How did COVID-19 models differ when they were built by economists vs. epidemiologists

As the COVID-19 pandemic struck, policymakers looked to researchers to both predict the future trend of the pandemic as well as examine how different policy options would impact society. Both epidemiologists and economists rose to the challenge, creating a variety of models to help inform policy decisions. How did the models developed by economists and epidemiologists differ? How did they complement each other? These are the topics of a recent paper by Darden et al. (2022).

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

Epidemiologic models generally emphasize understanding of health outcomes; thus, behavior shifts, and drivers thereof, are considered important largely to the extent that they affect public health. Economic models, by contrast, generally emphasize health-wealth tradeoffs; thus, understanding economic implications 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 appropriate nuance about the disease and make strong and/or unrealistic assumptions on disease and transmission dynamics. Many economic models, for instance, ignore heterogeneity in individual disease susceptibility, infectiousness, or disease severity. The authors note that the Grossman model uses a unidimensional metric of health, which is highly unrealistic and may miss important heterogeneity on health outcomes across individuals; the Susceptible, Exposed, Infectious, Recovered (SEIR) epidemiological model assumes individual susceptibility is given by an identity rather some type of flexible functional form/probability distribution.

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

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

The bad news, prioritizing the modelling of these different elements varied dramatically across the disciplines.

Epidemiologists were willing to accept strong simplifying assumptions in the realm of economic outcomes, data on endogenous behavior, and the mechanisms by which individual people might respond to policies; whereas economists were willing to accept equally strong assumptions regarding outcomes of disease spread, data on heterogeneous mixing patterns, and realism in terms of calibrating the model to population-level disease burden…the “fatal flaws” identified in each discipline’s model largely reflected a failure to prioritize elements that experts from the other discipline considered crucial.

Combining the two disciplines could results in superior modelling approaches whereby one could draw from the better disease nuance epidemiologists incorporate into their models, while also better incorporating the nuances of the health-wealth tradeoffs that economists consider. Perhaps a silver lining of the COVID-19 pandemic would be closer collaboration between economists and epidemiologists.