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Epidemiologists

Also called Disease epidemiologist, Infectious disease epidemiologist, Pharmacoepidemiologist, Hospital epidemiologist, Genomic epidemiologist

In brief

In May 2025, BLS counted 12,090 US epidemiologists, with median pay of $87,220 a year. Of the BLS counts since 2018, the May 2025 count is the highest. The earliest figure here is an estimate of 500 from 1910, not measured the same way as today's count. The last full technology era for epidemiologists: AI-powered outbreak surveillance and genomic epidemiology platforms, 2014 to 2020. AI-assisted surveillance tools increased the reach and speed of outbreak detection without reducing epidemiology headcount.

What the sources say

Official outlook

BLS projects employment to grow 18.7% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Epidemiologists: jobs and pay since 1910 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-1041

Chicago

Future History. "Epidemiologists: jobs and pay since 1910." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-1041.

BibTeX

@misc{futurehistory_19_1041, author = {{Future History}}, title = {Epidemiologists: jobs and pay since 1910}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-1041}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
1850187519001925195019752000now
2026
Known then as Physician / Sanitarian (investigator of epidemic disease, pre-professional era)
Known then as Medical Officer of Health / Hygienist (first professionalised public health roles)
Known then as Epidemiologist / Preventive Medicine Physician (academic and government roles emerging)
Known then as Epidemiologist (CDC/state health department and academic position; MPH/PhD credential normalised)
Known then as Epidemiologist (BLS SOC 19-1041; spans infectious, chronic, pharmaco-, genomic, and environmental sub-specialties)
Known today as Epidemiologist (BLS SOC 19-1041; spans infectious, chronic, pharmaco-, genomic, and environmental sub-specialties)
Projected to still be called Epidemiologist (BLS SOC 19-1041; spans infectious, chronic, pharmaco-, genomic, and environmental sub-specialties)
Data from the May 2025 survey
US Employment
12K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$87,220
Est.

≈ $84,984 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1850

May 6, 1850: The Epidemiological Society of London holds its first meeting at Hanover Square, London, under the presidency of Lord Ashley. John Snow and William Farr are among the founding members. The Society's founding statement defines its purpose as investigating "the causes and conditions which influence the origin, propagation, mitigation, and prevention of epidemic disease" -- a sentence that could appear verbatim in a 2026 job posting.

Beat · 1854

August-September 1854: John Snow conducts the Broad Street cholera investigation in London's Soho neighbourhood, interviewing hundreds of residents, mapping deaths by household, and identifying the Broad Street water pump as the source. He persuades the Vestry of St. James to remove the pump handle. The epidemic was already declining when the handle came off -- Snow himself acknowledged this -- but the investigation established the case-interview-and-map method that remains the template for every field investigation conducted today.

Beat · 1916

June 12, 1916: The Rockefeller Foundation awards $267,000 to Johns Hopkins University to establish the School of Hygiene and Public Health -- the first school of public health in the United States. Epidemiology is one of its founding departments. Wade Hampton Frost becomes the first professor of epidemiology in the United States in 1930. By the 1920s, Columbia, Harvard, Yale, and Michigan follow with their own public health schools. These institutions create the academic pipeline that will supply the modern epidemiology workforce.

Beat · 1951

July 1, 1951: The first class of 22 Epidemic Intelligence Service officers reports to CDC in Atlanta. The program was conceived by Alexander Langmuir, Chief Epidemiologist at CDC, partly in response to Cold War fears of biological warfare. Over the following 70 years, EIS trains more than 4,100 applied epidemiologists who investigate outbreaks ranging from the 1955 polio vaccine safety crisis (the Cutter incident) to the first recognition of AIDS in 1981, SARS in 2003, and the first US COVID-19 cases in January 2020. The two-year program becomes the gold standard for field epidemiology training worldwide.

Beat · 1981

June 5, 1981: CDC's MMWR publishes a short report by EIS officer Michael Gottlieb describing five cases of Pneumocystis carinii pneumonia in young gay men in Los Angeles -- the first published description of what would become the AIDS epidemic. The epidemiological investigation that followed, tracing the "Patient Zero" network using contact tracing and sexual history interviews, demonstrated that epidemic investigation methods developed for cholera in Victorian London could be applied to an entirely novel retroviral disease. AIDS epidemiology drove major advances in cohort study methods, survival analysis, and the statistical handling of competing risks.

