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  1. Article ; Online: A study of SARS-COV-2 outbreaks in US federal prisons: the linkage between staff, incarcerated populations, and community transmission.

    Towers, Sherry / Wallace, Danielle / Walker, Jason / Eason, John M / Nelson, Jake R / Grubesic, Tony H

    BMC public health

    2022  Volume 22, Issue 1, Page(s) 482

    Abstract: Background: Since the novel coronavirus SARS-COV-2 was first identified to be circulating in the US on January 20, 2020, some of the worst outbreaks have occurred within state and federal prisons. The vulnerability of incarcerated populations, and the ... ...

    Abstract Background: Since the novel coronavirus SARS-COV-2 was first identified to be circulating in the US on January 20, 2020, some of the worst outbreaks have occurred within state and federal prisons. The vulnerability of incarcerated populations, and the additional threats posed to the health of prison staff and the people they contact in surrounding communities underline the need to better understand the dynamics of transmission in the inter-linked incarcerated population/staff/community sub-populations to better inform optimal control of SARS-COV-2.
    Methods: We examined SARS-CoV-2 case data from 101 non-administrative federal prisons between 5/18/2020 to 01/31/2021 and examined the per capita size of outbreaks in staff and the incarcerated population compared to outbreaks in the communities in the counties surrounding the prisons during the summer and winter waves of the SARS-COV-2 pandemic. We also examined the impact of decarceration on per capita rates in the staff/incarcerated/community populations.
    Results: For both the summer and winter waves we found significant inter-correlations between per capita rates in the outbreaks among the incarcerated population, staff, and the community. Over-all during the pandemic, per capita rates were significantly higher in the incarcerated population than in both the staff and community (paired Student's t-test p = 0.03 and p < 0.001, respectively). Average per capita rates of incarcerated population outbreaks were significantly associated with prison security level, ranked from lowest per capita rate to highest: High, Minimum, Medium, and Low security. Federal prisons decreased the incarcerated population by a relative factor of 96% comparing the winter to summer wave (one SD range [90%,102%]). We found no significant impact of decarceration on per capita rates of SARS-COV-2 infection in the staff community populations, but decarceration was significantly associated with a decrease in incarcerated per capita rates during the winter wave (Negative Binomial regression p = 0.015).
    Conclusions: We found significant evidence of community/staff/incarcerated population inter-linkage of SARS-COV-2 transmission. Further study is warranted to determine which control measures aimed at the incarcerated population and/or staff are most efficacious at preventing or controlling outbreaks.
    MeSH term(s) COVID-19/epidemiology ; Disease Outbreaks ; Humans ; Prisoners ; Prisons ; SARS-CoV-2
    Language English
    Publishing date 2022-03-11
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2041338-5
    ISSN 1471-2458 ; 1471-2458
    ISSN (online) 1471-2458
    ISSN 1471-2458
    DOI 10.1186/s12889-022-12813-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Geodemographic insights on the COVID-19 pandemic in the State of Wisconsin and the role of risky facilities.

    Grubesic, Tony H / Nelson, Jake R / Wallace, Danielle / Eason, John / Towers, Sherry / Walker, Jason

    GeoJournal

    2021  Volume 87, Issue 5, Page(s) 4311–4333

    Abstract: The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to impact the United States. While age and comorbid health conditions remain primary concerns in the community-based transmission of the virus, ... ...

    Abstract The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to impact the United States. While age and comorbid health conditions remain primary concerns in the community-based transmission of the virus, empirical evidence continues to suggest that substantial variability exists in the geographic and geodemographic distribution of COVID-19 infection rates. The purpose of this paper is to provide an alternative, spatiotemporal perspective on the pandemic using the state of Wisconsin as a case study. Specifically, in this paper, we explore the geographic nuances of COVID-19 and its spread in Wisconsin using a suite of spatial statistical approaches. We link detected hot spots of COVID-19 to local geodemographic profiles and the presence of high-risk facilities, including federal and state correctional facilities. The results suggest that the virus disproportionately impacts several communities and geodemographic groups and that proximity to risky facilities correlates to increased community infection rates.
    Language English
    Publishing date 2021-09-12
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 715360-0
    ISSN 1572-9893 ; 0343-2521
    ISSN (online) 1572-9893
    ISSN 0343-2521
    DOI 10.1007/s10708-021-10503-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Geodemographic insights on the COVID-19 pandemic in the State of Wisconsin and the role of risky facilities

