Jon Zelner, Nina Masters, Ramya Naraharisetti, Sanyu Mojola, and Merlin Chowkwanyun write:
Mathematical models have come to play a key role in global pandemic preparedness and outbreak response . . . However, these models have systematically failed to account for the social and structural factors which lead to socioeconomic, racial, and geographic health disparities. . . . We evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as “equal opportunity infectors” despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) as a potential blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. . . .
I think it touches on some of the issues in our Patterns piece, but from the perspective of saying that transmission models that omit the structural drivers of risk—almost analogous to hyperpriors on the model parameters—are inherently misspecified.
This sort of connection between statistics and politics always interests me.