Predicting outbreaks is one of the great challenges of modern public health. When we talk about diseases such as human foot-and-mouth disease, known as HFMD, the ability to anticipate peaks of contagion not only saves lives, but also optimizes hospital resources and allows a coordinated response between health authorities, educational centers and the community. However, traditional forecasting models, based solely on time series or numerical treatment of external covariates, clash with the multifactorial and changing nature of these epidemics. In this context, an innovative approach emerges that combines the symbolic reasoning of large language models (LLMs) with the statistical soundness of probabilistic forecasts. This article explores how a two-agent architecture—an LLM-based event interpreter and a forecaster—can deliver auditable, contextual, and actionable predictions for HFMD, and how this philosophy aligns with the development of advanced AI-powered technology solutions for businesses and institutions.
The central problem is that HFMD outbreaks do not follow purely seasonal patterns. Factors such as the school calendar, weather conditions, government surveillance reports, and even quarantine policies have a direct impact on transmission dynamics. Classical models—ARIMA, Prophet, and even modern foundational models such as Chronos or TimesFM—treat these external variables as simple numerical inputs, lacking the semantic capacity to interpret contradictory signals or integrate epidemiological knowledge. For example, an increase in temperature may indicate increased viral activity, but if schools are simultaneously closed, the combined interpretation is not trivial. A purely numerical system can hardly decide which factor to weight more without explicit reasoning.
The solution proposed in recent works —and which we analyze here from an applied perspective— decouples two fundamental tasks: contextual interpretation and the generation of probabilistic forecasting. In the first agent, a specialized LLM acts as an interpreter of events: it ingests heterogeneous signals (school bulletins, weather summaries, government releases, clinical guidelines) and produces a scalar signal of impact on transmission. This signal is not a crude numerical prediction, but a conceptual indicator that reflects how recent events modify the risk of contagion. The second agent, the forecast generator, combines that signal with historical case counts to produce point estimates, which are then transformed into probabilistic intervals using moment-fitting techniques with Poisson or negative binomial distributions. The result is a one-week forecast — the critical horizon for hospital bed planning — accompanied by a concise textual justification that explains why the risk is expected to increase or decrease.
This neuro-symbolic approach has profound implications beyond numerical precision. In clinical and public health settings, a prognosis is not useful if it cannot be audited and explained. Decision-makers need to understand the fundamentals of an alert in order to act with confidence. This is where the combination of AI agents with semantic reasoning capabilities makes a difference. By generating textual explanations – for example, 'school closures reduce transmission, but high temperatures increase it; the net effect is a moderate increase in risk'—a bridge is created between data and action. This auditability is similar to what is sought in other sectors where the transparency of the models is critical, such as cybersecurity or business intelligence.
For technology companies working in the field of digital health, this paradigm opens up enormous opportunities. Developing bespoke applications that integrate LLM agents to interpret contextual data and generate auditable forecasts is not just a matter of predictive performance, but of usability and trust. At Q2BSTUDIO, we understand that deploying AI in critical environments requires a multidisciplinary approach: from software architecture to data security to integration with AWS and Azure cloud services. Our expertise in creating custom software allows us to design systems that go beyond forecasting, incorporating interactive dashboards, contextual alerts, and feedback mechanisms that empower public health teams.
From a technical perspective, the two-agent architecture can be scaled to handle multiple diseases, regions, and data sources. The key is modularity: the event interpreter can be a pre-trained LLM tuned with epidemiological literature, while the forecast generator can be based on lightweight statistical models that guarantee the speed of inference. This design is ideal for deployment in cloud environments, where the elasticity of AWS and Azure cloud services allows large volumes of data to be processed in real time without compromising latency. In addition, the integration with business intelligence tools such as Power BI makes it easier to visualize forecasts and their justifications, connecting directly with decision-making teams.
Another relevant aspect is cybersecurity. Systems that handle public health data must meet strict standards of protection. The incorporation of AI agents should not be a weak point; On the contrary, the neuro-symbolic architecture allows each interpreted signal and each forecast decision to be recorded, generating an audit trail that reinforces confidence. At Q2BSTUDIO we offer specialized AI services for companies, ensuring that each model complies with the regulatory and ethical requirements of the sector. In addition, our cybersecurity teams perform penetration testing and vulnerability analysis to protect the underlying infrastructure, whether on-premise or in the cloud.
The practical application of this technology has already been tested in real environments. In studies with surveillance data from Hong Kong and Lishui hospitals, the LLM agent approach demonstrated competitive accuracy against classical models, but with the added advantage of providing robust confidence intervals (85-100% coverage at 90% intervals) and clear explanations. These results validate that the combination of symbolic and statistical artificial intelligence is not only feasible, but superior in contexts where interpretability is as important as accuracy. For organizations looking to implement similar solutions, the way forward is to have a technology partner who is proficient in both the fundamentals of AI agents and integration with existing systems.
Beyond the healthcare field, the same principle can be applied to demand forecasting, inventory management or fraud detection, where contextual events (news, regulatory changes, weather) play a determining role. The ability of a system to read and reason about unstructured information and combine it with numerical data is a cross-cutting enabler. Companies that invest in services, business intelligence, and AI agent platforms are positioning themselves to make faster, more informed decisions. At Q2BSTUDIO, we develop these capabilities on demand, creating everything from prototypes to production systems that integrate Power BI for visualization and analysis.
In conclusion, the future of epidemiological prediction – and predictive analytics in general – is not in purely numerical models or black box systems. The convergence between the symbolic reasoning of LLMs and the probabilistic soundness of classical models offers a path to auditable, contextual, and actionable forecasts. For public health institutions and companies looking to transform data into decisions, this approach represents an opportunity to move towards proactive, evidence-based management. At Q2BSTUDIO, we combine our expertise in custom applications, artificial intelligence, and AWS and Azure cloud services to build solutions that not only predict, but explain and empower. Contact us to find out how we can help you implement AI agents in your organization.
