UNDERSTANDING THE SPATIAL PATCHWORK OF PREDICTIVE MODELING OF FIRST WAVE PANDEMIC DECISIONS BY US GOVERNORS

Abstract

The uneven outcomes of the covid-19 pandemic in the United States can be characterized by its patchwork patterns. Given a weak national coordinated response, state-level decisions offer an important frame for analysis. This article explores how such analysis invokes fundamental geographic challenges related to the modified areal unit problem, and results in scientific predictive models that behave differently in different states. We examined morbidity with respect to state-level policy decisions, by comparing the fit and significance of different types of predictive modeling using data from the first wave of 2020. Our research reflects upon public health literature, mathematical modeling, and geographic approaches in the wake of the underlying complex pattern of drivers, decisions, and their impact on public health outcomes state by statetime line. Contemplating these findings, we discuss the need to improve integration of fundamental geographic concepts to creatively develop modeling and interpretations across disciplines that offer value for both informing and holding accountable decision makers of the jurisdictions in which we live. Keywords: Accountability, covid-19, decision-making, modeling, patchwork.

Acknowledgments

Thanks to Katsiaryna Varfalameyeva for analytical support. We appreciate the feedback on early conceptualization from Dr. Ariane Middel.

Notes

1 The choice of Arizona is somewhat arbitrary, as any state could serve the purpose to illustrate the patchwork character of model performance. Arizona has the distinction of being among the top three states of earliest confirmed COVID-19 cases, with the longest time lag to a first decision to mediate.

2 While contributing to our process of developing the mathematical models as noted in the following sections, these decision data themselves were ultimately not directly included in the models as presented here. The specific proclamations of gubernatorial decisions are not rapidly time-varying factors, which makes using them in such models ineffective, so the more temporally variable factors accounted for the impact of the decisions on mobility data and mask data, for example.

Additional information

Funding

This research received funding from the US National Science Foundation, Award No. 2029044, RAPID: Active Tracking of Disease Spread in CoVID19 via Graph Predictive Analytics, Gautam Dasarathy, Principal Investigator. The ASU Knowledge Exchange for Resilience is supported by Virginia G. Piper Charitable Trust. Piper Trust supports organizations that enrich health, well-being, and opportunity for the people of Maricopa County, Arizona.