The Infectious Diseases fellowship offers a global-health specialty certificate, and IDEEEP runs Field Epidemiology and Tropical Medicine in Peru, but there is no module on the data and surveillance skills global-health work depends on. This module adds that quantitative surveillance layer: diagnostics interpretation, surveillance indicators that account for reporting delay, and the design of data systems in low-resource settings. It is a short module that appends to the existing global-health certificate and pairs with the field-epidemiology course.

Draft proposal. This is an early proposal for a bolt-on certificate module. Course numbers, credit hours, and the mapping onto the Infectious Diseases fellowship global-health specialty certificate are placeholders and will be confirmed with the fellowship program before the module is offered.


Overview

Global-health surveillance depends on skills that are rarely taught together: reading a diagnostic test and its performance characteristics, building indicators that correct for reporting delay and censoring, and designing data systems that work where resources are limited. This module brings those skills into one place and connects them to the applied field experience in Peru.

Who it is for

Learners are expected to have introductory statistics and, ideally, to be taking or to have taken the field-epidemiology course.

Structure and credits

This is a short bolt-on module of roughly 2 to 3 credits appended to the global-health specialty certificate. Delivery is modular and part-time compatible, and the module is designed to sit alongside the field-epidemiology experience.

ComponentFocus
Diagnostics and test performanceField and laboratory methods, sensitivity and specificity
Surveillance indicatorsCase definitions, reporting delay, censoring
Low-resource data systemsData quality and One Health surveillance design

Exact credit hours will be set with the Department of Biology and the fellowship program.

Learning outcomes

On completing the module, learners will be able to:

Curriculum and modules

The module draws on the diagnostics hub, its individual method pages, and the epidemiology concept pages. A planned page on surveillance in low-resource settings is being developed in parallel with the Applied ID Epidemiology course and is referred to here in plain text; it will be cross-linked rather than duplicated.

ComponentUnderlying courseSite resources
Diagnostics and test performanceField Epidemiology and Tropical MedicineDiagnostics and Surveillance hub, qPCR, ELISA, LAMP, Rapid antigen tests, Diagnostic testing
Surveillance indicatorsField Epidemiology and Tropical MedicineDelay distributions and censoring, Epidemiological intervals
Low-resource data systemsField Epidemiology and Tropical MedicinePlanned page on surveillance in low-resource settings; One Health and zoonoses

Relationship to existing Wake Forest certificates

Wake Forest’s Infectious Diseases fellowship already offers specialty certificates in antimicrobial stewardship, global health, patient safety, and translational sciences. This module is a bolt-on to the global-health specialty certificate. It adds a quantitative surveillance layer to that credential rather than standing alone. The exact credit and enrollment mapping is @placeholder and will be confirmed with the fellowship program.

Site resources

The module is powered by existing pages: the Diagnostics and Surveillance hub and its method pages (qPCR, ELISA, LAMP, Rapid antigen tests), Diagnostic testing, and Delay distributions and censoring. See the Field Epidemiology and Tropical Medicine course for the applied companion experience and the Programs page for how these fit the wider concentration.

Admission and how to enroll

Prospective learners should contact the program to discuss fit and sequencing.

Artificial intelligence and academic integrity

Large language models such as ChatGPT, Claude, and Gemini are now part of academic and professional work, and learners are encouraged to use them to support their learning. If you use these tools, cite them and describe how you used them. They supplement your work; they do not replace your own understanding, and using them without attribution is plagiarism and will be treated as such.

Proposal change notice

This module proposal and the details herein are subject to change.