TY - JOUR
T1 - UNDERSTANDING THE SPATIAL PATCHWORK OF PREDICTIVE MODELING OF FIRST WAVE PANDEMIC DECISIONS BY US GOVERNORS
AU - Solís, Patricia
AU - Dasarathy, Gautam
AU - Turaga, Pavan
AU - Drake, Alexandria
AU - Vora, Kevin Jatin
AU - Sajja, Akarshan
AU - Raaman, Ankith
AU - Praharaj, Sarbeswar
AU - Lattus, Robert
N1 - Publisher Copyright:
© Copyright © 2021 by the American Geographical Society of New York.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85112019961&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112019961&partnerID=8YFLogxK
U2 - 10.1080/00167428.2021.1947139
DO - 10.1080/00167428.2021.1947139
M3 - Article
AN - SCOPUS:85112019961
SN - 0016-7428
VL - 111
SP - 592
EP - 615
JO - Geographical Review
JF - Geographical Review
IS - 4
ER -