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.
Highlights
COVID-19 was first reported on 31st December 2019 as a pneumonia of unknown aetiology that was observed in the Chinese province of Hubei.1 The first cluster was identified in the proximities of the Wuhan market, which was closed for disinfection on the 1st of January 2020
We argue that early Institute for Health Metrics and Evaluation (IHME) predictions generated a specific type of understanding—which we call descriptive understanding, or DESC—of the relationship between certain restrictions and the evolution of the infection rate
In the case of the IHME model, this was feasible, following the dynamics we have described, even in the early statistical versions published during March and April
Summary
COVID-19 was first reported on 31st December 2019 as a pneumonia of unknown aetiology that was observed in the Chinese province of Hubei. The first cluster was identified in the proximities of the Wuhan market, which was closed for disinfection on the 1st of January 2020. 107).This assertion is essential, for it proves that in de Regt’s conception of scientific understanding, scientific explanations always play a mediating role between mere intelligibility and predictions This is analogous to Douglas’ idea, whom de Regt appeals to when articulating the nature of this relationship: “the relation between explanation and prediction is a tight, functional one: explanations provide the cognitive path to predictions, which serve to test and refine the explanations” Douglas The problem is that de Regt’s theory assumes, correctly in our view, a circularity or, as we prefer to express it, a dialectical relationship that develops with time How this relationship develops, and whether it necessarily requires an explanation in between intelligibility and prediction, is precisely the question we are investigating in this paper. We show an example of such a possibility, spelling out the details of the relationship, including the cognitive path between understanding and prediction, in the remainder of this paper (Sects. 4, 5)
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