--- title: "Appendix P: Course Syllabus Template – The Public Health AI Handbook" source: "https://publichealthaihandbook.com/appendices/course-syllabus.html" author: "Bryan Tegomoh, MD, MPH" last_updated: "2026-10-30" doi: "10.5281/zenodo.18263441" license: CC BY 4.0 generated-by: handbook-markdown-export --- # Appendix P: Course Syllabus Template This syllabus template is designed for MPH programs, epidemiology training, health department professional development, and related programs. Faculty may adapt the structure, readings, and assignments to their specific context and audience. Using This Template - **12-week format**: Standard graduate semester structure - **6-week intensive format**: Combine weeks as indicated for accelerated programs - **Modular use**: Individual weeks can be extracted for focused workshops - **Health department adaptation**: See notes for practice-focused versions - **Shorter formats**: Four- and eight-session tracks, CEPH and Core Competencies maps, journal club questions for every chapter, and assessment items are in [Teaching with the Public Health AI Handbook](../teaching.html) ------------------------------------------------------------------------ ## Course Overview **Course Title:** AI for Public Health Practice: From Surveillance to Implementation **Level:** Graduate seminar (MPH, DrPH, PhD) or professional development for health departments **Prerequisites:** Basic epidemiology and biostatistics recommended. No prior AI knowledge required. **Format:** One 2-hour seminar per week (24 contact hours over 12 weeks), with about 4 hours of reading and assignment work outside class. Set the credit value under the institution’s credit-hour policy. **Primary Text:** Tegomoh, B. (2025). *The Public Health AI Handbook: Evaluating AI Tools for Public Health Practice*. DOI: [10.5281/zenodo.18263442](https://doi.org/10.5281/zenodo.18263442) **Learning Objectives:** By the end of this course, students will be able to: 1. Explain core AI/ML concepts in public health terminology 2. Evaluate AI tools for disease surveillance and outbreak detection 3. Assess epidemic forecasting models and their limitations 4. Apply genomic surveillance AI to pathogen tracking 5. Identify deployment challenges specific to public health infrastructure 6. Analyze ethical and equity implications of population-level AI 7. Design implementation strategies appropriate for health department contexts ------------------------------------------------------------------------ ## 12-Week Syllabus ### Week 1: Why Most AI Projects Fail in Public Health **Theme:** Historical context and failure patterns **Required Reading:** - [History of AI in Public Health](../foundations/history.html) - [Preface](../preface.html) **Learning Objectives:** - Trace AI development in public health from early expert systems to modern ML - Explain why Google Flu Trends worked, then failed - Identify recurring patterns in public health AI failures **Discussion Questions:** 1. Google Flu Trends initially produced influenza estimates ahead of CDC reports. What went wrong? 2. What distinguishes public health AI from clinical AI? 3. Why are public health practitioners often skeptical of AI hype? ------------------------------------------------------------------------ ### Week 2: Machine Learning Fundamentals for Epidemiologists **Theme:** AI concepts translated for public health audiences **Required Reading:** - [Machine Learning Fundamentals](../foundations/ai-basics.html) **Learning Objectives:** - Distinguish supervised learning, unsupervised learning, and reinforcement learning - Explain overfitting in epidemiological terms - Connect ML concepts to familiar statistical methods **Discussion Questions:** 1. How is an ML classification model similar to and different from logistic regression? 2. When would you use supervised vs. unsupervised learning in surveillance? 3. What is the epidemiological equivalent of overfitting? ------------------------------------------------------------------------ ### Week 3: The Data Problem in Public Health AI **Theme:** Why public health data makes AI hard **Required Reading:** - [The Data Problem in Public Health AI](../foundations/data.html) **Learning Objectives:** - Identify data quality issues specific to public health (reporting lags, inconsistent standards) - Analyze how data limitations constrain AI applications - Assess generalizability concerns for population-level AI **Discussion Questions:** 1. A vendor trained their model on hospital data. What problems arise for health department use? 2. How do reporting delays affect real-time surveillance AI? 3. What data infrastructure investments would most improve AI readiness? **Assignment:** Data audit (1,500 words): Assess the AI readiness of a public health dataset you have access to ------------------------------------------------------------------------ ### Week 4: AI for Disease Surveillance and Outbreak Detection **Theme:** Current applications and evidence **Required Reading:** - [AI in Disease Surveillance and Outbreak Detection](../applications/surveillance.html) **Learning Objectives:** - Evaluate syndromic surveillance AI systems - Analyze wastewater surveillance AI applications - Assess early warning systems and their performance **Discussion Questions:** 1. How should AI outbreak detection integrate with traditional surveillance? 2. What is the cost of false positives in automated outbreak detection? 