Spatial Clustering of County-Level COVID-19 Rates in the U.S.

Andrews, Marcus R; Tamura, Kosuke; Best, Janae N.; Ceasar, Joniqua N; Batey, Kaylin G.; Kearse, Troy A.; Allen, Lavell V.; Baumer, Yvonne; Collins, Billy; Mitchell, Valérie; Powell‐Wiley, Tiffany M.

doi:10.3390/ijerph182212170

Cited by 19 publications

(15 citation statements)

References 41 publications

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“…Garfield County, Utah experienced a decline in population due to death during the pandemic [25], which might be due to pandemic prevention efforts themselves. Hooker County, Nebraska was previously identified as a high-incident outlier [26]. Neighboring Brown County may be associated with the same problematic interventions.…”

Section: Discussionmentioning

confidence: 99%

Indexing Inefficacy of Efforts to Stop Escalation of COVID Mortality

Shanmugam¹,

Fulton²,

Betancourt³

et al. 2022

Preprint

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Background. COVID-19 efforts were often ineffective in controlling the spread of the pandemic. Identifying ineffective controls during a pandemic is thus vital. Method. Utilizing publicly available data on COVID deaths in the counties of US states, we create an index to capture and interpret ineffectiveness in the efforts to reduce the spread of the pandemic in US counties. This index is based on the Intervened Poisson Distribution (IPD) introduced originally by Shanmugam. Motivation for the research idea occurred while we noticed the data dispersion of the COVID deaths is smaller than the average only in some counties. Under-dispersed data is common in statistical modeling. A novel approach we adapted in this article includes the estimation of an intervention parameter estimated through iterative non-linear optimization. Results. Twenty-five counties in California, Idaho, Minnesota, Mississippi, Montana, Nebraska, North Carolina, North Dakota, Texas, and Utah were found to be ineffective in controlling for fatalities based on the expected probability distribution. A review of the policies enacted in these areas would provide insight into ineffective prevention efforts, and some of these issues are documented in current literature. Conclusion. The IPD index an innovate way to document efficacy of interventions during pandemics. The IPD may identify ineffective efforts prior to statistical models intended to evaluate efficacy of efforts.

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Section: Discussionmentioning

confidence: 99%

Indexing Inefficacy of Efforts to Stop Escalation of COVID Mortality

Shanmugam¹,

Fulton²,

Betancourt³

et al. 2022

Preprint

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“…The importance of GIS in public health has been made even more relevant during the coronavirus-19 (COVID-19) pandemic. In addition to helping researchers visualize spatial patterns of COVID-19 incidence, GIS analysis has allowed for associations of social distancing with community transmission [28,29].…”

Section: The Role Of Geographic Information Systems Analysis In Ophth...mentioning

confidence: 99%

The evolving field of Big Data: understanding geographic information systems analysis and its transformative potential in ophthalmic research

Soares¹

2022

Current Opinion in Ophthalmology

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Purpose of reviewThe purpose of this review to is to highlight alternative uses of Big Data in the pursuit of ophthalmologic public health. In particular, we highlight geographic information systems (GIS) analysis as a type of Big Data, summarize various GIS methods, and enumerate sources of geographic data. Recent findingsThe recent implementation of the IRIS Registry Data, has expanded our real-world knowledge of ophthalmology in the United States. Such innovations in Big Data allow us to better define ophthalmic diseases, treatments, and outcomes for underserved individuals and subpopulations. One underutilized source of Big Data entails use of geographic information to evaluate geographic heterogeneity and access across the United States. SummaryGIS and Big Data allow for refined epidemiologic estimates of eye disease for specific communities. In particular, how GIS can enable researchers to examine disparities in access to ophthalmic care is reviewed. GIS best practices and some data sources for GIS in ophthalmology are also summarized.

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“…Spatio-temporal models for infection counts [ 15 , 16 ] are a particular sub-theme. These incorporate the themes of the broader disease mapping literature, such as the gains through borrowing strength and the need to reflect spatial correlation in disease; for example, see Andrews et al [ 17 ] on spatial clustering in COVID rates. Space–time models also need to incorporate the spatial diffusion or spillover related to behaviours such as commuting [ 18 , 19 ].…”

Section: Relevant Literaturementioning

confidence: 99%

A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic

Congdon

2022

IJERPH

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Spatio-temporal models need to address specific features of spatio-temporal infection data, such as periods of stable infection levels (endemicity), followed by epidemic phases, as well as infection spread from neighbouring areas. In this paper, we consider a mixture-link model for infection counts that allows alternation between epidemic phases (possibly multiple) and stable endemicity, with higher AR1 coefficients in epidemic phases. This is a form of regime-switching, allowing for non-stationarity in infection levels. We adopt a generalised Poisson model appropriate to the infection count data and avoid transformations (e.g., differencing) to alternative metrics, which have been adopted in many studies. We allow for neighbourhood spillover in infection, which is also governed by adaptive regime-switching. Compared to existing models, the observational (in-sample) model is expected to better reflect the balance between epidemic and endemic tendencies, and short-term extrapolations are likely to be improved. Two case study applications involve COVID area-time data, one for 32 London boroughs (and 96 weeks) since the start of the COVID epidemic, the other for a shorter time span focusing on the epidemic phase in 144 areas of Southeast England associated with the Alpha variant. In both applications, the proposed methods produce a better in-sample fit and out-of-sample short term predictions. The spatial dynamic implications are highlighted in the case studies.

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Spatial Clustering of County-Level COVID-19 Rates in the U.S.

Cited by 19 publications

References 41 publications

Indexing Inefficacy of Efforts to Stop Escalation of COVID Mortality

Indexing Inefficacy of Efforts to Stop Escalation of COVID Mortality

The evolving field of Big Data: understanding geographic information systems analysis and its transformative potential in ophthalmic research

A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic

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