Understanding Spatial Heterogeneity of COVID-19 Pandemic Using Shape Analysis of Growth Rate Curves (Preprint)

2020 ◽  
Author(s):  
Anuj Srivastava ◽  
Gerardo Chowell
Keyword(s):  
Growth Rate ◽  
Spatial Heterogeneity ◽  
Spatial Scales ◽  
State Level ◽  
Public Health Decision ◽  
Country Level ◽  
Geographical Regions ◽  
Health Decision ◽  
The Usa ◽  
Level Analysis

UNSTRUCTURED The growth rates of COVID-19 across different geographical regions (e.g., states in a nation, countries in a continent) follow different shapes and patterns. The overall summaries at coarser spatial scales that are obtained by simply averaging individual curves (across regions) obscure nuanced variability and blurs the spatial heterogeneity at finer spatial scales. We employ statistical methods to analyze shapes of local COVID-19 growth rate curves and statistically group them into distinct clusters, according to their shapes. Using this information, we derive the so-called elastic averages of curves within these clusters, which correspond to the dominant incidence patterns. We apply this methodology to the analysis of the daily incidence trajectory of the COVID-pandemic at two spatial scales: A state-level analysis within the USA and a country-level analysis within Europe during mid-February to mid-May, 2020. Our analyses reveal a few dominant incidence trajectories that characterize transmission dynamics across states in the USA and across countries in Europe. This approach results in broad classifications of spatial areas into different trajectories and adds to the methodological toolkit for guiding public health decision making at different spatial scales.

scholarly journals Understanding Spatial Heterogeneity of COVID-19 Pandemic Using Shape Analysis of Growth Rate Curves

2020 ◽  
Author(s):  
Anuj Srivastava ◽  
Gerardo Chowell
Keyword(s):  
Public Health ◽  
Growth Rate ◽  
Shape Analysis ◽  
Spatial Scales ◽  
Transmission Dynamics ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Health Decision ◽  
The Usa ◽  
Level Analysis

AbstractThe growth rates of COVID-19 across different geographical regions (e.g., states in a nation, countries in a continent) follow different shapes and patterns. The overall summaries at coarser spatial scales that are obtained by simply averaging individual curves (across regions) obscure nuanced variability and blurs the spatial heterogeneity at finer spatial scales. We employ statistical methods to analyze shapes of local COVID-19 growth rate curves and statistically group them into distinct clusters, according to their shapes. Using this information, we derive the so-called elastic averages of curves within these clusters, which correspond to the dominant incidence patterns. We apply this methodology to the analysis of the daily incidence trajectory of the COVID-pandemic at two spatial scales: A state-level analysis within the USA and a country-level analysis within Europe during mid-February to mid-May, 2020. Our analyses reveal a few dominant incidence trajectories that characterize transmission dynamics across states in the USA and across countries in Europe. This approach results in broad classifications of spatial areas into different trajectories and adds to the methodological toolkit for guiding public health decision making at different spatial scales.HighlightsCoarsely summarizing epidemic data collected at finer spatial scales can result in a loss of heterogenous spatial patterns that exist at finer scales. For instance, the average curves may give the impression that the epidemic’s trajectory is declining when, in fact, the trajectory of the epidemic is increasing in certain areas.Shape analysis of COVID-19 growth rate curves discovers significant heterogeneity in epidemic spread patterns across spatial areas which can be statistically clustered into distinct groups.At a higher level, clustering spatial patterns into distinct groups helps discern broad trends, such as rapid growth, leveling off, and slow decline in epidemic growth curves resulting from local transmission dynamics. At a finer level, it helps identify temporal patterns of multiple waves that characterize rate curves for different clusters.Quantitative methods for characterizing the spatial-temporal dynamics of evolving epidemic emergencies provide an objective framework to understand transmission dynamics for public health decision making.

scholarly journals Title: Modeling Study: Characterizing the Spatial Heterogeneity of the COVID-19 Pandemic through Shape Analysis of Epidemic Curves

2021 ◽  
Author(s):  
Anuj Srivast ◽  
Gerardo Chowell
Keyword(s):  
Spatial Heterogeneity ◽  
Incidence Rate ◽  
Spatial Scales ◽  
State Level ◽  
Incidence Rates ◽  
Transmission Dynamics ◽  
Rate Curve ◽  
Geographical Regions ◽  
The Usa ◽  
Level Analysis