    Grubesic, Tony H. / Nelson, Jake R. / Wallace, Danielle / Eason, John / Towers, Sherry / Walker, Jason

    GeoJournal. 2022 Oct., v. 87, no. 5 p.4311-4333

    2022  

    Abstract: The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to impact the United States. While age and comorbid health conditions remain primary concerns in the community-based transmission of the virus, ... ...

    Abstract The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to impact the United States. While age and comorbid health conditions remain primary concerns in the community-based transmission of the virus, empirical evidence continues to suggest that substantial variability exists in the geographic and geodemographic distribution of COVID-19 infection rates. The purpose of this paper is to provide an alternative, spatiotemporal perspective on the pandemic using the state of Wisconsin as a case study. Specifically, in this paper, we explore the geographic nuances of COVID-19 and its spread in Wisconsin using a suite of spatial statistical approaches. We link detected hot spots of COVID-19 to local geodemographic profiles and the presence of high-risk facilities, including federal and state correctional facilities. The results suggest that the virus disproportionately impacts several communities and geodemographic groups and that proximity to risky facilities correlates to increased community infection rates.
    Keywords COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; case studies ; pandemic ; viruses ; Wisconsin
    Language English
    Dates of publication 2022-10
    Size p. 4311-4333.
    Publishing place Springer Netherlands
    Document type Article ; Online
    ZDB-ID 715360-0
    ISSN 1572-9893 ; 0343-2521
    ISSN (online) 1572-9893
    ISSN 0343-2521
    DOI 10.1007/s10708-021-10503-5
    Database NAL-Catalogue (AGRICOLA)

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  4. Article ; Online: Is There a Temporal Relationship between COVID-19 Infections among Prison Staff, Incarcerated Persons and the Larger Community in the United States?

    Wallace, Danielle / Eason, John M / Walker, Jason / Towers, Sherry / Grubesic, Tony H / Nelson, Jake R

    International journal of environmental research and public health

    2021  Volume 18, Issue 13

    Abstract: Background: Our objective was to examine the temporal relationship between COVID-19 infections among prison staff, incarcerated individuals, and the general population in the county where the prison is located among federal prisons in the United States.! ...

    Abstract Background: Our objective was to examine the temporal relationship between COVID-19 infections among prison staff, incarcerated individuals, and the general population in the county where the prison is located among federal prisons in the United States.
    Methods: We employed population-standardized regressions with fixed effects for prisons to predict the number of active cases of COVID-19 among incarcerated persons using data from the Federal Bureau of Prisons (BOP) for the months of March to December in 2020 for 63 prisons.
    Results: There is a significant relationship between the COVID-19 prevalence among staff, and through them, the larger community, and COVID-19 prevalence among incarcerated persons in the US federal prison system. When staff rates are low or at zero, COVID-19 incidence in the larger community continues to have an association with COVID-19 prevalence among incarcerated persons, suggesting possible pre-symptomatic and asymptomatic transmission by staff. Masking policies slightly reduced COVID-19 prevalence among incarcerated persons, though the association between infections among staff, the community, and incarcerated persons remained significant and strong.
    Conclusion: The relationship between COVID-19 infections among staff and incarcerated persons shows that staff is vital to infection control, and correctional administrators should also focus infection containment efforts on staff, in addition to incarcerated persons.
    MeSH term(s) COVID-19 ; Humans ; Infection Control ; Prisoners ; Prisons ; SARS-CoV-2 ; United States/epidemiology
    Language English
    Publishing date 2021-06-26
    Publishing country Switzerland
    Document type Journal Article
    ISSN 1660-4601
    ISSN (online) 1660-4601
    DOI 10.3390/ijerph18136873
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective.

