1  Introduction

1.1 Our Mission and Research Focus

Our mission is to identify, develop, and advocate for equitable ways to improve population health.

Our research falls roughly into two overlapping areas.

  1. Computational epidemiology. We use computational methods to analyze non-health data sources (mobile phone data, social media data) alongside traditional health data. The goal is to understand large-scale human behavior and its effects on health.
  2. Social epidemiology. We investigate the way the structures of society permeate all aspects of health and produce an inequitable distribution of disease and mortality. Our work aims to identify equitable policies for improving health for everybody — especially among structurally and medically vulnerable populations.

We love borrowing methods from other fields and working with exciting data, but the questions and problems drive our work. In pursuit of that mission, we draw on computational (e.g., machine learning, microsimulations), demographic (e.g., decomposition methods), epidemiologic (e.g., dynamical models), and statistical tools (e.g., Bayesian spatial models) as the research question demands. We’re a solutions-oriented lab — we want our work to change policy and improve health.

1.2 About This Manual

This manual serves as a contract between all members of the lab, a repository of institutional knowledge, and a guide for when we encounter issues. I ask all new members to read through this manual and let me know when they have. This ensures a common understanding of our policies and helps us work together effectively and fairly.1

  1. However, this manual is a living document. Life and research can be messy and I won’t be able to predict every situation. While I will do my best to adhere to this manual, and expect you to do the same, I also know there will be exceptions.↩︎