PHC4024: Applied Epidemiology

Course Description

PHC4024 Applied Epidemiology is the course in which a public health student stops studying epidemiologic concepts and starts using them — investigating outbreaks, working with surveillance data, and doing the analysis that a health department actually does.

The course is offered at approximately four Florida institutions: Florida Gulf Coast University, Florida International University, the University of Florida and the University of West Florida.

Florida Gulf Coast University carries it under the statewide title, Applied Epidemiology, at 3 credits: "principles, methods, and techniques of epidemiological investigation focusing on both infectious and noninfectious diseases," with "emphasis on outbreak investigations, field epidemiology, and epidemiology careers," and it is described as "an epidemiological methods course which builds on" prior epidemiology. Florida International University carries it as Principles of Applied Epidemiology at 3 credits: "methods and techniques used by epidemiologists investigating the distribution and causes of diseases are studied," with "a holistic approach to principles of disease surveillance and control."

⚠⚠ The University of West Florida runs a broader course under this number, and you should check which version yours is. UWF titles it Applied Research Methods in Public Health: "students will learn how to conduct and interpret research in population health and disease, building on their knowledge from epidemiology and biostatistics and other content areas in public health," providing "skills in critical" appraisal and research practice. That is a general public health research methods course, of which applied epidemiology is one part. See Special Information.

What "applied" means here, and why it is a genuinely different course from introductory epidemiology. The first epidemiology course teaches the vocabulary and the calculations — incidence, prevalence, relative risk, odds ratios, study designs, bias and confounding. This course puts you in the position of someone who has to use them under time pressure with incomplete information. An introductory course asks you to compute an odds ratio; this one asks you what to do when eighteen people at a wedding reception are ill, you have a guest list, and the caterer wants to know whether to close.

The outbreak investigation is the course's organising structure, and the standard steps are worth knowing before you start because everything else hangs on them: establish the existence of an outbreak; verify the diagnosis; define and identify cases; describe the outbreak by person, place and time; develop hypotheses; test them analytically; implement control measures; and communicate the findings. ⚠ The step students most often misplace is control measures — they come as soon as you have enough to act, not after the analysis is finished. Outbreak investigation is not a research project; the point is to stop it.

Descriptive epidemiology carries far more weight than students expect. The epidemic curve — cases plotted against time of onset — is the single most informative object in an investigation. Its shape distinguishes a point-source outbreak from a continuous common-source or propagated one, and working backwards from the peak by the incubation period gives you an estimated exposure window. A well-drawn epi curve frequently identifies the source before any analytic work is done at all.

The analytic half is the retrospective cohort and the case-control study, chosen according to whether the exposed population is enumerable. ⚠ The choice is practical, not theoretical: if you have a guest list you can do a cohort study and compute attack rates; if you are investigating cases scattered across a city with no defined population, you need controls. Knowing which design the situation permits is the assessed skill.

Surveillance is the other pillar and the less dramatic one. Notifiable disease reporting, syndromic surveillance, sentinel systems, registries and vital statistics — the routine collection that makes it possible to notice an outbreak at all. ⚠ Surveillance data is always incomplete, and understanding how it is incomplete is the skill: reporting is passive and under-ascertained, case definitions change, and testing availability determines counts as much as disease does.

⚠ Florida makes this course unusually concrete. The state's epidemiology is distinctive: vector-borne disease with locally acquired dengue and periodic Zika and West Nile activity; a very large elderly population shaping chronic disease and influenza burden; enormous tourist and seasonal population flows that complicate denominators and case attribution; hurricane-related morbidity and post-storm surveillance; and waterborne and marine hazards including Vibrio and harmful algal bloom exposures. The Florida Department of Health publishes the data, and its county epidemiology programmes are where graduates work.

Learning Outcomes

Required Outcomes

• Apply core epidemiologic measures — incidence, prevalence, attack rate, mortality, case fatality — and select the appropriate measure for a question.
• Compute and interpret measures of association — risk ratio, odds ratio, risk difference — and state what each does and does not support.
• Construct and interpret an epidemic curve, and distinguish point-source, continuous common-source and propagated patterns.
• Perform descriptive epidemiology by person, place and time, including basic spatial description.
• Write an operational case definition with confirmed, probable and suspect categories, and explain the sensitivity-specificity trade-off in setting it.
• Conduct the steps of an outbreak investigation in the correct order and justify departures from it.
• Select and justify an appropriate study design for a given investigation — retrospective cohort or case-control — on practical grounds.
• Design a questionnaire for an outbreak investigation.
• Identify and address bias and confounding — selection bias, information bias, recall bias, confounding — and apply stratification.
• Apply causal inference reasoning to observational findings.
• Explain surveillance systems — passive, active, sentinel, syndromic — and evaluate a system's completeness, timeliness and representativeness.
• Explain notifiable disease reporting and the legal reporting framework.
• Interpret screening test performance — sensitivity, specificity, predictive values — and explain why predictive value depends on prevalence.
• Apply epidemiologic methods to chronic and non-infectious disease as well as infectious disease.
• Explain infectious disease transmission dynamics — incubation period, infectious period, reproduction number, herd immunity threshold.
• Recommend and justify control measures appropriate to the stage of an investigation.
• Manage and clean an epidemiologic dataset and produce appropriate tables and figures.
• Communicate findings to a professional audience and to the public, including honest expression of uncertainty.
• Apply ethical and legal standards — confidentiality, HIPAA, IRB requirements, and the distinction between public health practice and research.

