How COVID-19 models differed when they were built by economists versus epidemiologists – a healthcare economist

With the outbreak of the COVID-19 pandemic, policy makers have looked to researchers to predict the future direction of the pandemic as well as examine how different policy choices will affect society. Both epidemiologists and economists have risen to the challenge, creating a variety of models to aid policy decisions. How do the models developed by economists and epidemiologists differ? How do they complement each other? These are the topics of a recent paper written by Darden and others. (2022).

The first element to note is that the goals of models often differ.

Epidemiological models generally emphasize understanding of health outcomes; Thus, behavioral shifts and their drivers are so important that they affect overall health. By contrast, economic models generally emphasize health-wealth trade-offs; Thus, understanding the economic effects and drivers of behavior change is often seen as a primary goal of the model rather than as a ‘means to an end’ of understanding health outcomes.

The authors note that economic models often do not take into account appropriate nuances about disease and make strong and/or unrealistic assumptions about disease dynamics and transmission. Many economic models, for example, ignore heterogeneity in individual disease susceptibility, contagiousness, or disease severity. The authors note that Grossman’s model uses a one-dimensional measure of health, which is highly unrealistic and may miss important variance in health outcomes between individuals; The Sensitive, Exposed, Infectious, Recovered (SEIR) epidemiological model assumes that individual susceptibility is presented by an identity rather than some kind of functional elastic/probability distribution.

Because of these different modeling approaches, economists and epidemiologists have had very different policy recommendations. To see if economists and epidemiologists could work together to create consensus policy recommendations, Darden and co-authors put together a panel of experts from both fields and asked them to assess a concrete — albeit hypothetical — issue: restaurant capacity limitations. The good news:

Members of both disciplines agreed on the importance of: (a) considering health and economic outcomes together; (b) using data to report differential disease transmission (i.e. patterns of admixture, progression of infection) and endogenous behavioral responses; and (c) make the model realistic in terms of disease burden and human behaviour.

The bad news, the prioritization of modeling these different elements varied widely across disciplines.

Epidemiologists were willing to accept strong simplistic assumptions in the field of economic outcomes, data about internal behavior, and the mechanisms by which individuals might respond to policies; Whereas economists were willing to accept equally strong assumptions regarding disease prevalence outcomes, data for heterogeneous admixture patterns, and realism in terms of the model’s calibration of disease burden at the population level…the “fatal flaws” identified in the per-discipline model reflected a failure to some extent. Great for prioritizing items that experts from other disciplines consider crucial.

Combining these two disciplines can lead to superior modeling approaches where one can take advantage of the best nuance for epidemiologists that epidemiologists incorporate into their models, while also incorporating nuances of health wealth trade-offs that economists best consider. Perhaps the bright side of the COVID-19 pandemic is the close collaboration between economists and epidemiologists.

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