Beat · 2003

February-July 2003: The SARS-CoV-1 outbreak -- 8,098 probable cases and 774 deaths in 29 countries -- became the first major test of global infectious disease surveillance infrastructure since the 1918 influenza. Epidemiologists established the clinical case definition within days, estimated the serial interval and basic reproduction number within weeks, and provided the epidemiological evidence that drove quarantine and hospital infection control policies that contained the outbreak without a vaccine. The SARS response also exposed how poorly equipped most national public health systems were for rapid epidemic analysis and communication -- a lesson that led to the 2005 revision of the International Health Regulations (IHR) and significant investment in national epidemiology capacity in Asia and North America.

Beat · 2020

January-March 2020: Epidemiologists globally mobilise for the COVID-19 pandemic. BlueDot detects the Wuhan cluster on January 9, 2020; HealthMap flags signals on December 30, 2019. The Johns Hopkins COVID-19 Dashboard (launched January 22, 2020) aggregates global case counts in near real time, becoming the most-viewed epidemiological display in history. In the US, CSTE surveys show that state and local health department epi positions grow 23% from 2017 to 2021, primarily through COVID-19 response hiring. The pandemic also triggers severe burnout: post-COVID CSTE surveys find 1 in 4 epi positions nationally unfilled as departures from government roles outpace the new hires.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace epidemiologists? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+18.7%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 19-1041 (Epidemiologists): 12.8 thousand jobs in 2025, projected 15.2 thousand in 2035, a change of +2.4 thousand (+18.7%). Matrix employment counts all jobs in the occupation, including self-employed workers. The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
55.8%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 19-1041.00 Epidemiologists. Shown: human-rated β = 0.558, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.346 and γ (all LLM-assisted) 0.769; GPT-4-rated β 0.442. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for epidemiologists

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereConduct genomic epidemiology surveillance using Nextstrain and Pangolin, submitting pathogen sequences from surveillance networks to GISAID

Conduct genomic epidemiology surveillance using Nextstrain and Pangolin, submitting pathogen sequences from surveillance networks to GISAID; running Pangolin lineage assignment to classify circulating SARS-CoV-2, influenza, or other pathogen variants; building and interpreting Nextstrain phylogenetic trees to identify transmission clusters, geographic spread patterns, and novel clade emergence; and translating phylogenomic findings into actionable public health recommendations for response prioritization.[6],[7],[13]

Where your edge is

Pangolin automates the lineage assignment step that once required manual phylogenetic expertise, but the biological interpretation layer is yours: deciding whether a novel sublineage represents convergent evolution, a genuine new transmission chain, or a sequencing artifact requires pathogen-specific knowledge that no pipeline provides. Build fluency in reading Nextstrain trees by practicing with historical outbreak datasets (SARS-CoV-2 early 2020, mpox 2022) so you develop intuition for what meaningful cluster divergence looks like versus sequencing noise. Genomic epidemiologists who can bridge the bioinformatics pipeline to the public health action, writing the MMWR brief that translates a phylogeny into a vaccination priority recommendation, are the highest-value professionals in this space.

AI is sitting alongside you hereConduct systematic reviews and meta-analyses on disease etiology, intervention effectiveness, or diagnostic test accuracy: using Elicit to screen thousands of PubMed abstracts for PICO eligibility, extracting effect estimates and quality assessments from included studies, applying random-effects meta-analysis in R (meta, metafor packages), producing forest plots and funnel plots, and writing GRADE-quality evidence summaries for clinical practice guideline panels or WHO technical advisory groups.

Conduct systematic reviews and meta-analyses on disease etiology, intervention effectiveness, or diagnostic test accuracy: using Elicit to screen thousands of PubMed abstracts for PICO eligibility, extracting effect estimates and quality assessments from included studies, applying random-effects meta-analysis in R (meta, metafor packages), producing forest plots and funnel plots, and writing GRADE-quality evidence summaries for clinical practice guideline panels or WHO technical advisory groups.[14],[12],[1]

Where your edge is

Elicit and PubMedBERT-based tools compress the abstract screening phase of a systematic review from weeks of manual double-extraction to days of AI-assisted triage with human verification: adopt them for any review involving hundreds of abstracts. Your critical contribution is the full-text eligibility adjudication (resolving inclusion ambiguities that AI misclassifies), the risk-of-bias assessment (which requires methodological judgment about the specific study design and outcome reported), and the GRADE evidence-to-recommendation step (which requires knowing the policy context, the values and preferences of the affected population, and the feasibility of the intervention). The GRADE step cannot be automated; it is a structured expert judgment process that guideline developers will not accept from an AI system.

