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Article: Spatial Modeling of Sociodemographic Risk for COVID-19 Mortality.

Seamon, Erich / Ridenhour, Benjamin J / Miller, Craig R / Johnson-Leung, Jennifer

medRxiv : the preprint server for health sciences

2024

Abstract: In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States (US), exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, ... ...

Abstract	In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States (US), exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, few have looked at spatiotemporal variation at refined geographic scales. The objective of this analysis is to examine this spatiotemporal variation in COVID-19 deaths with respect to association with socioeconomic, health, demographic, and political factors. We use multivariate regression applied to Health and Human Services (HHS) regions as well as nationwide county-level geographically weighted random forest (GWRF) models. Analyses were performed on data from three separate time frames which correspond to the spread of distinct viral variants in the US: pandemic onset until May 2021, May 2021 through November 2021, and December 2021 until April 2022. Multivariate regression results for all regions across three time windows suggest that existing measures of social vulnerability for disaster preparedness (SVI) are predictive of a higher degree of mortality from COVID-19. In comparison, GWRF models provide a more robust evaluation of feature importance and prediction, exposing the value of local features for prediction, such as obesity, which is obscured by coarse-grained analysis. Overall, GWRF results indicate that this more nuanced modeling strategy is useful for determining the spatial variation in the importance of sociodemographic risk factors for predicting COVID-19 mortality.
Language	English
Publishing date	2024-02-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.07.21.23292785
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities.

Meadows, Tyler / Coats, Erik R / Narum, Solana / Top, Eva / Ridenhour, Benjamin J / Stalder, Thibault

medRxiv : the preprint server for health sciences

2024

Abstract: Wastewater can play a vital role in infectious disease surveillance, especially in underserved communities where it can reduce the equity gap to larger municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. We ... ...

Abstract	Wastewater can play a vital role in infectious disease surveillance, especially in underserved communities where it can reduce the equity gap to larger municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. We tested if detecting SARS-CoV-2 in wastewater can predict outbreaks in rural communities. Under the CDC National Wastewater Surveillance program, we monitored several rural communities in Idaho (USA). While high daily variations in wastewater viral load made real-time interpretation difficult, a SEIR model could factor out the data noise and forecast the start of the Omicron outbreak in five of the six cities that were sampled soon after SARS-CoV-2 quantities increased in wastewater. For one city, the model could predict an outbreak 11 days before reported clinical cases began to increase. An epidemiological modeling approach can transform how epidemiologists use wastewater data to provide public health guidance on infectious diseases in rural communities.
Language	English
Publishing date	2024-02-03
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.02.01.24302131
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Application of elastic net regression for modeling COVID-19 sociodemographic risk factors.

Moxley, Tristan A / Johnson-Leung, Jennifer / Seamon, Erich / Williams, Christopher / Ridenhour, Benjamin J

PloS one

2024 Volume 19, Issue 1, Page(s) e0297065

Abstract: Objectives: COVID-19 has been at the forefront of global concern since its emergence in December of 2019. Determining the social factors that drive case incidence is paramount to mitigating disease spread. We gathered data from the Social Vulnerability ... ...

Abstract	Objectives: COVID-19 has been at the forefront of global concern since its emergence in December of 2019. Determining the social factors that drive case incidence is paramount to mitigating disease spread. We gathered data from the Social Vulnerability Index (SVI) along with Democratic voting percentage to attempt to understand which county-level sociodemographic metrics had a significant correlation with case rate for COVID-19. Methods: We used elastic net regression due to issues with variable collinearity and model overfitting. Our modelling framework included using the ten Health and Human Services regions as submodels for the two time periods 22 March 2020 to 15 June 2021 (prior to the Delta time period) and 15 June 2021 to 1 November 2021 (the Delta time period). Results: Statistically, elastic net improved prediction when compared to multiple regression, as almost every HHS model consistently had a lower root mean square error (RMSE) and satisfactory R2 coefficients. These analyses show that the percentage of minorities, disabled individuals, individuals living in group quarters, and individuals who voted Democratic correlated significantly with COVID-19 attack rate as determined by Variable Importance Plots (VIPs). Conclusions: The percentage of minorities per county correlated positively with cases in the earlier time period and negatively in the later time period, which complements previous research. In contrast, higher percentages of disabled individuals per county correlated negatively in the earlier time period. Counties with an above average percentage of group quarters experienced a high attack rate early which then diminished in significance after the primary vaccine rollout. Higher Democratic voting consistently correlated negatively with cases, coinciding with previous findings regarding a partisan divide in COVID-19 cases at the county level. Our findings can assist regional policymakers in distributing resources to more vulnerable counties in future pandemics based on SVI.
MeSH term(s)	Humans ; United States ; COVID-19/epidemiology ; Incidence ; Voting ; Pandemics/prevention & control ; Risk Factors
Language	English
Publishing date	2024-01-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0297065
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Stability of equilibria in quantitative genetic models based on modified-gradient systems.

