Evidence map›Paper›PMID 33307443›Full record

ReviewEpidemics2020

Responsible modelling: Unit testing for infectious disease epidemiology.

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

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Epidemics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Reproducibility in Biomedical Research: A Systems Prescription.Journal of general internal medicine · 2026
    Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Engagement and adherence trade-offs for SARS-CoV-2 contact tracing.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021
    Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tim C D LucasBig 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.
Timothy M PollingtonBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK. MathSys CDT, University of Warwick, UK.
Emma L DavisBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK.
T Déirdre HollingsworthBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Models, BiologicalCommunicable DiseasesComputer SimulationCOVID-19HumansPandemicsReinfectionSoftwareComputational modelsReproducible scienceSoftware developmentUnit testing

Identifiers

PMID33307443
PMCPMC7690327

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.