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Descriptive Understanding and Prediction in COVID-19 Modelling

Findl, Johannes and Suárez, Javier (2021) Descriptive Understanding and Prediction in COVID-19 Modelling. [Preprint]

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Abstract

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical question about how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Findl, Johannes
Suárez, Javierjavier.suarez@uj.edu.pl0000-0001-5851-2277
Keywords: SARS-CoV-2 Description Scientific explanation Epidemiological modelling Statistical modelling
Subjects: General Issues > Computer Simulation
Specific Sciences > Medicine > Epidemiology
General Issues > Explanation
General Issues > Models and Idealization
Depositing User: Dr Javier Suárez
Date Deposited: 21 Sep 2021 02:55
Last Modified: 21 Sep 2021 02:55
Item ID: 19580
Journal or Publication Title: History and Philosophy of the Life Sciences
Publisher: Springer
DOI or Unique Handle: 10.1007/s40656-021-00461-z
Subjects: General Issues > Computer Simulation
Specific Sciences > Medicine > Epidemiology
General Issues > Explanation
General Issues > Models and Idealization
Date: September 2021
URI: https://philsci-archive.pitt.edu/id/eprint/19580

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