Abstract Background: The COVID-19 incidence rates across different geographical regions (e.g., counties in a state, states in a nation, countries in a continent) follow different shapes and patterns. The overall summaries at coarser spatial scales, that are obtained by simply averaging individual curves (across regions), hide nuanced variability and blur the spatial heterogeneity at finer spatial scales. For instance, a decreasing incidence rate curve in one region is obscured by an increasing rate curve for another region, when the analysis relies on coarse averages of locally heterogeneous transmission dynamics. Objective: To highlight regional differences in COVID-19 incidence rates and to discover prominent patterns in shapes of incidence rate curves in multiple regions (USA and Europe). Methods: We employ statistical methods to analyze shapes of local COVID-19 incidence rate curves and statistically group them into distinct clusters, according to their shapes. Using this information, we derive the so-called shape averages of curves within these clusters, which represent the dominant incidence patterns of these clusters. We apply this methodology to the analysis of the daily incidence trajectory of the COVID-pandemic for two geographic areas: A state-level analysis within the USA and a country-level analysis within Europe during late-February to October 1 st , 2020. Results: Our analyses reveal that pandemic curves often differ substantially across regions. However, there are only a handful of shapes that dominate transmission dynamics for all states in the USA and countries in Europe. This approach yields a broad classification of spatial areas into different characteristic epidemic trajectories. In particular, spatial areas within the same cluster have followed similar transmission and control dynamics.Conclusion: The shape-based analysis of pandemic curves presented here helps divide country or continental data into multiple regional clusters, each cluster containing areas with similar trend patterns. This clustering helps highlight differences in pandemic curves across regions and provides summaries that better reflect dynamical patterns within the clusters. This approach adds to the methodological toolkit for public health practitioners to facilitate decision making at different spatial scales.

scholarly journals Using informatics to guide public health policy during the COVID-19 pandemic in the USA

2020 ◽  
Vol 42 (4) ◽  
pp. 660-664
Author(s):  
Jay G Ronquillo ◽  
William T Lester ◽  
Diana M Zuckerman
Keyword(s):  
Public Health ◽  
Cross Sectional Study ◽  
Washington Dc ◽  
Cross Sectional ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Health Decision ◽  
The Usa ◽  
The Impact ◽  
At Home

Abstract Background Current and future pandemics will require informatics solutions to assess the risks, resources and policies to guide better public health decision-making. Methods Cross-sectional study of all COVID-19 cases and deaths in the USA on a population- and resource-adjusted basis (as of 24 April 2020) by applying biomedical informatics and data visualization tools to several public and federal government datasets, including analysis of the impact of statewide stay-at-home orders. Results There were 2753.2 cases and 158.0 deaths per million residents, respectively, in the USA with variable distributions throughout divisions, regions and states. Forty-two states and Washington, DC, (84.3%) had statewide stay-at-home orders, with the remaining states having population-adjusted characteristics in the highest risk quartile. Conclusions Effective national preparedness requires clearly understanding states’ ability to predict, manage and balance public health needs through all stages of a pandemic. This will require leveraging data quickly, correctly and responsibly into sound public health policies.

Sophistry in American medicine? Platonic reflections on expertise, influence and the public’s health in the democratic context

2018 ◽  
Vol 45 (1) ◽  
pp. 45-51
Author(s):  
Evan V Goldstein
Keyword(s):  
Public Health ◽  
Decision Making ◽  
American Medical ◽  
Care Quality ◽  
Public Affairs ◽  
Healthcare Sector ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Health Decision ◽  
The Usa

Without question, the American medical craft—the physicians, clinicians and healthcare organisations that comprise the American healthcare sector—provides immense value to patients and contributes expertise on matters relevant to the public’s health. However, several conspicuous realities about healthcare in America should give the reader pause. Most problematic are the comparative measures of access to care, quality of care, life expectancy, racial health disparity and cost, all of which demonstrate how many Americans experience relatively lower value public health than other Western liberal democratic states. Since the early 1900s, American medical craft behaviour contributed to suboptimal social investment in public health, successfully influencing greater medical investment and higher healthcare expenditure relative to social welfare investments. Today, American policymakers seek the ‘holy grail’, a mythical panacea that purports to restrict spending and improve care quality and value, leading the USA to chase ‘technocratic solutions to political problems’. This paper explores the claim that the USA is hampered by suboptimal public health decision making. Public health decision making has been historically impacted by the overextended reach of medical craft expertise—technê in Platonic terms of art—as permitted by the American democratic political system. American policymakers must not forget that the debate over technê, epistêmê, sophistry and who should have authority in public affairs is not new. Rather, it is an ancient debate, and now as then, the ancient arguments remain relevant in a democratic context. For particularly helpful insight, one ought to look no further than the lessons of Plato’s dialogues. Platonic lessons on expertise and decision making can enlighten our understanding of modern public health decision making, specifically regarding the appropriation, allocation and distribution of health-related resources in the state.

