Understanding the spatial patchwork of predictive modeling of first wave pandemic decisions by US governors. (Special Issue: Examining the COVID crisis.)
Geographical Review
; 111(4):592-615, 2021.
Article
in English
| GIM | ID: covidwho-1747099
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 a real 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 state time 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.
mathematical models; human diseases; public health; morbidity; pandemics; coronavirus disease 2019; viral diseases; man; Severe acute respiratory syndrome coronavirus 2; USA; Homo; Hominidae; primates; mammals; vertebrates; Chordata; animals; eukaryotes; APEC countries; high income countries; North America; America; OECD Countries; very high Human Development Index countries; Severe acute respiratory syndrome-related coronavirus; Betacoronavirus; Coronavirinae; Coronaviridae; Nidovirales; positive-sense ssRNA Viruses; ssRNA Viruses; RNA Viruses; viruses; United States of America; SARS-CoV-2; viral infections
Full text:
Available
Collection:
Databases of international organizations
Database:
GIM
Type of study:
Prognostic study
Language:
English
Journal:
Geographical Review
Year:
2021
Document Type:
Article
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