US dataswitch to UK
Epidemiologists
communicating research findings on various types of diseases to health practitioners, planning and directing studies to investigate human or animal disease and providing expertise in the design. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: overseeing public health programs, including statistical analysis, health care planning, surveillance systems and public health improvement is work software can't reach.
What shifts is monitoring and reporting incidents of infectious diseases to local and state health agencies: the overhead at the edges, not the middle you trained for.
Your week, as this page understands it
Investigate and describe the determinants and distribution of disease, disability, or health outcomes. May develop the means for prevention and control. The job title says “epidemiologists”. The real job is the part underneath: overseeing public health programs, including statistical analysis, health care planning, surveillance systems and public health improvement. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But epidemiologists is not one task. It is 16 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is overseeing public health programs, including statistical analysis, health care planning, surveillance systems and public health improvement, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 13%
- changing shape
- 48%
- staying human
- 39%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 44 out of 100 (37–51 allowing for uncertainty): partial exposure, across 16 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for epidemiologists is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
2 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Monitoring and reporting incidents of infectious diseases to local and state health agencies
This is reading one thing and writing another: incidents of infectious diseases in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Monitor and report incidents of infectious diseases to local and state health agencies.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Notifiable disease reporting follows fixed forms and rules, which software already fills from lab feeds.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying and analyzing public health issues related to foodborne parasitic diseases and their impact on public policies
This is reading one thing and writing another: public health issues in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Identify and analyze public health issues related to foodborne parasitic diseases and their impact on public policies, scientific studies, or surveys.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Pulling together what is known about foodborne parasites and its policy relevance is literature and data synthesis.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Changing shape
7 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Communicating research findings on various types of diseases to health practitioners
The software now makes the first pass at research findings, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Communicate research findings on various types of diseases to health practitioners, policy makers, and the public.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Findings can be written up clearly for different audiences, though persuading officials still leans on a trusted messenger.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Providing expertise in the design
The software now makes the first pass at expertise, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Provide expertise in the design, management and evaluation of study protocols and health status questionnaires, sample selection, and analysis.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Protocol and questionnaire design follows well published methods, so drafts are usually close to usable.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Investigating diseases or parasites to determine cause and risk factors
The software now makes the first pass at diseases, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Investigate diseases or parasites to determine cause and risk factors, progress, life cycle, or mode of transmission.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Literature and data work is strong support, but pinning down how a disease actually spreads needs expert investigation.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Educating healthcare workers, patients and the public about infectious and communicable diseases, including disease transmission and prevention
The software now makes the first pass at healthcare workers, patients and the public, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Educate healthcare workers, patients, and the public about infectious and communicable diseases, including disease transmission and prevention.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Teaching material on disease transmission is abundant and easy to generate, though delivery often happens in person.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 4/4.
Staying human
7 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Planning and directing studies to investigate human or animal disease
The ratings behind this row put studies well outside what today's tools can do on their own.
importance 4 · CoreSource: “Plan and direct studies to investigate human or animal disease, preventive methods, and treatments for disease.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: AI drafts study designs well, yet a named researcher still has to run the study and answer for it.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Overseeing public health programs, including statistical analysis, health care planning, surveillance systems and public health improvement
The value here is that a specific person handles public health programs, including statistical analysis and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Oversee public health programs, including statistical analysis, health care planning, surveillance systems, and public health improvement.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Analysis is easy to automate, but running a public health program means leading people and owning decisions.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Conducting research to develop methodologies
The ratings behind this row put research well outside what today's tools can do on their own.
importance 4 · CoreSource: “Conduct research to develop methodologies, instrumentation, and procedures for medical application, analyzing data and presenting findings.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Data analysis is well supported, but developing new methods and instruments needs hands-on scientific work.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Planning, administering and evaluating health safety standards and programs to improve public health
The value here is that a specific person handles health safety standards and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Plan, administer and evaluate health safety standards and programs to improve public health, conferring with health department, industry personnel, physicians, and others.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Standards can be drafted, but agreeing and running safety programs across agencies depends on working relationships.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Show the other 6 tasks
Writing articles for publication in professional journals
changing shapeThe software now makes the first pass at articles, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Write articles for publication in professional journals.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: AI drafts scientific writing well, but a publishable journal article still needs substantial expert rewriting.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Writing grant applications to fund epidemiologic research
changing shapeThe software now makes the first pass at grant applications, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Write grant applications to fund epidemiologic research.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Grant applications draft quickly, but a fundable one is heavily rewritten around the specific funder.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Consulting with and advising physicians
changing shapeThe software now makes the first pass at and advising physicians, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Consult with and advise physicians, educators, researchers, government health officials and others regarding medical applications of sciences, such as physics, biology, and chemistry.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Scientific advice can be drafted well, though physicians and officials weigh who is giving it.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Teaching principles of medicine and medical and laboratory procedures
staying humanThe value here is that a specific person handles principles of medicine and stands behind it. That is earned, not computed.