Obesity and motor vehicle deaths in the USA: a state‐level analysis

2010 ◽  
Vol 37 (5) ◽  
pp. 544-556 ◽  
Author(s):  
Walter O. Simmons ◽  
Thomas J. Zlatoper
Keyword(s):  
Motor Vehicle ◽  
State Level ◽  
The Usa ◽  
Level Analysis

scholarly journals Development of a tobacco 21 policy assessment tool and state-level analysis in the USA, 2015–2019

2019 ◽  
pp. tobaccocontrol-2019-055102 ◽  
Author(s):  
Page D Dobbs ◽  
Ginny Chadwick ◽  
Katherine W Ungar ◽  
Chris M Dunlap ◽  
Katherine A White ◽  
...  
Keyword(s):  
Tobacco Control ◽  
Development Process ◽  
Assessment Tool ◽  
State Level ◽  
Tool Development ◽  
Tobacco Products ◽  
State Laws ◽  
The Usa ◽  
Key Policy ◽  
Level Analysis

ObjectivePolicies raising the minimum legal sales age (MLSA) of tobacco products to 21 are commonly referred to as tobacco 21. This study sought to identify components of tobacco 21 policies and develop an instrument to examine policy language within 16 state laws adopted by July 2019.MethodsThe multistage tool development process began with a review of established literature and existing tobacco 21 policies. In a series of meetings, tobacco control experts identified key policy components used to develop an initial tool. After testing and revisions, the instrument was used to code the existing tobacco 21 state-level policies. Inter-rater reliability (κ=0.70) was measured and discrepancies were discussed until consensus was met. Policy component frequencies were reported by state.ResultsWhile all 16 states raised the MLSA to 21, the laws varied widely. Two laws omitted purchaser identification requirements. Fifteen laws mentioned enforcement would include inspections, but only three provided justification for conducting inspections. All 16 states provided a penalty structure for retailer/clerk violations, but penalties ranged considerably. Fourteen states required a tobacco retail licence, nine renewed annually. Six laws contained a military exemption, five were phased-in and 10 contained purchase, use or possession laws, which penalised youth. Four states introduced or expanded pre-emption of local tobacco control.ConclusionsThe instrument developed is the first to examine policy components within state-level tobacco 21 laws. Policies that include negative components or omit positive components may not effectively prevent retailers from selling to youth, which could result in less effective laws.

scholarly journals Does High Population Density Catalyze the Spread of COVID-19?

2020 ◽  
Vol 23 (s1) ◽  
pp. 13-27
Author(s):  
Berislav Žmuk ◽  
Hrvoje Jošić
Keyword(s):  
Public Health ◽  
Population Density ◽  
Economic Problem ◽  
Economic Sectors ◽  
Policy Makers ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Country Level ◽  
Health Decision ◽  
Variable Population

Abstract COVID-19 represents not only public health emergency but has become a global economic problem. It has affected all economic sectors threatening global poverty. The important question that arises is what catalyses the spread of the disease? In this paper the relationship between population density and spread of COVID-19 is observed which is goal of the paper. For the purpose of the analysis the correlation between the population variables and COVID-19 variables on a global country level (209 countries) and regional level of individual countries with the most cases of infection is observed. The results have shown that on a country level variable population is statistically significant in all regression models for total cases, deaths and total tests variables whereas variable population density was not. The research results from this paper can be important and relevant for economic and health policy makers to guide COVID-19 surveillance and public health decision-making.

scholarly journals Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Chelsea S. Lutz ◽  
Mimi P. Huynh ◽  
Monica Schroeder ◽  
Sophia Anyatonwu ◽  
F. Scott Dahlgren ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
State Level ◽  
The United States ◽  
Federal State ◽  
Main Body ◽  
Disease Forecasting ◽  
Public Health Decision ◽  
Health Decision ◽  
State And Local

Abstract Background Infectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts. Main body For forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013–14 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication. Conclusions These efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.

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