    Towers, Sherry / Chen, Siqiao / Malik, Abish / Ebert, David

    PloS one

    2018  Volume 13, Issue 10, Page(s) e0205151

    Abstract: Background: Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime.: Methods: We examine 5.7 million ... ...

    Abstract Background: Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime.
    Methods: We examine 5.7 million reported incidents of crime that occurred in the City of Chicago between 2001 to 2014. Using linear regression methods, we examine the temporal relationship of the crime incidents to weather, holidays, school vacations, day-of-week, and paydays. We correct the data for dominant sources of auto-correlation, and we then employ bootstrap methods for model selection. Importantly for the aspect of predictive analytics, we validate the predictive capabilities of our model on an independent data set; model validation has been almost universally overlooked in the literature on this subject.
    Results: We find significant dependence of crime on time of year, holidays, and weekdays. We find that dependence of aggressive crime on temperature depends on the hour of the day, and whether it takes place outside or inside. In addition, unusually hot/cold days are associated with unusual fluctuations upwards/downwards in crimes of aggression, respectively, regardless of the time of year.
    Conclusions: Including holidays, festivals, and school holiday periods in crime predictive analytics software can improve the accuracy and precision of temporal predictions. We also find that including forecasts for temperature may significantly improve short term crime forecasts for the temporal trends in many types of crime, particularly aggressive crime.
    MeSH term(s) Aggression ; Animals ; Chicago ; Crime ; Environment, Controlled ; Humans ; Models, Theoretical ; Periodicity ; Time Factors ; United States ; Weather
    Language English
    Publishing date 2018-10-24
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Validation Studies
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0205151
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Detecting the contagion effect in mass killings; a constructive example of the statistical advantages of unbinned likelihood methods.

    Towers, Sherry / Mubayi, Anuj / Castillo-Chavez, Carlos

    PloS one

    2018  Volume 13, Issue 5, Page(s) e0196863

    Abstract: ... social sciences.: Methods: In 2015, Towers et al published a paper that quantified the long-suspected contagion ... to contagion effects. The unbinned likelihood methods used by Towers et al (2015) did not suffer ...

    Abstract Background: When attempting to statistically distinguish between a null and an alternative hypothesis, many researchers in the life and social sciences turn to binned statistical analysis methods, or methods that are simply based on the moments of a distribution (such as the mean, and variance). These methods have the advantage of simplicity of implementation, and simplicity of explanation. However, when null and alternative hypotheses manifest themselves in subtle differences in patterns in the data, binned analysis methods may be insensitive to these differences, and researchers may erroneously fail to reject the null hypothesis when in fact more sensitive statistical analysis methods might produce a different result when the null hypothesis is actually false. Here, with a focus on two recent conflicting studies of contagion in mass killings as instructive examples, we discuss how the use of unbinned likelihood methods makes optimal use of the information in the data; a fact that has been long known in statistical theory, but perhaps is not as widely appreciated amongst general researchers in the life and social sciences.
    Methods: In 2015, Towers et al published a paper that quantified the long-suspected contagion effect in mass killings. However, in 2017, Lankford & Tomek subsequently published a paper, based upon the same data, that claimed to contradict the results of the earlier study. The former used unbinned likelihood methods, and the latter used binned methods, and comparison of distribution moments. Using these analyses, we also discuss how visualization of the data can aid in determination of the most appropriate statistical analysis methods to distinguish between a null and alternate hypothesis. We also discuss the importance of assessment of the robustness of analysis results to methodological assumptions made (for example, arbitrary choices of number of bins and bin widths when using binned methods); an issue that is widely overlooked in the literature, but is critical to analysis reproducibility and robustness.
    Conclusions: When an analysis cannot distinguish between a null and alternate hypothesis, care must be taken to ensure that the analysis methodology itself maximizes the use of information in the data that can distinguish between the two hypotheses. The use of binned methods by Lankford & Tomek (2017), that examined how many mass killings fell within a 14 day window from a previous mass killing, substantially reduced the sensitivity of their analysis to contagion effects. The unbinned likelihood methods used by Towers et al (2015) did not suffer from this problem. While a binned analysis might be favorable for simplicity and clarity of presentation, unbinned likelihood methods are preferable when effects might be somewhat subtle.
    MeSH term(s) Biological Science Disciplines/statistics & numerical data ; Biometry/methods ; Humans ; Likelihood Functions ; Social Sciences/statistics & numerical data
    Language English
    Publishing date 2018
    Publishing country United States
    Document type Journal Article
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0196863
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: A study of SARS-COV-2 outbreaks in US federal prisons

