Executive Summary

Public-health teams and academic research groups routinely spend days to weeks writing and debugging code to produce epidemic curves, case maps, and survival plots from messy line-lists and genomic metadata; this problem affects an estimated 60,000 organizations globally and contributes to delayed situational awareness and inconsistent reporting that can cost lives and funding responsiveness. The total addressable market implied by a $60K annual contract value per organization is roughly $3.6B, so the need is both widespread and commercially meaningful for vendors that can reliably reduce manual effort and error. A viable product would be an automated visualization and reporting platform that ingests FHIR/DHIS2 feeds, public surveillance line-lists, and genomic metadata, applies configurable epidemiological methods, produces reproducible epidemic curves, geospatial maps, and survival analyses, and generates transparent natural-language narratives and audit trails using LLM-assisted toolchains. Features would include automated data cleaning, selectable statistical methods with provenance, exportable high-resolution figures and dashboards, and secure on-prem/offline deployment options for sensitive environments. This market is attractive now because three coincident trends materially lower barriers to entry and increase buyer readiness: matured AI-assisted analytics that can automate method selection and narrative generation, growing availability of public surveillance and genomic data, and expanding adoption of interoperability standards like FHIR and DHIS2. Independent metrics suggest strong commercial potential (Market Score 92/100, Revenue Potential 84/100) and real demand for time-saving solutions in outbreak response and routine surveillance. To stand out you must pair automation with epidemiological rigor: make method choices transparent, provide validation datasets, and offer certified deployment and training pathways to build institutional trust—features that many competitors in a “medium” competitive landscape currently underdeliver. Expect real challenges around heterogeneous data quality, regulatory and privacy constraints, and the need for clinical validation and long sales cycles with public agencies, but a focused product that delivers measurable time savings and reproducible outputs could capture significant share if you invest early in partnerships and certification.

Market Opportunity

Automated epidemiology figures: epidemic curves, maps, survival plots targets a $3.6B = 60,000 potential public-health organizations & research groups x $60K ACV total addressable market with medium saturation and a year-over-year growth rate of 14% CAGR for health analytics & surveillance tooling.

Key trends driving demand: AI-assisted analytics -- LLMs and model toolchains automate data cleaning, method selection, and narrative generation for figures, reducing manual coding time.; Open/public surveillance data -- more shared line-lists and genomic metadata enable integrated visualizations across outbreaks and pathogens.; Standards & interoperability -- FHIR/DHIS2 adoption lowers integration friction for real-time feeds into visualization pipelines.; Reproducible-research demand -- journals and funders increasingly require reproducible code and auditable analysis, favoring tools that emit both figures and code..

Key competitors include Esri ArcGIS, Tableau (Salesforce), CDC Epi Info, R / Python custom workflows (ggplot2, matplotlib, survminer, Shiny), Nextstrain / outbreak.info (adjacent open solutions).

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Automated epidemiology figures: epidemic curves, maps, survival plots

Executive Summary

Market Validation

Market Opportunity

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