Optional Outcomes

• Use statistical software — Epi Info, R, SAS or Stata — for epidemiologic analysis.
• Apply GIS and spatial analysis to disease mapping and cluster detection.
• Explain molecular and genomic epidemiology — PulseNet, sequence-based outbreak linkage.
• Explain infectious disease modelling at an introductory level.
• Investigate a suspected disease cluster and explain why most reported clusters are not real.
• Apply environmental and occupational epidemiologic methods.
• Explain disaster and post-hurricane epidemiology — ⚠ a standing Florida application.
• Explain pharmacoepidemiology and surveillance of adverse events.
• Explain One Health and zoonotic outbreak investigation.
• Explain career pathways in epidemiology, including applied training programmes.

Major Topics

Required Topics

• Review of epidemiologic measures and study designs.
• Case definitions and case ascertainment.
• Descriptive epidemiology — person, place, time; the epidemic curve.
• Outbreak investigation — the standard steps, in practice.
• Hypothesis generation and testing in the field.
• Retrospective cohort and case-control designs in outbreak settings.
• Questionnaire design and data collection.
• Bias, confounding and stratification.
• Causal inference from observational data.
• Surveillance systems — types, evaluation, notifiable disease reporting.
• Screening and diagnostic test performance.
• Infectious disease dynamics — incubation, transmission, R, herd immunity.
• Chronic disease epidemiology.
• Control and prevention measures.
• Data management and presentation.
• Communication of findings, including risk communication.
• Ethics, confidentiality and the practice/research distinction.

Optional Topics

• Statistical software for epidemiology.
• GIS and spatial epidemiology.
• Molecular and genomic epidemiology.
• Infectious disease modelling.
• Cluster investigation.
• Environmental and occupational epidemiology.
• Disaster epidemiology.
• Pharmacoepidemiology.
• One Health and zoonoses.
• Careers and applied training programmes.

Resources & Tools

• The CDC's own materials are the primary sources for this course, and they are free: Principles of Epidemiology in Public Health Practice (the "Self-Study Course SS1978") — the CDC's own field epidemiology text, comprehensive and free; the CDC Field Epidemiology Manual; and the Epidemic Intelligence Service case studies, which are classroom-ready reconstructions of real investigations and are among the best teaching material available anywhere.
• Textbooks in use: Gregg, Field Epidemiology; Friis and Sellers, Epidemiology for Public Health Practice; Gordis, Epidemiology (the standard introductory text, normally the prerequisite course's book); Aschengrau and Seage, Essentials of Epidemiology in Public Health.
• ⚠ MMWR (Morbidity and Mortality Weekly Report) is free, weekly, and publishes real outbreak investigations in exactly the format you will be asked to write. Reading two or three MMWR outbreak reports early in the term teaches the genre faster than any textbook chapter.
• Software: Epi Info (CDC, free) is purpose-built for outbreak investigation and is what many health departments actually use; R (free) with the epidemiology packages; SAS and Stata where licensed; and Excel for smaller analyses. OpenEpi (free, browser-based) handles two-by-two tables and sample size quickly.
• Florida data: FLHealthCHARTS (flhealthcharts.gov) — county-level indicators; Florida Department of Health weekly arbovirus and influenza surveillance reports; the FDOH reportable disease list and practitioner reporting guidance; the FWC red tide status maps for HAB-related exposure work; and Merlin, Florida's reportable disease surveillance system, which the department describes publicly.
• National data: CDC WONDER, BRFSS, NNDSS (notifiable diseases), and FoodNet.
• Professional bodies and applied training: the Council of State and Territorial Epidemiologists (CSTE), which sets case definitions and runs an Applied Epidemiology Fellowship; the American Public Health Association; and ⚠ the CDC's Epidemic Intelligence Service, the field's flagship two-year applied training programme — competitive, and normally entered after a doctorate or an MPH with experience.
• Journals: American Journal of Epidemiology, Epidemiology, Emerging Infectious Diseases (free), Annals of Epidemiology.

