EpidBot: A Natural Language Platform for Generalized Epidemic Intelligence

Public health professionals have access to more data than ever before. Yet answering a relatively simple epidemiological question often requires navigating multiple databases, formats, software tools, and reporting systems. As a result, valuable data often remain locked behind technical barriers, making it harder for public health professionals to turn information into decisions.

We developed EpidBot to simplify this process. EpidBot is a platform that allows users to retrieve, analyze, visualize, model, and report epidemiological data through natural language interaction. By connecting multiple public health data sources within a single environment, the platform enables users to conduct analyses that would traditionally require several independent tools and specialized technical skills.

Rather than functioning solely as a search interface, EpidBot supports complete analytical workflows. Users can explore surveillance data, compare trends across locations and time periods, generate maps and visualizations, construct epidemiological models, and produce structured technical reports while maintaining full visibility of data sources and analytical procedures.

To show what this looks like in practice, we present representative use cases, including the automatic generation of a mathematical model for Ebola virus disease in the Democratic Republic of the Congo. From a single user request, EpidBot assembled evidence from published sources, generated and calibrated a compartmental transmission model, identified key transmission drivers, evaluated intervention scenarios, and produced a technical report with quantitative findings and policy-relevant recommendations.

EpidBot shows how natural language interaction can reduce the technical barriers that often separate public health professionals from the analyses they need to perform. By bringing data access, analysis, modeling, visualization, and reporting into a single environment, the platform helps transform information into evidence while preserving transparency and reproducibility.

Author summary

Public health professionals rarely struggle with a lack of data. More often, they struggle with the effort required to find the right data, combine information from different sources, perform analyses, create visualizations, and communicate results. Even relatively simple questions can require moving between multiple databases, software tools, and reporting systems.

We created EpidBot to simplify this process. EpidBot allows users to interact with epidemiological data using natural language while preserving the analytical rigor required for public health practice. Instead of serving only as a search tool, the platform supports complete workflows that may include data retrieval, visualization, statistical analysis, mathematical modeling, and report generation.

In this article, we show how these workflows can be applied in practice through a series of examples, including the automatic construction of an Ebola transmission model for the Democratic Republic of the Congo. Starting from a single user request, EpidBot gathered evidence from published sources, identified key transmission drivers, evaluated intervention scenarios, and generated a technical report with quantitative results and public health recommendations.

EpidBot was designed for epidemiologists, surveillance teams, researchers, and public health professionals who need answers, not just data. By reducing technical barriers and bringing multiple analytical tasks into a single environment, the platform helps transform information into evidence that can support public health decisions.