Covid-19 and the epistemology of epidemiological models at the dawn of AI
Bibliographic Data
| ID | 14679345 |
|---|---|
| Authors | George T H Ellison (0000-0001-8914-6812, University of Lancashire, corresponding author) |
| Year | 2020 |
| Volume | 47 |
| Issue | 6 |
| Pages | 506-513 |
| Publication date | 2020-08-17 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Annals of Human Biology (JOURNAL) |
| Journal identifiers | ISSN: 0301-4460 • E-ISSN: 1464-5033 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/03014460.2020.1839132 |
| PMID | 33228409 |
| OpenAlex | W3048050216 |
| Language | EN |
| Citations received | 1 |
| References cited | 75 |
adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at 'mediation analysis'). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to 'data-driven' modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks
Causal inference · Confounding · Econometrics · Extrapolation · Inference · Statistical inference · Statistics · Computer Science · COVID-19 diagnosis using AI · COVID-19 epidemiological studies · Mathematics · SARS-CoV-2 and COVID-19 Research · Artificial Intelligence
Mediation analysis in epidemiology
Projecting the transmission dynamics of Sars-CoV-2 through the postpandemic period
Coronavirus Infections—More Than Just the Common Cold
The Table 2 Fallacy
Overadjustment Bias and Unnecessary Adjustment in Epidemiologic Studies
Prediction models for diagnosis and prognosis of covid-19
Illustrating bias due to conditioning on a collider
Numeracy, ratio bias, and denominator neglect in judgments of risk and probability
The spread of awareness and its impact on epidemic outbreaks
Response to Covid-19 in Taiwan
A contribution to the mathematical theory of epidemics
Science and Statistics
Factors associated with Covid-19-related death using OpenSafely
Prediction of Number of Cases of 2019 Novel Coronavirus (Covid-19) Using Social Media Search Index
Causation and prediction in epidemiology
Disease and Diversity in History
Basic Methods for Sensitivity Analysis of Biases
The myth and fallacy of simple extrapolation in medicine
Interventions and Causal Inference
| Citations per year | 0.2 |
|---|---|
| Citation span | 2021 - 2021 (1) |
| Highly cited | No |