness

AI-enabled surveillance only succeeds when frontline staff can reliably enter data and interpret outputs. Training programs should be tiered, from basic digital literacy to advanced analytics for national teams, with mentorship and communities of practice. Clear standard operating procedures embed roles for data officers, epidemiologists, and logisticians in the response cycle. Simulations and after-action reviews strengthen readiness while surfacing usability gaps and unintended consequences. Investing in a multidisciplinary workforce accelerates the translation of analytics into decisions in real-world conditions.

Sustainable funding and procurement models

Short-term emergency grants can launch prototypes but rarely cover maintenance, retraining, and governance overheads. Blended financing that includes domestic budgets, pooled donor funds, and outcome-linked contracts can stabilize operations. Procurement should require interoperability, transparent pricing, and rights to data and configuration artifacts to avoid lock-in. Service-level agreements must include model monitoring, recalibration, and security updates as part of routine support. Funding should follow functions: core surveillance, analytics, and response coordination should be protected budget lines to ensure continuity between outbreaks.

Cross-border coordination and resilience

Mpox does not respect borders, and neither should the information architecture that supports response. Regional protocols for data sharing, privacy protection, and joint risk assessment allow signals to be contextualized across mobility corridors. Structured liaison between neighbors can align case definitions, enhance mutual assistance, and reduce reporting asymmetry. Regional dashboards that expose aggregated indicators enable situational awareness without compromising individual privacy. By designing for regional use from the outset, national systems avoid retrofitting during crises.

Regional data sharing and risk communication

Cross-border coordination requires technical standards and governance agreements that are understood by both informatics teams and policymakers. Memoranda of understanding can codify the circumstances and granularity of data exchange, while joint exercises test operational readiness. Regional situation reports should include structured uncertainty narratives so that decisions are robust to data limitations. Risk communication should be synchronized to minimize conflicting messages and misinformation. Where feasible, federated learning can support model training across datasets without moving sensitive data, balancing utility with sovereignty.

Evaluation, metrics, and continuous learning

Accountable deployment requires metrics that cover both technical performance and public health value. Technical metrics include timeliness, completeness, false alert rates, and stability across data drifts; operational metrics include time to investigation, specimen turnaround, and supply chain responsiveness. Governance metrics should track compliance with data governance policies and adherence to ethical standards. Embedding A-B testing and routine model audits fosters learning without compromising response. Publishing performance summaries and lessons learned strengthens transparency and accelerates regional knowledge transfer.

Governance for emerging technologies

As tools evolve, ministries will face decisions about advanced architectures, including edge inference, synthetic data, and privacy-preserving pipelines. Clear criteria should guide when to adopt innovations, balancing benefits with operational complexity and regulatory maturity. A national advisory mechanism that includes epidemiology, informatics, law, and community perspectives can adjudicate trade-offs. Attention to interoperability and backward compatibility avoids stranding prior investments. Principles-based governance coupled with iterative safeguards allows innovation without sacrificing accountability or equity.

The path to AI-enabled mpox surveillance in Africa is a governance and systems challenge as much as a technical one. Aligning policy, architecture, and workforce capacity will determine whether analytics improve outcomes equitably and reliably. Investments should prioritize digital health functions that are explainable, auditable, and interoperable, nested within regional cooperation. Integrating One Health perspectives can also surface zoonotic and environmental signals relevant to early warning. Ultimately, progress will be measured not only by model accuracy but by faster, fairer, and more trusted decisions when they matter most.