Leonelli, Sabina (2023) Is Data Science Transforming Biomedical Research? Evidence, Expertise and Experiments in COVID-19 Science. [Preprint]

Text COVID_Impact_Medicine_Final_RevisedAugust2023.docx Download (649kB)

Abstract

Biomedical deployments of data science capitalise on vast, heterogeneous data sources. This promotes a diversified understanding of what counts as evidence for health-related interventions, beyond the strictures associated with evidence-based medicine. Focusing on COVID-19 transmission and prevention research, I consider the epistemic implications of this diversification of evidence in relation to: (1) experimental design, especially the revival of natural experiments as sources of reliable epidemiological knowledge; and (2) modelling practices, particularly the recognition of transdisciplinary expertise as crucial to developing and interpreting data models. Acknowledging such shifts in evidential, experimental and modelling practices helps avoid harmful applications of data-intensive methods.

---

Export/Citation:

EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL

Social Networking:

Share |

---

Item Type:	Preprint
Creators:	Creators Email ORCID Leonelli, Sabina s.leonelli@exeter.ac.uk 0000-0002-7815-6609
Keywords:	Data; medicine; evidence; COVID-19; epidemiology; natural experiments; experimental design; data models.
Subjects:	General Issues > Data Specific Sciences > Medicine > Clinical Trials Specific Sciences > Medicine > Epidemiology General Issues > Evidence General Issues > Experimentation Specific Sciences > Medicine General Issues > Social Epistemology of Science
Depositing User:	Sabina Leonelli
Date Deposited:	25 Aug 2023 13:58
Last Modified:	25 Aug 2023 13:58
Item ID:	22450
Subjects:	General Issues > Data Specific Sciences > Medicine > Clinical Trials Specific Sciences > Medicine > Epidemiology General Issues > Evidence General Issues > Experimentation Specific Sciences > Medicine General Issues > Social Epistemology of Science
Date:	24 August 2023
URI:	https://philsci-archive.pitt.edu/id/eprint/22450

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item