Ridenhour, Benjamin J / Ridenhour, Jerry R

Journal of biological dynamics

2018 Volume 12, Issue 1, Page(s) 39–50

Abstract: Motivated by questions in biology, we investigate the stability of equilibria of the dynamical system [Formula: see text] which arise as critical points of f, under the assumption that [Formula: see text] is positive semi-definite. It is shown that the ... ...

Abstract	Motivated by questions in biology, we investigate the stability of equilibria of the dynamical system [Formula: see text] which arise as critical points of f, under the assumption that [Formula: see text] is positive semi-definite. It is shown that the condition [Formula: see text], where [Formula: see text] is the smallest eigenvalue of [Formula: see text], plays a key role in guaranteeing uniform asymptotic stability and in providing information on the basis of attraction of those equilibria.
MeSH term(s)	Models, Genetic ; Phenotype
Language	English
Publishing date	2018-12
Publishing country	England
Document type	Journal Article
ISSN	1751-3766
ISSN (online)	1751-3766
DOI	10.1080/17513758.2017.1400598
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Application of Elastic Net Regression for Modeling COVID-19 Sociodemographic Risk Factors.

Moxley, Tristan A / Johnson-Leung, Jennifer / Seamon, Erich / Williams, Christopher / Ridenhour, Benjamin J

medRxiv : the preprint server for health sciences

2023

Abstract	Objectives: COVID-19 has been at the forefront of global concern since its emergence in December of 2019. Determining the social factors that drive case incidence is paramount to mitigating disease spread. We gathered data from the Social Vulnerability Index (SVI) along with Democratic voting percentage to attempt to understand which county-level sociodemographic metrics had a significant correlation with case rate for COVID-19. Methods: We used elastic net regression due to issues with variable collinearity and model overfitting. Our modelling framework included using the ten Health and Human Services regions as submodels for the two time periods 22 March 2020 to 15 June 2021 (prior to the Delta time period) and 15 June 2021 to 1 November 2021 (the Delta time period). Results: Statistically, elastic net improved prediction when compared to multiple regression, as almost every HHS model consistently had a lower root mean square error (RMSE) and satisfactory Conclusions: The percentage of minorities per county correlated positively with cases in the earlier time period and negatively in the later time period, which complements previous research. In contrast, higher percentages of disabled individuals per county correlated negatively in the earlier time period. Counties with an above average percentage of group quarters experienced a high attack rate early which then diminished in significance after the primary vaccine rollout. Higher Democratic voting consistently correlated negatively with cases, coinciding with previous findings regarding a partisan divide in COVID-19 cases at the county level. Our findings can assist policymakers in distributing resources to more vulnerable counties in future pandemics based on SVI.
Language	English
Publishing date	2023-01-20
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.19.23284288
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Structural identifiability of the generalized Lotka-Volterra model for microbiome studies.

Remien, Christopher H / Eckwright, Mariah J / Ridenhour, Benjamin J

Royal Society open science

2021 Volume 8, Issue 7, Page(s) 201378

Abstract: Population dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research, including predicting how populations ... ...

Abstract	Population dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research, including predicting how populations change over time, determining how manipulations of microbiomes affect dynamics and designing synthetic microbiomes to perform tasks. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model. We used structural identifiability analyses to determine the extent to which a time series of relative abundances can be used to parametrize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Using synthetic data of a simple community for which we know the underlying structure, local practical identifiability analysis showed that modest amounts of both process and measurement error do not fundamentally affect these identifiability properties.
Language	English
Publishing date	2021-07-21
Publishing country	England
Document type	Journal Article
ZDB-ID	2787755-3
ISSN	2054-5703
ISSN	2054-5703
DOI	10.1098/rsos.201378
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Article ; Online: Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities

Meadows, Tyler / Coats, Erik R. / Narum, Solana / Top, Eva / Ridenhour, Benjamin J. / Stalder, Thibault

medRxiv

Abstract	Wastewater can play a vital role in infectious disease surveillance, especially in underserved communities where it can reduce the equity gap to larger municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. We tested if detecting SARS-CoV-2 in wastewater can predict outbreaks in rural communities. Under the CDC National Wastewater Surveillance program, we monitored several rural communities in Idaho (USA). While high daily variations in wastewater viral load made real-time interpretation difficult, a SEIR model could factor out the data noise and forecast the start of the Omicron outbreak in five of the six cities that were sampled soon after SARS-CoV-2 quantities increased in wastewater. For one city, the model could predict an outbreak 11 days before reported clinical cases began to increase. An epidemiological modeling approach can transform how epidemiologists use wastewater data to provide public health guidance on infectious diseases in rural communities.
Keywords	covid19
Language	English
Publishing date	2024-02-03
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2024.02.01.24302131
Database	COVID19

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Article ; Online: Correction: Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations.

Lee, Jessica A / Riazi, Siavash / Nemati, Shahla / Bazurto, Jannell V / Vasdekis, Andreas E / Ridenhour, Benjamin J / Remien, Christopher H / Marx, Christopher J

PLoS genetics

2023 Volume 19, Issue 4, Page(s) e1010714

Abstract: This corrects the article DOI: 10.1371/journal.pgen.1008458.]. ...

Abstract	[This corrects the article DOI: 10.1371/journal.pgen.1008458.].
Language	English
Publishing date	2023-04-05
Publishing country	United States
Document type	Published Erratum
ZDB-ID	2186725-2
ISSN	1553-7404 ; 1553-7390
ISSN (online)	1553-7404
ISSN	1553-7390
DOI	10.1371/journal.pgen.1010714
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Article ; Online: Spatiotemporal Impacts of Ideology and Social Vulnerability on COVID-19 for the United States

Seamon, Erich / Johnson-Leung, Jennifer / Miller, Craig R. / Ridenhour, Benjamin J.

medRxiv

Abstract: In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States, exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, few ... ...

Abstract	In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States, exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, few have looked at spatiotemporal variation at refined geographic scales. The objective of this analysis is to examine spatiotemporal variation of COVID-19 deaths in association with socioeconomic, health, demographic, and political factors, using regionalized multivariate regression as well as nationwide county-level geographically weighted random forest (GWRF) models. Analyses were performed on data from three sepearate timeframes: pandemic onset until May 2021, May 2021 through November 2021, and December 2021 until April 2022.Regionalized regression results across three time windows suggest that existing measures of social vulnerability for disaster preparedness (SVI) are associated with a higher degree of mortality from COVID-19. In comparison, GWRF models provide a more robust evaluation of feature importance and prediction, exposing the importance of local features, such as obesity, which is obscured by regional delineation. Overall, GWRF results indicate a more nuanced modeling strategy is useful for capturing the diverse spatial and temporal nature of the COVID-19 pandemic.
Keywords	covid19
Language	English
Publishing date	2023-07-26
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.07.21.23292785
Database	COVID19

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Article ; Online: Stability of equilibria in quantitative genetic models based on modified-gradient systems

Benjamin J. Ridenhour / Jerry R. Ridenhour

Journal of Biological Dynamics, Vol 12, Iss 1, Pp 39-

2018 Volume 50

Abstract: Motivated by questions in biology, we investigate the stability of equilibria of the dynamical system $ \mathbf {x}^{\prime }=P(t)\nabla f(x) $ which arise as critical points of f, under the assumption that $ P(t) $ is positive semi-definite. It is shown ...

Abstract	Motivated by questions in biology, we investigate the stability of equilibria of the dynamical system $ \mathbf {x}^{\prime }=P(t)\nabla f(x) $ which arise as critical points of f, under the assumption that $ P(t) $ is positive semi-definite. It is shown that the condition $ \int ^{\infty }\lambda _{1}(P(t))\ {\rm d}t=\infty $ , where $ \lambda _{1}(P(t)) $ is the smallest eigenvalue of $ P(t) $ , plays a key role in guaranteeing uniform asymptotic stability and in providing information on the basis of attraction of those equilibria.
Keywords	dynamical systems ; modified-gradient system ; equilibria ; asymptotic stability ; basin of attraction ; Environmental sciences ; GE1-350 ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2018-01-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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