Related Experiment Video
Updated: Jun 28, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
SIR… or MADAM? The impact of privilege on careers in epidemic modelling
1MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, School of Public Health Building, Wood Lane, White City, London W12 0BZ, United Kingdom.
Abstract:
As we emerge from what may be the largest global public health crises of our lives, our community of epidemic modellers is naturally reflecting. What role can modelling play in supporting decision making during epidemics? How could we more effectively interact with policy makers? How should we design future disease surveillance systems? All crucial questions. But who is going to be addressing them in 10 years' time? With high burnout and poor attrition rates in academia, both magnified in our field by our unprecedented efforts during the pandemic, and with low wages coinciding with inflation at its highest for decades, how do we retain talent? This is a multifaceted challenge, that I argue is underpinned by privilege. In this perspective, I introduce the notion of privilege and highlight how various aspects of privilege (namely gender, ethnicity, sexual orientation, language and caring responsibilities) may affect the ability of individuals to access to and progress within academic modelling careers. I propose actions that members of the epidemic modelling research community may take to mitigate these issues and ensure we have a more diverse and equitable workforce going forward.
More Related Videos
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Related Concept Videos
Causality in Epidemiology
Steps in Outbreak Investigation
Introduction to Epidemiology
Statistical Methods for Analyzing Epidemiological Data
Confounding in Epidemiological Studies
Bias in Epidemiological Studies