Introduction to Public Health Surveillance

Course description

Surveillance is one of the fundamental activities of public health organizations and is critical for understanding disease burden, impacts of interventions, and the detection of unusual events. This course will begin with an overview of surveillance in different contexts. Several guest lecturers will address the question “What is surveillance” and will discuss challenges in performing surveillance in different settings and data that are available for use. The rest of the semester will be broken into 2 sections: “Aberration Detection” and “Monitoring Interventions”. Throughout the semester, we will discuss analytical methods used with surveillance data and will have “hands-on” practical data analysis workshops and associated homework assignments. Throughout the course, there will be a focus on the critical evaluation of surveillance data from different sources. We will use a mix of lectures, discussion, guided data analyses, and in-class exercises to analyze and interpret a number of “real world” surveillance dataset from different sources.

Methods modules will be structured as follows: we will have a lecture to introduce the topic. We will then go through some worked examples as a group, going through the code in detail. We will then have a session where you go through in groups and complete an analysis from end-to-end in class, with the instructors circulating to help troubleshoot coding and analysis issues. You will then have a take-home problems on the same topic that you will complete individually.

Several statistical packages, including R and SATSCAN will be used in hands-on demonstrations and homework assignments. We will use the RStudio.Cloud platform, which is a web-based version of RStudio. You are also welcome to run the code locally on your computer or on myapps.yale.edu. All code can be downloaded from Github https://github.com/YalePublicHealthSurveillance

My teaching philosophy

As graduate students in a professional training program, the goal is to arm you with tools that you need to be successful practitioners. The best (and only) way to be able to implement these tools is through hands on experience and data analysis. Wherever possible, we will analyze real world datasets, with warts and all, when learning new data analysis methods. With any type of data analysis, there are several pieces that need to be mastered: understanding the motivations underlying the analysis, understanding the analysis approach itself (and its limitations), and being able to implement the mechanics of analysis. We will have some didactic lectures to introduce topics and address these first two pieces. But to fully master new techniques, it is important to get your hands dirty, try some analyses, and understand what different pieces of code are doing.

Prerequisites

  • Previous experience with coding in R (ie intro stats, AAME). However, those without previous experience in R are welcome to take the class, and the computational instruction is geared towards novices.

  • Basic familiarity with statistical principles (including linear and Poisson regression) is highly recommended.

Course learning objectives

After successfully completing this course, you should be able to:

1) Define the strengths and weaknesses of surveillance systems frequently used by public health agencies and researchers

2) Identify common sources of data used in surveillance activities

3) Critically analyze data from surveillance systems with statistical tools

4) Communicate the results of surveillance studies along with appropriate caveats

Instructors

Main Instructor: Dan Weinberger, PhD, Associate Professor of Epidemiology (EMD)

TF: Elisabeth (Zoe) Nelson, MPH: PhD student (EMD)

Guest lectures

Stepanie Perniciaro, Yale Emerging Infections Program.

Don Olson, Formerly New York City Health Department of Health and Mental Hygiene

Dr. Nathan Grubaugh, YSPH/EMD

Dr. Kayoko Shioda, Boston University School of Public Health

Components of grades

  • Assignments 5x: 90% of grade (1,2,34 worth 20% each; assignment #5 worth 15%)

  • Attendance and participation (5%)

Course textbook

There is no textbook for the course. The final part of the course on evaluating interventions will closely follow the material developed for a draft WHO guidance document on the use of administrative data for evaluating the impact of pneumococcal conjugate vaccines. That text can be accessed here.

Readings will be posted to the Yale Canvas website. Readings will consist of a mix of journal articles, MMWR reports, and book chapters. Good additional references include

Infectious disease surveillance by M’ikanatha, Lynfield, Van Beneden, and de Valk;

The Handbook of Biosurveillance (available for free and online through the Yale library),

Spatial and Syndromic Suveillance for Public Health by Lawson and Kleinman.

Getting started in Rstudio.cloud

For novices:

  • Create a free account on https://rstudio.cloud This is a web-based version of RStudio, with all packages, data, and code pre-loaded

  • Step 2: Join the Public Health Surveillance Course workspace by clicking this link

  • Step 3: Access the Public Health Surveillance Course Workspace

    1. Open a project by clicking on its name. For example: ‘Session-0-Intro-to-R’

For advanced users:

  • Run the workshop materials on your local machine on RStudio. All workshop materials are provided as a set of RProjects, which can be access from https://github.com/orgs/VaccineEvaluationWorkshop

  • You can either directly download and then open the projects on your computer by clicking the .rproj file in the corresponding directory, or you can download via Git, if installed on your computer

Academic Integrity

“Academic integrity is a core institutional value at Yale. It means, among other things, truth in presentation, diligence and precision in citing works and ideas we have used, and acknowledging our collaborations with others. In view of our commitment to maintaining the highest standards of academic integrity, the Graduate School Code of Conduct specifically prohibits the following forms of behavior: cheating on examinations, problem sets and all other forms of assessment; falsification and/or fabrication of data; plagiarism, that is, the failure in a dissertation, essay or other written exercise to acknowledge ideas, research, or language taken from others; and multiple submission of the same work without obtaining explicit written permission from both instructors before the material is submitted. Students found guilty of violations of academic integrity are subject to one or more of the following penalties: written reprimand, probation, suspension (noted on a student’s transcript) or dismissal (noted on a student’s transcript).