Epidemiology to One Health.

For the first time, we can calculate health results across humans, animals, and the earth's ecology to improve health and stop pandemics at their source.

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Level 2: What is epidemiology not just?

We start with what epidemiology is not just, because the history reveals why One Health emerged.

The Historical Negations

≠ Just Medicine

If epidemiology were just medicine, it would begin with Neanderthal healthcare (250,000 years ago). Skilled pro-social care existed, but without population-scale data.

≠ Just Rational Records

If it were just Hippocratic case notes (400 BCE) documenting natural causes, we would have excellent individual observations—but no rates, no statistics.

≠ Just Public Health

If it were just government systems—hospitals, vaccinations, social safety nets (1000 CE)—we would have institutions without the mathematical tools to guide them.

≠ Just Germ Theory

If it were just scientific medicine (1880), we would understand pathogens but lack the frameworks to model their movement through populations.

The Turning Point: 1950

Modern epidemiology emerges

When standard scientific methods met mathematical calculation (1950–2010), epidemiologists controlled diseases at scale:

  • Smallpox eradicated (the only human disease ever eliminated)
  • Polio, malaria, TB suppressed through mass vaccination and antibiotics
  • Millions of lives saved annually

Interactive Timeline: 250,000 Years to One Health

Click any era to explore the progression:

250,000 BP – Neanderthal Medicine

Technically advanced pro-social healthcare. Evidence of caregiving for injured individuals, but no systematic disease tracking.

1000 CE – Public Health Systems

Hippocratic-style records, hospitals, vaccinations (variolation), and social safety nets. State-funded responses to disease, but lacking statistical frameworks.

1880 – Scientific Medicine

Germ theory established. Tabular data and environmental interventions (John Snow's cholera maps). Foundation for evidence-based medicine.

1960 – Epidemiology Controls Diseases

Global mass vaccinations and antibiotics at scale. Millions of lives saved annually following control of smallpox, malaria, polio, TB.

1970 – Actuarial Orientation

Risk factor analysis, surveillance systems, and child vaccination programs. Control of measles, rubella, yellow fever.

1980 – Modern Tools

Computers enable SEIR models and large cohort studies. Smallpox eradication certified. Elimination of malaria, polio, and measles targeted.

1990 – Population-Scale Policy

Stochastic models and epidemic simulation. Solutions for complex epidemics: AIDS, SIDS, Ebola, Hepatitis C.

2010 – Limits Reached

Narrow technical approaches hit barriers. Models exclude environment, vector ecology, and climate. Emerging diseases accelerate.

→ 2010-2026 – One Health (The New Epidemiology)

For the first time in history, technical ability and political motivation converge to integrate:

  • Human health data
  • Animal surveillance (zoonoses)
  • Ecosystem monitoring (vectors, climate)

The WHO One Health Initiative aims to get derailed 2030 global health goals back on track.

Note: The US withdrawal from WHO in 2025 has created funding instability, but global commitment continues.

Why This Matters for You

One Health involves most natural and social sciences. Whether you're a computer scientist (agent-based models), ecologist (vector ecology), veterinarian (zoonotic spillover), or climate researcher (changing disease ranges)—your discipline contains variables and rates essential to global health.

Ready for the technical details? →

Level 3: Technical Monograph

Discovering Epidemiology and One Health

For scientists joining the new health collaboration

Author: Dan Shearer, University of Southampton
Format: PDF (45 pages, 692 KB)
Date: February 2026
License: CC BY-SA (where applicable)

Contents:

  1. Introduction
  2. What is epidemiology not just?
  3. What exactly is One Health?
  4. Conclusion
  5. Appendix A: Cheatsheet glossary for non-epidemiologists
  6. Appendix B: Historical steps towards epidemiology
  7. Appendix C: How did Epidemiology forget the environment?
  8. Appendix D: Minimum requirement: mathematics (SIR, Gillespie Algorithm)
  9. Appendix E: US withdrawal from WHO and One Health
  10. Appendix F: Governance structures (CBPP, Polycentricity)
  11. Bibliography: 80+ references (ancient sources to 2026)

📄 Download PDF

Also available: BibTeX source | LaTeX source

Audience Guide

Computer Scientists

See Appendix D for Rule-Based Epidemiology Modelling (RBEM), agent-based vs. compartmental models, and the combinatorial explosion problem in multi-species modeling.

Ecologists & Veterinarians

See Appendix C on environmental exclusion from 20th-century epidemiology, and Appendix A for zoonosis/reservoir/vector definitions.

Policy Researchers

See Appendix E for analysis of the 2025 US withdrawal from WHO and the WHO Pandemic Agreement (May 2025).

Historians of Science

See Appendix B for the "Can epidemiology be done?" test applied to ancient medical texts (Hippocrates to Al-Razi).

Next Steps

One Health needs your disciplinary expertise. Start here:

Search Google Scholar WHO One Health RBEM Codeberg

Last updated: February 2026 | Contact