AI is sitting alongside you hereMonitor global infectious disease signals using AI-powered surveillance platforms, reviewing BlueDot AI and HealthMap dashboards for emerging outbreak alerts, assessing EIOS WHO cluster reports and ProMED postings, cross-referencing genomic surveillance signals from Nextstrain against travel and case data, and escalating credible threats through the health department's IHR notification chain or institutional incident command system within required reporting windows.

Monitor global infectious disease signals using AI-powered surveillance platforms, reviewing BlueDot AI and HealthMap dashboards for emerging outbreak alerts, assessing EIOS WHO cluster reports and ProMED postings, cross-referencing genomic surveillance signals from Nextstrain against travel and case data, and escalating credible threats through the health department's IHR notification chain or institutional incident command system within required reporting windows.[3],[4],[5],[1]

Where your edge is

BlueDot and EIOS now surface thousands of signals automatically across many languages: your job has shifted from first-line signal detection to expert triage: deciding which AI-flagged alerts represent genuine novel threats versus known endemic background. Build a structured signal-scoring workflow that evaluates each alert against pathogen characteristics (transmissibility, severity, immune escape potential), geographic context (health system capacity, travel connectivity), and data quality (official vs. informal source, case definition consistency). The epidemiologist who can rapidly assess a new signal and decide within hours whether it warrants escalation, rather than within days, is the irreplaceable human layer in a system that AI can monitor but cannot judge.

Where epidemiologists go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Medical and Health Services Managers

Hospital epidemiologists and health department program epidemiologists often move into Medical and Health Services Manager roles, as directors of infection prevention programs, population health departments, quality improvement offices, or public health agency divisions. This transition is particularly natural for hospital epidemiologists (a well-established subspecialty combining epidemiology with clinical infection control) who already operate in healthcare administration contexts and have established relationships with hospital C-suite leadership. The pivot offers a substantial salary increase and strong employment growth (much faster than average). For public health agency epidemiologists, the transition requires building administrative and budget management competencies but leverages the interagency coordination, policy communication, and program evaluation skills already developed in applied epi roles.

What you'd add
  • · Healthcare operations and quality management: Joint Commission infection prevention standards, CMS Conditions of Participation for infection control, hospital quality metrics (HAI rates, CAUTI, CLABSI), the language of hospital administration that epidemiologists must learn to lead infection prevention programs effectively
  • · Healthcare financial management: hospital operating budget structure, DRG reimbursement and value-based purchasing incentives, the financial case for infection prevention programs (HAI cost avoidance, CMS HAC penalty avoidance), grant accounting for HRSA and CDC hospital preparedness program funding
  • · Electronic health record and population health informatics: Epic Clarity and Caboodle data warehouse querying for population health management, HL7 FHIR data standards for health information exchange, population health management platform evaluation (Health Catalyst, Arcadia, Lightbeam), increasingly required for Medical and Health Services Manager roles in large health systems
  • · Regulatory compliance and accreditation management: CMS Conditions of Participation, OSHA bloodborne pathogen standard, state public health reporting requirements, the compliance landscape that hospital and health system managers navigate
  • · Leadership and change management: managing multidisciplinary clinical teams (physicians, nurses, infection preventionists, environmental services) with different professional cultures and incentives; facilitating behavior change in clinical practice through evidence-based intervention design rather than mandate
What it takesMost of your skills carry over
Tools of the era

How has the work of epidemiologists changed since 1850?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Hand mapping + vital statistics registers (Snow, Farr, and the dot-map era)

    John Snow's 1854 Broad Street investigation introduced the foundational tools of the trade: door-to-door case interviews, a hand-drawn dot map of deaths by residence, and comparison of attack rates across subgroups (households using different water companies). William Farr's vital statistics office provided the population denominators that made Snow's rate calculations possible. These methods -- spatial case plotting, attack rate comparison, hypothesis testing against exposure data -- remain the conceptual skeleton of every outbreak investigation conducted today, from COVID-19 to salmonella traced to romaine lettuce. The instruments were a pencil, a ledger, and the willingness to walk.

