Epidemiology’s Time of Need : COVID-19 Calls for Epidemic-Related Economics - American Economic Association
▻https://www.aeaweb.org/articles?id=10.1257/jep.34.4.105
The COVID-19 pandemic has catapulted scientific conversations and scientific divisions into the public consciousness. Epidemiology and economics have long operated in distinct silos, but the COVID-19 pandemic presents a complex and cross-disciplinary problem that impacts all facets of society. Many economists have recognized this and want to engage in efforts to mitigate and control the pandemic, but others seem more interested in attacking epidemiology than attacking the virus. As an epidemiologist, I call upon economists to join with us in combating COVID-19 and in preventing future pandemics. In this essay, I attempt to provide some insight for economists into how epidemiology works, where it doesn’t work, and the much-needed answers that economists can help us obtain. I hope this will spur economists towards an epidemic-related economics that can provide a blueprint for a healthy economy and population.
The principles of epidemic dynamics, prevention, and elimination are well-established and have been tested in disease outbreaks, large and small, as well as in computational models and laboratory experiments. There is no more reason for economists to jump into the production of epidemiology models than there is for them to become atmospheric scientists
À noter un intéressant passage sur la distinction entre « applied epidemiology » et « academic epidemiology », sur la question des masques.
sur les épidémiologistes en chambre (comme vous et moi) :
Many early attempts by non-epidemiologists (or epidemiologists with no experience in infectious diseases) to understand or predict COVID-19 went wrong when analysts either assumed that initial data would continue to describe the changes in disease spread over time, or that initial data could only be biased in one direction
et sur les modèles « worst case » dont on a entendu parler au début :
When critics argue over what high-profile epidemiology models “got wrong” about the COVID-19 pandemic, their analysis presupposes that the goal of these models was to predict, with both validity and accuracy, the actual total number of cases and deaths expected throughout the course of the pandemic under actual pandemic responses at both the individual and governmental levels. It is absolutely the case that both the high-profile Imperial College (Ferguson et al. 2020) and Institute for Health Metrics and Evaluation (IHME) models (Murray 2020) as well as all other current models, fell well short of this lofty goal; this is to be expected because it was not the intended goal of these models. (...)
But to put these concerns in real-world context, no infectious disease modeler expects to be able to accurately forecast the future based on sparse data from early in a pandemic. Even “nowcasting,” the task of modeling the current number of true infections, is extremely challenging, especially early in a pandemic. Asking an infectious disease modeler to predict the exact trajectory of an outbreak is akin to asking an economist to select stocks for your portfolio or a climate scientist to predict the best day in 2022 for an outdoor wedding.