3. How did COVID-19 change surveillance AI applications? ------------------------------------------------------------------------ ### Week 5: Epidemic Forecasting with AI **Theme:** Promises and limitations of predictive models **Required Reading:** - [Epidemic Forecasting with AI](../applications/forecasting.html) - [The AI Morgue: Failure Post-Mortems](../appendices/failures.html): COVID-19 forecasting failures **Learning Objectives:** - Evaluate epidemic forecasting model types (mechanistic, statistical, ML, ensemble) - Analyze why COVID-19 forecasting performed poorly in 2020 - Apply appropriate skepticism to forecasting claims **Discussion Questions:** 1. Why is epidemic forecasting fundamentally harder than weather forecasting? 2. What did the COVID-19 Forecast Hub show about ensemble models? 3. How should decision-makers use uncertain forecasts? ------------------------------------------------------------------------ ### Week 6: Genomic Surveillance and Pathogen Analysis **Theme:** AI for sequencing data and variant tracking **Required Reading:** - [AI in Genomic Surveillance and Pathogen Analysis](../applications/genomics.html) **Learning Objectives:** - Explain how AI assists variant classification and lineage assignment - Evaluate phylogenetic inference AI tools - Assess antimicrobial resistance prediction applications **Discussion Questions:** 1. How did AI support SARS-CoV-2 variant tracking? 2. What are the limitations of AI for predicting variant characteristics? 3. How should health departments integrate genomic AI into routine surveillance? **Midterm:** Take-home exam covering Weeks 1–6 ------------------------------------------------------------------------ ### Week 7: Clinical Decision Support and Diagnostic AI **Theme:** Population health implications of clinical AI **Required Reading:** - [AI in Diagnostic and Clinical Decision Support](../applications/clinical.html) **Learning Objectives:** - Analyze population-level impacts of clinical AI deployment - Evaluate screening AI (diabetic retinopathy, cancer) from a public health perspective - Assess implications for health equity at population scale **Discussion Questions:** 1. How does clinical AI deployment affect population health metrics? 2. What is the public health role in clinical AI oversight? 3. Should health departments influence clinical AI adoption? How? ------------------------------------------------------------------------ ### Week 8: AI for Substance Use and Social Determinants **Theme:** AI addressing upstream factors **Required Reading:** - [AI for Substance Use and Overdose Prevention](../applications/substance-use.html) **Learning Objectives:** - Evaluate AI applications for overdose prediction and prevention - Analyze ethical considerations in predictive models targeting vulnerable populations - Assess AI for social determinants of health **Discussion Questions:** 1. When does predictive risk modeling help vs. harm vulnerable populations? 2. How should overdose prediction models be deployed? By whom? 3. What safeguards prevent AI from encoding discrimination? ------------------------------------------------------------------------ ### Week 9: Evaluating AI Systems for Public Health **Theme:** Practical evaluation frameworks **Required Reading:** - [Evaluating AI in Public Health Practice](../implementation/evaluation.html) - [AI Vendor Evaluation Checklist](../appendices/vendor-evaluation.html) **Learning Objectives:** - Apply evaluation frameworks specific to public health contexts - Identify red flags in vendor marketing and validation claims - Design validation studies appropriate for health department resources **Discussion Questions:** 1. How should health departments evaluate AI they cannot replicate internally? 2. What validation should vendors provide before public health deployment? 3. How do you evaluate AI when you do not have data science staff? **Assignment:** Vendor evaluation (2,000 words): Critically evaluate an AI product marketed to health departments ------------------------------------------------------------------------ ### Week 10: Ethics, Equity, and Privacy **Theme:** Values and risks in population-level AI **Required Reading:** - [Ethics, Bias, and Equity in Healthcare AI](../implementation/ethics.html) - [Privacy, Security, and Governance for Health AI](../implementation/privacy.html) **Learning Objectives:** - Analyze algorithmic bias in population health AI - Apply ethical frameworks to surveillance AI decisions - Address privacy considerations in public health AI **Discussion Questions:** 1. How does algorithmic bias manifest differently in public health vs. clinical AI? 2. When is surveillance AI an appropriate public health intervention? 3. How should public health balance AI capability with privacy protection? ------------------------------------------------------------------------ ### Week 11: Why Most Prototypes Fail in Health Departments **Theme:** Implementation challenges specific to public health **Required Reading:** - [AI Deployment in Healthcare: Why Most Prototypes Fail](../implementation/deployment.html) - [AI Safety in Healthcare: Protecting Patients and Populations](../implementation/safety.html) **Learning Objectives:** - Identify infrastructure barriers in health departments - Analyze why academic pilots often fail to scale - Design implementation strategies for resource-constrained settings **Discussion Questions:** 1. Why do AI tools that work in research settings fail in health departments? 