Findl, Johannes and Suárez, Javier (2021) Descriptive Understanding and Prediction in COVID-19 Modelling. [Preprint]
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Findl Suarez SUBIR COVID-19 model 26-07.docx Download (1MB) |
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 | |||||||||
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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 |
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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 |
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Date: | September 2021 | |||||||||
URI: | https://philsci-archive.pitt.edu/id/eprint/19580 |
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