    Sherry Towers / Danielle Wallace / Jason Walker / John M. Eason / Jake R. Nelson / Tony H. Grubesic

    BMC Public Health, Vol 22, Iss 1, Pp 1-

    the linkage between staff, incarcerated populations, and community transmission

    2022  Volume 11

    Abstract: Abstract Background Since the novel coronavirus SARS-COV-2 was first identified to be circulating in the US on January 20, 2020, some of the worst outbreaks have occurred within state and federal prisons. The vulnerability of incarcerated populations, ... ...

    Abstract Abstract Background Since the novel coronavirus SARS-COV-2 was first identified to be circulating in the US on January 20, 2020, some of the worst outbreaks have occurred within state and federal prisons. The vulnerability of incarcerated populations, and the additional threats posed to the health of prison staff and the people they contact in surrounding communities underline the need to better understand the dynamics of transmission in the inter-linked incarcerated population/staff/community sub-populations to better inform optimal control of SARS-COV-2. Methods We examined SARS-CoV-2 case data from 101 non-administrative federal prisons between 5/18/2020 to 01/31/2021 and examined the per capita size of outbreaks in staff and the incarcerated population compared to outbreaks in the communities in the counties surrounding the prisons during the summer and winter waves of the SARS-COV-2 pandemic. We also examined the impact of decarceration on per capita rates in the staff/incarcerated/community populations. Results For both the summer and winter waves we found significant inter-correlations between per capita rates in the outbreaks among the incarcerated population, staff, and the community. Over-all during the pandemic, per capita rates were significantly higher in the incarcerated population than in both the staff and community (paired Student’s t-test p = 0.03 and p < 0.001, respectively). Average per capita rates of incarcerated population outbreaks were significantly associated with prison security level, ranked from lowest per capita rate to highest: High, Minimum, Medium, and Low security. Federal prisons decreased the incarcerated population by a relative factor of 96% comparing the winter to summer wave (one SD range [90%,102%]). We found no significant impact of decarceration on per capita rates of SARS-COV-2 infection in the staff community populations, but decarceration was significantly associated with a decrease in incarcerated per capita rates during the winter wave (Negative Binomial ...
    Keywords Coronavirus ; Prisons ; Pandemic ; SARS-COV-2 ; Disease intervention strategies ; Decarceration ; Public aspects of medicine ; RA1-1270
    Subject code 306
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Detecting the contagion effect in mass killings; a constructive example of the statistical advantages of unbinned likelihood methods.

    Sherry Towers / Anuj Mubayi / Carlos Castillo-Chavez

    PLoS ONE, Vol 13, Iss 5, p e

    2018  Volume 0196863

    Abstract: ... appreciated amongst general researchers in the life and social sciences.In 2015, Towers et al published ...

    Abstract When attempting to statistically distinguish between a null and an alternative hypothesis, many researchers in the life and social sciences turn to binned statistical analysis methods, or methods that are simply based on the moments of a distribution (such as the mean, and variance). These methods have the advantage of simplicity of implementation, and simplicity of explanation. However, when null and alternative hypotheses manifest themselves in subtle differences in patterns in the data, binned analysis methods may be insensitive to these differences, and researchers may erroneously fail to reject the null hypothesis when in fact more sensitive statistical analysis methods might produce a different result when the null hypothesis is actually false. Here, with a focus on two recent conflicting studies of contagion in mass killings as instructive examples, we discuss how the use of unbinned likelihood methods makes optimal use of the information in the data; a fact that has been long known in statistical theory, but perhaps is not as widely appreciated amongst general researchers in the life and social sciences.In 2015, Towers et al published a paper that quantified the long-suspected contagion effect in mass killings. However, in 2017, Lankford & Tomek subsequently published a paper, based upon the same data, that claimed to contradict the results of the earlier study. The former used unbinned likelihood methods, and the latter used binned methods, and comparison of distribution moments. Using these analyses, we also discuss how visualization of the data can aid in determination of the most appropriate statistical analysis methods to distinguish between a null and alternate hypothesis. We also discuss the importance of assessment of the robustness of analysis results to methodological assumptions made (for example, arbitrary choices of number of bins and bin widths when using binned methods); an issue that is widely overlooked in the literature, but is critical to analysis reproducibility and ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 310
    Language English
    Publishing date 2018-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Factors influencing temporal patterns in crime in a large American city