2. What infrastructure investments enable AI adoption? 3. How should health departments approach AI when they lack technical staff? ------------------------------------------------------------------------ ### Week 12: Future Directions and Career Pathways **Theme:** Emerging technologies and professional development **Required Reading:** - [Emerging AI Technologies for Public Health](../future/emerging.html) - [Large Language Models in Public Health: Theory and Practice](../future/llm-theory-practice.html) - [Career Guide: Pathways in Public Health AI](../appendices/career-guide.html) **Learning Objectives:** - Evaluate emerging AI technologies (LLMs, multimodal AI) - Analyze how AI might transform public health practice - Identify career pathways in public health AI **Discussion Questions:** 1. How should public health practitioners prepare for AI-augmented practice? 2. Which LLM applications are appropriate for public health, and which are not? 3. What skills will public health professionals need in 5–10 years? **Final Assignment:** Implementation proposal or policy brief (3,000 words) ------------------------------------------------------------------------ ## Assessment Structure | Component | Weight | Due | |----------------------------|--------|---------| | Class participation | 15% | Ongoing | | Data audit (Week 3) | 15% | Week 3 | | Midterm exam | 20% | Week 6 | | Vendor evaluation (Week 9) | 20% | Week 9 | | Final proposal/brief | 30% | Week 12 | ## Grading Rubric One rubric covers the three written assignments. Weight its rows to the assignment: the data audit rests on evidence appraisal, the vendor evaluation on evidence appraisal and source verification, and the final proposal or brief on all five. | Criterion | Exceeds expectations | Meets expectations | Below expectations | |----|----|----|----| | **Evidence appraisal** | Names the data source, population, comparator, and outcome; separates retrospective validation from prospective use; states what the evidence cannot show | Names the evaluation design and its main limitations | Treats reported accuracy as proof of population benefit | | **Source verification** | Traces every factual claim to a primary source, such as a peer-reviewed study, an MMWR report, or agency documentation; labels vendor figures as vendor-reported | Cites primary sources for most claims and labels vendor figures | Relies on press releases, vendor materials, or AI-generated summaries without verification | | **Equity and population impact** | Names the populations the tool could miss or burden, the subgroup evidence that exists, and how disparities would be detected after deployment | Identifies the main equity concerns | Omits equity, or asserts it without evidence | | **Health department implementation** | Specifies data agreements, staffing, workflow, and how performance is monitored after launch | Describes the workflow and a monitoring plan | Assumes adoption without describing who runs the tool or how | | **Communication** | Clear to the stated audience of agency leaders, partners, or the public; states uncertainty plainly; the recommendation follows from the evidence | Clear, with minor gaps in stating limits | Unclear or overstated, or the recommendation does not follow from the evidence | Participation is assessed on preparation and on contributions that use evidence, not on volume. ## Learner Use of Generative AI Adopt a written policy before the first session and align it with the institution’s academic-integrity, privacy, and data-use rules. A default policy for this course: - Learners may use generative AI tools to search, brainstorm, and edit. They disclose each use at the end of the assignment: the tool, the task, and how the output was checked. - Learners verify every citation, statistic, and policy claim against its primary source. An unverified or fabricated citation is graded under source verification, whatever produced it. - Surveillance, case, and other identifiable data stay out of any tool the agency or university has not approved for that data. The Week 3 data audit uses public, de-identified, or synthetic data unless a data-use agreement covers the coursework. - The instructor states which assessments exclude AI assistance and how that is checked. A take-home exam cannot rely on a prohibition alone. - Course requirements use only tools the institution provides to every learner. - Learners remain responsible for everything they submit. One exercise can ask learners to verify and correct an AI-drafted summary of a published surveillance or forecasting study, which practices the verification this policy requires. ## Alignment With CEPH Competencies Accredited MPH programs must assess every student on each of CEPH’s 22 foundational competencies, through specific assessment activities the program documents ([CEPH, 2024](https://media.ceph.org/documents/2024_Criteria.D2.pdf)). The assignments below can serve as those activities when the program records them in the competency matrix of its self-study. The program decides whether to adopt them, and group work needs a method for assessing each student individually. | CEPH foundational competency | Assignment that can assess it | |----|----| | 3\. Analyze quantitative and qualitative data using biostatistics, informatics, computer-based programming, and software, as