  • Vital statistics systems + punch-card tabulation (first quantitative surveillance infrastructure)

    William Farr's national vital statistics system, first implemented in England and Wales in 1839, provided the population-level denominators that transformed anecdote into epidemiology. US states began mandatory death registration around 1900-1910; the national Death Registration System was complete by 1933. Punch-card tabulation machines (IBM Hollerith tabulators) arrived at federal health agencies during the 1930s and 1940s, allowing mortality and morbidity data to be aggregated and cross-tabulated by cause, age, sex, and geography at a speed that was impossible with hand ledgers. This era also produced the first structured communicable disease surveillance reports: the US Public Health Service began publishing Morbidity and Mortality Weekly Reports (predecessor to the modern MMWR) in 1952. The epidemiologist's job shifted from single-outbreak investigation toward continuous population surveillance.

    Effect on the work

    Punch-card tabulation reduced the manual labour of mortality table construction from months to weeks but created new analytical roles -- biometric officers, health statisticians -- that were distinct from but adjacent to epidemiology. The net effect was to scale the surveillance function rather than displace workers.

  • Cohort study design + mainframe statistical computing (Doll/Hill, Framingham, SAS era)

    Two landmark studies in the 1950s crystallised the modern epidemiological method and transformed what the occupation was for. Richard Doll and Austin Bradford Hill's 1950 case-control study and 1954 cohort study of British doctors established that cigarette smoking caused lung cancer -- despite resistance from clinicians who found the absence of a biological mechanism uncomfortable. The Framingham Heart Study, launched in 1948 by the US Public Health Service, became the template for the longitudinal cohort study, following thousands of residents of Framingham, Massachusetts for decades to identify cardiovascular risk factors. These designs required statistical methods (the Mantel-Haenszel test, logistic regression, Cox proportional hazards model) that were feasible only with mainframe computers, and the arrival of SAS (first released 1976) and statistical packages on university mainframes gave epidemiologists the computational infrastructure to run multi-variable models on large cohort datasets. The occupation professionalized around methods expertise as much as subject expertise during this period.

  • Personal computers + Epi Info / SPSS desktop (democratisation of epidemiological analysis)

    CDC released the first version of Epi Info in 1985, a free DOS-based software package that allowed epidemiologists at state and local health departments to design questionnaires, enter data, and run standard outbreak analysis (epidemic curves, attack rate tables, chi-square tests, odds ratios) on a desktop PC without mainframe access. It was transformative for field epidemiology: an EIS officer arriving at a state health department to investigate a foodborne outbreak could set up a line-list database and run the first analysis within hours of landing. Epi Info spread to more than a hundred countries by the early 1990s and became the standard tool for field investigation globally. SAS and SPSS also moved to desktop platforms during the late 1980s, making multivariable regression analyses that had required mainframe time feasible on a personal computer. The democratisation of analytical tools expanded the epidemiologist's working reach without displacing the occupation.

    Effect on the work

    Desktop computing allowed a single epidemiologist to do the data management and analysis work that had previously required a team of data-entry clerks and a statistician. Net employment effect was positive -- more epidemiological analyses were conducted -- rather than displacing existing positions.

  • R, Python, SAS + electronic health records + large administrative claims databases (modern pharmacoepidemiology era)

    The 2000s saw two structural shifts that remade the epidemiologist's toolkit. First, electronic health records (the US HITECH Act of 2009 mandated their adoption) and administrative insurance claims databases (IBM MarketScan, Optum, CMS Medicare/Medicaid) created linked longitudinal datasets on tens of millions of patients -- far too large for the desktop tools of the Epi Info era. Second, R (open-source statistical language, widely adopted in epidemiology by the mid-2000s) and Python gave epidemiologists programmatic access to large datasets, reproducible analysis pipelines, and community-built packages for causal inference (the MatchIt, WeightIt, and dagitty ecosystems). These tools enabled pharmacoepidemiology and real-world evidence (RWE) to become major sub-specialties: epidemiologists could now estimate vaccine effectiveness, drug safety signals, and health policy impacts from administrative data at a scale and speed that the cohort study design of the 1950s-70s could not approach. The pharmaceutical and CRO sectors began hiring epidemiologists with causal inference expertise at salaries that competed with government and academia for the first time, beginning the bifurcation in the occupation between public health government roles and private-sector RWE roles.