Career Pathways

• Epidemiologists (SOC 19-1041) — ⚠ most epidemiologist positions require an MPH or higher, and this course is the undergraduate entry point rather than the qualification. Be clear about that when planning.
• Public health analysts and surveillance staff (SOC 13-1111, 19-4092) — ⚠ Florida county health departments employ epidemiology staff at bachelor's level in surveillance, data and investigation support roles, and this is the most realistic direct destination.
• Disease intervention specialists (SOC 21-1094) — contact tracing and case investigation, particularly in STI and TB programmes; a genuine entry-level route into health departments.
• Infection preventionists (SOC 29-9021, 21-1094) — hospital-based; ⚠ the CIC credential (Certification in Infection Prevention and Control) is the standard, and Florida's large hospital systems employ heavily.
• Clinical research coordinators and data managers (SOC 11-9121, 19-4021) — academic medical centres and contract research organisations.
• Health data analysts and biostatisticians (SOC 15-2041, 15-2051) — with quantitative training.
• Emergency preparedness and response (SOC 11-9161) — ⚠ a durable Florida specialism given the hurricane cycle.
• Environmental health specialists (SOC 29-9011) — foodborne outbreak investigation is a joint epidemiology and environmental health function in Florida county health departments.
• Pharmaceutical and device safety (SOC 19-1041) — pharmacovigilance and post-market surveillance.
• Federal service — CDC, FDA, the VA, and the US Public Health Service Commissioned Corps.
• Graduate study — the MPH in epidemiology is the standard next step; also MS and PhD programmes. ⚠ This course is one of the strongest lines on an application, because it demonstrates applied capability rather than only coursework.

Special Information

⚠⚠ The scope differs between institutions — check which course yours is

	Statewide / FGCU / FIU	UWF
Title	Applied Epidemiology / Principles of Applied Epidemiology	Applied Research Methods in Public Health
Scope	epidemiologic investigation — outbreak investigation, field epidemiology, surveillance and control	public health research methods generally — conducting and interpreting research in population health, building on epidemiology and biostatistics
Relationship	UWF's is broader; applied epidemiology is one component of public health research methods

This guide is written to the majority reading — applied epidemiology — because it matches the statewide title and two documented institutions. UWF's course covers a wider methodological range and correspondingly less outbreak-investigation practice.

⚠ Why this matters practically. These are not equivalent preparation for the same next step:

• If you want field epidemiology — health department work, outbreak response, CSTE or EIS-track careers — the applied epidemiology version is the one that prepares you, and its outbreak investigation exercise is the portfolio piece.
• If you want research — graduate study, a thesis, clinical research coordination — the broader research methods version is arguably better preparation, since it covers appraisal and design across methods rather than one applied setting.
• The credit articulates either way, because the number is identical. ⚠ That is the risk rather than the reassurance: a receiving programme will record you as having applied epidemiology whichever you took. Keep your syllabus, and if a subsequent course or a practicum assumes outbreak investigation skills you have not practised, say so early.

Not treated as a one-number-two-subjects split — both are public health methods courses and one contains the other, which is scope narrowing rather than a different subject. But it is a real difference and the guide names it prominently.

Prerequisites and position in the curriculum

⚠ This is a second course and every source says so. FGCU calls it "an epidemiological methods course which builds on" prior epidemiology; UWF describes students "building on their knowledge from epidemiology and biostatistics."

The genuine prerequisites are introductory epidemiology and biostatistics, and they are content rather than gatekeeping: this course assumes you can already compute and interpret rates and measures of association, and that you know what a confounder is. ⚠ A student without them will be learning the tools and their application at once, which is the most common reason students struggle here.

⚠ Comfort with data handling is the unlisted prerequisite that matters. Real investigation data is messy — inconsistent dates, free-text symptoms, missing values — and a substantial share of the work is cleaning it. Prior experience with Excel, and ideally with R or Epi Info, materially changes how this course goes.

The course sits in the senior year of a public health degree, after the foundational sequence, and frequently precedes or accompanies a practicum or capstone.

Course format and workload

3 credits, 45 contact hours — lecture and applied work, three hours per week. ⚠ Neither documented institution carries a C suffix, so the computer and data work is assigned rather than scheduled — the practical hours come out of your own week.

Expect 7–10 hours per week outside class. Assessment normally centres on an outbreak investigation exercise — frequently a CDC case study worked through in stages — plus data analysis assignments, examinations, and a written investigation report.

⚠ The investigation report is the signature deliverable and it has a professional format. Read two or three MMWR outbreak reports before writing your first one; the genre is specific and learning it from examples is far faster than learning it from feedback.

