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.

摘要

新冠病毒的不平衡结果-19 美国大流行的特点是其错落有致。鉴于国家协调反应薄弱，州级决策提供了一个重要的分析框架。本文探讨了此类分析如何引发与修改后的面积单位问题相关的基本地理挑战，并产生在不同状态下表现不同的科学预测模型。我们通过使用 2020 年第一波数据比较不同类型预测模型的拟合度和重要性，研究了州级政策决策的发病率。我们的研究反映了公共卫生文献、数学模型和随后的地理方法驱动因素、决策的潜在复杂模式及其对公共卫生结果的影响按状态时间线划分。考虑到这些发现，关键词：问责制，covid -19，决策，建模，拼凑。