    Sherry Towers / Siqiao Chen / Abish Malik / David Ebert

    PLoS ONE, Vol 13, Iss 10, p e

    A predictive analytics perspective.

    2018  Volume 0205151

    Abstract: BACKGROUND:Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime. METHODS:We examine 5.7 million reported ... ...

    Abstract BACKGROUND:Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime. METHODS:We examine 5.7 million reported incidents of crime that occurred in the City of Chicago between 2001 to 2014. Using linear regression methods, we examine the temporal relationship of the crime incidents to weather, holidays, school vacations, day-of-week, and paydays. We correct the data for dominant sources of auto-correlation, and we then employ bootstrap methods for model selection. Importantly for the aspect of predictive analytics, we validate the predictive capabilities of our model on an independent data set; model validation has been almost universally overlooked in the literature on this subject. RESULTS:We find significant dependence of crime on time of year, holidays, and weekdays. We find that dependence of aggressive crime on temperature depends on the hour of the day, and whether it takes place outside or inside. In addition, unusually hot/cold days are associated with unusual fluctuations upwards/downwards in crimes of aggression, respectively, regardless of the time of year. CONCLUSIONS:Including holidays, festivals, and school holiday periods in crime predictive analytics software can improve the accuracy and precision of temporal predictions. We also find that including forecasts for temperature may significantly improve short term crime forecasts for the temporal trends in many types of crime, particularly aggressive crime.
    Keywords Medicine ; R ; Science ; Q
    Subject code 360
    Language English
    Publishing date 2018-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Is There a Temporal Relationship between COVID-19 Infections among Prison Staff, Incarcerated Persons and the Larger Community in the United States?

    Danielle Wallace / John M. Eason / Jason Walker / Sherry Towers / Tony H. Grubesic / Jake R. Nelson

    International Journal of Environmental Research and Public Health, Vol 18, Iss 6873, p

    2021  Volume 6873

    Abstract: Background: Our objective was to examine the temporal relationship between COVID-19 infections among prison staff, incarcerated individuals, and the general population in the county where the prison is located among federal prisons in the United States. ... ...

    Abstract Background: Our objective was to examine the temporal relationship between COVID-19 infections among prison staff, incarcerated individuals, and the general population in the county where the prison is located among federal prisons in the United States. Methods: We employed population-standardized regressions with fixed effects for prisons to predict the number of active cases of COVID-19 among incarcerated persons using data from the Federal Bureau of Prisons (BOP) for the months of March to December in 2020 for 63 prisons. Results: There is a significant relationship between the COVID-19 prevalence among staff, and through them, the larger community, and COVID-19 prevalence among incarcerated persons in the US federal prison system. When staff rates are low or at zero, COVID-19 incidence in the larger community continues to have an association with COVID-19 prevalence among incarcerated persons, suggesting possible pre-symptomatic and asymptomatic transmission by staff. Masking policies slightly reduced COVID-19 prevalence among incarcerated persons, though the association between infections among staff, the community, and incarcerated persons remained significant and strong. Conclusion: The relationship between COVID-19 infections among staff and incarcerated persons shows that staff is vital to infection control, and correctional administrators should also focus infection containment efforts on staff, in addition to incarcerated persons.
    Keywords COVID-19 ; incarceration ; pandemic ; incarcerated populations ; correctional staff ; prisons ; Medicine ; R
    Subject code 020
    Language English
    Publishing date 2021-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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