  • AI-powered outbreak surveillance (BlueDot, HealthMap, EIOS) and genomic epidemiology platforms (Nextstrain, Pangolin)

    Two distinct AI tooling waves arrived within a few years of each other and changed what field and surveillance epidemiologists could see. BlueDot (founded 2014) and HealthMap (Boston Children's Hospital, open-source since 2006 but AI-augmented through the 2010s) applied natural language processing to global news, social media, and official reports across dozens of languages, surfacing outbreak signals days to weeks before official declarations. Both systems detected COVID-19 signals in late December 2019, before the WHO Emergency Committee convened on January 22, 2020. WHO launched EIOS (Epidemic Intelligence from Open Sources) in 2019, operationalising AI-assisted signal screening for national public health authorities in many countries. Simultaneously, Nextstrain (Fred Hutchinson, NIH-funded) and Pangolin (Rambaut et al.) brought automated phylogenetic surveillance to genomic epidemiology: a single epidemiologist could now track pathogen evolution and transmission chains across continents in near real time, work that required a large bioinformatics team in the SARS-2003 era. These tools expanded what the occupation could accomplish without displacing epidemiologists -- they automated the first-line monitoring layer while the human triage and response functions remained essential.

    Effect on the work

    AI-assisted surveillance tools increased the reach and speed of outbreak detection without reducing epidemiology headcount. CSTE 2021 found state and local health department epi positions grew 23% from 2017 to 2021, with most of the growth driven by COVID-19 response needs alongside the tool adoption.

  • Large language models + AI synthesis tools (Elicit, PubMedBERT, EpiNow2 Bayesian pipelines)

    The 2020s AI wave layered on top of the surveillance tools a second set of capabilities aimed at the analytical and writing tasks: Elicit (launched 2021) compresses systematic literature review triage from weeks to days by screening millions of PubMed abstracts for PICO eligibility; PubMedBERT and BioBERT-family models automate named entity recognition and relationship extraction from clinical notes and scientific text; EpiNow2 and EpiEstim provide Bayesian Rt estimation pipelines that a single analyst can deploy against national surveillance data, work that in 2019 required a modeling team. LLMs (including general-purpose tools used for drafting MMWR briefs, grant application backgrounds, and surveillance report narratives) added a writing augmentation layer. The combined effect is that an epidemiologist equipped with these tools in 2026 can accomplish surveillance, analysis, and synthesis work that would have taken a team of five in 2015. The occupation remains strongly human in its fieldwork, causal inference judgment, policy communication, and regulatory accountability functions -- but the AI augmentation tier is now standard equipment, not a novelty.

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Epidemiologists employment and pay by year

On record since1850
Latest US employment12,090 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$87,220 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+18.7% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 26 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
1910500n/aEst., NCBI Bookshelf Researched contextReport
19511,200n/aEst., CDC Foundation Researched contextReport
1990n/a$38,000Est., US Office of Personnel Management Researched contextReport
20033,770$54,530BLS OEWS May 2003 Official statisticReport
20044,560$54,800BLS OEWS May 2004 Official statisticReport
20053,630$52,170BLS OEWS May 2005 Official statisticReport
20064,120$56,670BLS OEWS May 2006 Official statisticReport
20073,960$60,010BLS OEWS May 2007 Official statisticReport
20084,370$61,360BLS OEWS May 2008 Official statisticReport
20094,610$61,700BLS OEWS May 2009 Official statisticReport
20104,710$63,010BLS OEWS May 2010 Official statisticReport
20114,610$64,220BLS OEWS May 2011 Official statisticReport
20124,850$65,270BLS OEWS May 2012 Official statisticReport
20135,350$66,330BLS OEWS May 2013 Official statisticReport
20145,420$67,420BLS OEWS May 2014 Official statisticReport
20155,460$69,450BLS OEWS May 2015 Official statisticReport
20165,690$70,820BLS OEWS May 2016 Official statisticReport
20176,870$69,660BLS OEWS May 2017 Official statisticReport
20187,060$69,660BLS OEWS May 2018 Official statisticReport
20197,410$70,990BLS OEWS May 2019 Official statisticReport
20207,500$74,560BLS OEWS May 2020 Official statisticReport
20218,180$78,830BLS OEWS May 2021 Official statisticReport
20229,430$78,520BLS OEWS May 2022 Official statisticReport
202310,230$81,390BLS OEWS May 2023 (employment) Official statisticBLS OEWS May 2023 (pay) Official statisticReport
202411,460$83,980BLS OEWS May 2024 Official statisticReport
202512,090$87,220BLS OEWS May 2025 Official statisticReport
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