Dominant Modes of Agricultural Production Helped Structure Initial COVID-19 Spread in the US Midwest

The spread of COVID-19 is geographically uneven in agricultural regions. Explanations proposed include differences in occupational risks, access to healthcare, racial inequalities, and approaches to public health. Here, we additionally explore the impacts of coexisting modes of agricultural production across counties from twelve midwestern U.S. states. In modeling COVID-19 spread before vaccine authorization, we employed and extended spatial statistical methods that make different assumptions about the natures and scales of underlying sociospatial processes. In the process, we also develop a novel approach to visualizing the results of geographically weighted regressions that allows us to identify distinctive regional regimes of epidemiological processes. Our approaches allowed for models using abstract spatial weights (e.g., inverse-squared distances) to be meaningfully improved by also integrating process-specific relations (e.g., the geographical relations of the food system or of commuting). We thus contribute in several ways to methods in health geography and epidemiology for identifying contextually sensitive public engagements in socio-eco-epidemiological issues. Our results further show that agricultural modes of production are associated with the spread of COVID-19, with counties more engaged in modes of regenerative agricultural production having lower COVID-19 rates than those dominated by modes of conventional agricultural production, even when accounting for other factors.

Automated summary

The spread of COVID-19 in agricultural regions varies geographically. Possible explanations include differences in occupational risks, access to healthcare, racial inequalities, and approaches to public health. In this study, the impacts of different modes of agricultural production across counties in the Midwest United States on COVID-19 spread were explored. Using spatial statistical methods, the researchers developed a novel approach to visualize the results and identified distinct regional patterns in epidemiological processes. The findings show that counties with more engagement in regenerative agricultural production have lower COVID-19 rates compared to those dominated by conventional agricultural production, even after considering other factors.