Responsible modelling: Unit testing for infectious disease epidemiology

Tim C D Lucas; Timothy M Pollington; Emma L Davis; T Déirdre Hollingsworth

doi:10.1016/j.epidem.2020.100425

Responsible modelling: Unit testing for infectious disease epidemiology

Epidemics. 2020 Dec:33:100425. doi: 10.1016/j.epidem.2020.100425. Epub 2020 Nov 26.

Authors

Tim C D Lucas¹, Timothy M Pollington², Emma L Davis³, T Déirdre Hollingsworth³

Affiliations

¹ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK. Centre for Environment and Health, School of Public Health, Imperial College, UK. Electronic address: timcdlucas@gmail.com.
² Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK. MathSys CDT, University of Warwick, UK.
³ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK.

Abstract

Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field.

Keywords: Computational models; Reproducible science; Software development; Unit testing.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

COVID-19 / epidemiology
Communicable Diseases / epidemiology*
Computer Simulation
Humans
Models, Biological*
Pandemics
Reinfection / epidemiology
Software