appropriate | Data audit (Week 3), when it analyzes the dataset’s completeness, timeliness, and representativeness | | 4\. Interpret results of data analysis for public health research, policy, or practice | Vendor evaluation (Week 9), interpreting a vendor’s validation results for a health department decision | | 6\. Discuss the means by which structural bias, social inequities, and racism undermine health and create challenges to achieving health equity at organizational, community, and systemic levels | Final proposal or brief, with an equity analysis of the proposed AI tool | | 11\. Select methods to evaluate public health programs | Final proposal, with an evaluation plan for the AI deployment | | 15\. Evaluate policies for their impact on public health and health equity | Final policy brief | | 19\. Communicate audience-appropriate public health content, both in writing and through oral presentation to a non-academic, non-peer audience with attention to factors such as literacy and health literacy | Final brief written for health department leaders or a community audience, with a short oral briefing | | 22\. Apply a systems thinking tool to visually represent a public health issue in a format other than standard narrative | Final proposal, with a causal loop diagram or system map of how the AI tool would change surveillance or response; CEPH states that logic models and evidence tables are not sufficient to address this competency | For the ASPPH AI framework for schools and programs of public health, see [AI Governance in Public Health: Regulation and Oversight](../future/policy.html#sec-cdc-ai-strategy-public-health). ## Evaluating the Course Plan the evaluation before the course starts: - **Learner feedback:** a short mid-course check at Week 6 and an end-of-course survey on relevance, workload, and readings. - **Learning:** two comparable published surveillance or forecasting studies, one appraised in Week 1 and one in Week 12, scored with the evidence-appraisal and source-verification rows of the rubric. The change between them measures what the course taught better than a final exam score alone. - **Application:** a follow-up three to six months later asking whether learners have evaluated, questioned, or declined an AI tool in their agency or research. - **Currency:** an annual review of the readings against the handbook’s [Changelog](../changelog.html), because agency guidance, product evidence, and data standards change within an academic year. ------------------------------------------------------------------------ ## 6-Week Intensive Format For professional development programs, combine weeks as follows: | Intensive Week | Standard Weeks | Focus | |----|----|----| | 1 | 1–2 | History, failures, ML fundamentals | | 2 | 3–4 | Data challenges, surveillance AI | | 3 | 5–6 | Forecasting, genomic surveillance | | 4 | 7–8 | Clinical AI, substance use, social determinants | | 5 | 9–10 | Evaluation, ethics, privacy | | 6 | 11–12 | Implementation, futures, careers | ------------------------------------------------------------------------ ## Supplementary Reading Lists ### Public Health AI Foundations - Rajkomar, A., Dean, J., & Kohane, I. (2019). [Machine Learning in Medicine](https://www.nejm.org/doi/full/10.1056/NEJMra1814259). NEJM, 380(14), 1347-1358. - Wiens, J. et al. (2019). [Do no harm: A roadmap for responsible machine learning for health care](https://www.nature.com/articles/s41591-019-0548-6). Nature Medicine, 25(9), 1337-1340. ### Surveillance and Forecasting - Mooney, S.J. & Pejaver, V. (2018). [Big Data in Public Health: Terminology, Machine Learning, and Privacy](https://www.annualreviews.org/content/journals/10.1146/annurev-publhealth-040617-014208). Annual Review of Public Health, 39, 95-112. - Reich, N.G. et al. (2019). [Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007486). PLOS Computational Biology, 15(11), e1007486. ### Implementation and Policy - Shaw, J. et al. (2019). [Artificial Intelligence and the Implementation Challenge](https://www.jmir.org/2019/7/e13659). Journal of Medical Internet Research, 21(7), e13659. - World Health Organization (2021). [Ethics and governance of artificial intelligence for health](https://www.who.int/publications/i/item/9789240029200). WHO Guidance. ------------------------------------------------------------------------ ## Adaptation Notes for Instructors Co-teaching helps: informatics or data science faculty for Weeks 2 and 3, and health department practitioners for Weeks 9 and 11. **For MPH programs:** Use the CEPH competency map above to record which assignments assess which foundational competencies. Emphasize Weeks 3–6 (surveillance applications) for students focusing on epidemiology. **For DrPH/PhD programs:** Expand technical content in Weeks 2 and 5–6. Add methods-focused assignments requiring students to implement simple surveillance AI. **For health department training:** Compress to 6-week format. Emphasize practical evaluation (Week 9) and implementation (Week 11). Add hands-on workshops with tools actually used in your jurisdiction. **For informatics programs:** Expand technical content throughout. Add coding exercises and model development assignments. ------------------------------------------------------------------------ ## License This syllabus template is released under the same CC BY 4.0 license as The Public Health AI Handbook. Faculty may adapt freely with attribution.