⚠ What students find hardest

• Acting before the analysis is complete. Academic training rewards waiting for certainty; outbreak response does not. Recommending control measures on incomplete evidence, and being explicit about the uncertainty, is the professional skill — and it feels wrong to students the first several times.
• Case definitions. A definition too narrow misses cases; too broad admits non-cases and dilutes the association. There is no correct answer independent of the investigation's stage, and students want one.
• Choosing the design. The decision is driven by whether the population is enumerable, not by which design is methodologically superior.
• Messy data. Students expect clean datasets. Real line lists are not clean, and the cleaning is graded.
• Predictive value and prevalence. That a highly accurate test produces mostly false positives in a low-prevalence population is counter-intuitive, heavily examined, and one of the most useful things in the course.

⚠ Ethics, confidentiality and the practice/research distinction

This course handles material that is confidential in practice, and the professional standards are worth learning properly:

• Case data is identifiable health information. ⚠ Never put real case data into any tool, service or document outside the approved system — that includes AI services, personal cloud storage and email. In practice this is a HIPAA and state-law matter; in coursework it is a habit not to form.
• Public health practice and human subjects research are legally distinct. Outbreak investigation conducted under public health authority is generally not research requiring IRB review; the same data analysed to produce generalisable knowledge frequently is. The distinction is genuinely subtle and getting it wrong has consequences in both directions — investigate the boundary rather than assuming.
• Public health authority is legal authority. Reporting requirements, isolation and quarantine powers and inspection powers are grounded in statute, and their exercise involves real trade-offs with individual liberty. The field's own history — Tuskegee above all — is why consent and community engagement are now foundational.
• Naming and stigma. How an outbreak, a place or a group is described in a report affects real people, and risk communication practice has moved deliberately away from naming diseases for places or populations.

Articulation and transfer

PHC4024 is a 4000-level upper-division course, not offered at Florida College System institutions, and taken after transfer. The number is used consistently, so SCNS articulation is clean — subject to the scope difference above.

⚠ Accreditation note, repeated from the PHC4101 guide because it applies here too: CEPH accredits programmes, not courses. An accredited receiving programme may require its own version of a methods course to document coverage for accreditation, regardless of transferred credit. Ask the programme.

⚠ Prefix note. PHC is public health; HSC health sciences; STA statistics; HSA health services administration. Related numbers: PHC4101 (the foundation survey), PHC4069 (introductory epidemiology at several institutions), PHC4030, and biostatistics under STA or PHC depending on institution.

AI Integration

⚠ Epidemiology is among the public health disciplines most changed by computation, and this course is close enough to practice that the changes are worth knowing in detail.

Where it is genuinely deployed:

• Genomic epidemiology. ⚠ This is the largest real change in outbreak investigation in a generation. Whole-genome sequencing linked through networks such as PulseNet can connect cases across states that no interview would have connected, and it routinely identifies multi-state foodborne outbreaks that were previously invisible. Sequence analysis at that scale is a computational problem.
• Syndromic and event-based surveillance — automated detection of anomalies in emergency department chief complaints, laboratory feeds, wastewater signals and news streams.
• Aberration detection algorithms in routine surveillance, which flag counts exceeding expected baselines.
• Natural language processing of clinical notes for case ascertainment.
• Transmission modelling and forecasting.
• Record linkage across incomplete datasets — a persistent and underrated problem in surveillance.

⚠ The course-relevant caution is that automated detection raises rather than lowers the value of epidemiologic judgement. An aberration algorithm produces signals; most signals are artefacts — a reporting change, a new test, a data feed outage, a coding shift. Distinguishing a signal from an artefact requires knowing how the surveillance system actually works, which is exactly what this course teaches. The same applies to cluster detection: most reported disease clusters are chance, and the statistical machinery for saying so is only useful in the hands of someone who understands the multiple-comparisons problem.

Using AI tools for coursework. Models are useful for explaining a method, for writing and debugging R or Epi Info code — genuinely time-saving and now standard practice — for drafting the narrative sections of a report, and for generating practice scenarios.

⚠⚠ Where they fail, and the rules here are firmer than in most courses:

• Never put case data into an AI service. Identifiable health information, and in practice a reportable disclosure. This is not a study-habits point.
• Fabricated statistics and citations. Rates, case counts and study results are invented confidently. Use CDC WONDER, NNDSS, FLHealthCHARTS and MMWR — all free, all authoritative.
• Multi-step calculations. Stratified analyses and adjusted measures involve chains where one error invalidates everything downstream, presented with the same confidence as a correct result.
• Currency. Case definitions change — CSTE revises them — and guidance changes. A model's version may be superseded.
• False confidence. ⚠ Models produce clean conclusions where real epidemiologic evidence is mixed, which is the opposite of the professional standard. Communicating uncertainty accurately is a core competency of this field, and a tool that smooths it away is teaching the wrong habit.

Academic integrity. Read the syllabus. Submitting generated work as your own violates every Florida institution's policy, and ⚠ fabricated data in a health science context is treated more seriously than plagiarism — correctly, since the discipline's authority and its capacity to protect people rest entirely on the integrity of its numbers.