importance 3 · SupplementalSource: “Teach principles of medicine and medical and laboratory procedures to physicians, residents, students, and technicians.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: AI can build the teaching material, though residents and technicians still learn procedures from a person in the room.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Supervising professional, technical and clerical personnel
staying humanThe value here is that a specific person handles professional, technical and clerical personnel and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Supervise professional, technical, and clerical personnel.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Supervising staff runs on day-to-day trust with the individuals being managed.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Preparing and analyzing samples to study effects of drugs
staying humanThis work happens in the physical world: samples, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Prepare and analyze samples to study effects of drugs, gases, pesticides, or microorganisms on cell structure and tissue.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Preparing samples and running lab tests on cells and tissue is bench work with real materials.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $87,220a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 12,090in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: incidents of infectious diseases in, a record out. The rows above are exactly that shape: monitoring and reporting incidents of infectious diseases to local and state health agencies and identifying and analyzing public health issues related to foodborne parasitic diseases and their impact on public policies. What it cannot do is be trusted in person, which is what public health programs, including statistical analysis run on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Start with what does not change: overseeing public health programs, including statistical analysis, health care planning, surveillance systems and public health improvement is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 13% of this job's task weight sits in rows the software is already learning, 48% in rows that change shape rather than disappear, and 39% in rows it is nowhere near. That is the position, measured across 16 scored tasks. It is not a forecast about you.
So the thing worth your attention is not the job going away. It is the layer around it. Monitoring and reporting incidents of infectious diseases to local and state health agencies is the part turning into software, and being the person who understands that layer is worth money.
This week: one thing
Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to incidents of infectious diseases, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.
- What you end up holding
- a straight answer about what is actually being rolled out, and when
- How long it takes
- ten minutes
If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether public health programs, including statistical analysis are in scope or not. Nothing to log into, no license needed.
Over the next 90 days
Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near communicating research findings on various types of diseases to health practitioners. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.
Over the next 12 months
On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to epidemiologists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was medical scientists, except epidemiologists: only about 35% of its durable work is work you already do. Your own job splits about 13/87: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “communicate research findings on various types of diseases to health practitioners, policy…”, and let the exposed end go.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Medical Scientists, Except Epidemiologists
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “plan and direct studies to investigate human or animal disease, preventive methods, and treatments for disease”. Across the whole of both lists that adds up to about 35% of the work in that job the software is not taking.
Why I am not recommending it: It is closer than most, and still not close enough: about 35% of that job's durable work is already yours, against the 35% I want to see before I will call something a route.
Health Education Specialists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already supervise professional, technical, and clerical personnel, and their equivalent is to supervise professional and technical staff in implementing health programs, objectives, and goals. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $64,070 against your $87,220, 26.5% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Medical and Health Services Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already supervise professional, technical, and clerical personnel, and their equivalent is to direct, supervise and evaluate work activities of medical, nursing, technical, clerical, service, maintenance…. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 13% of its task weight, across 16 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like overseeing public health programs, including statistical analysis, health care planning, surveillance systems and public health improvement arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Other researchers, unspecified discipline is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Other researchers, unspecified discipline, Natural and social science professionals n.e.c., Biochemists and biomedical scientists and Biological scientists. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for epidemiologists, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 13% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for epidemiologists. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for epidemiologists launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Epidemiologists?
- Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Epidemiologists (United States, SOC 19-1041), 13% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 44 out of 100 (range 37–51, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Epidemiologists” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Identify and analyze public health issues related to foodborne parasitic diseases and their impact on public policies, scientific studies, or surveys” (75/100, high); “Monitor and report incidents of infectious diseases to local and state health agencies” (66/100, high); “Write articles for publication in professional journals” (58/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Epidemiologists” stay human?
- About 39% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Prepare and analyze samples to study effects of drugs, gases, pesticides, or microorganisms on cell structure and tissue” (10/100, minimal); “Supervise professional, technical, and clerical personnel” (13/100, minimal); “Plan, administer and evaluate health safety standards and programs to improve public health, conferring with health department, industry personnel, physician…” (26/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Epidemiologists” do about AI?
- Start from the ledger rather than the headline: 13% of this job's weighted core work is exposed, and roughly 39% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Epidemiologists calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 16 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.