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  1. Article: Modeling the Transmission Mitigation Impact of Testing for Infectious Diseases.

    Middleton, Casey / Larremore, Daniel B

    medRxiv : the preprint server for health sciences

    2024  

    Abstract: A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing transmission. Here, we introduce testing effectiveness (TE)-the fraction by which testing and post-diagnosis ... ...

    Abstract A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing transmission. Here, we introduce testing effectiveness (TE)-the fraction by which testing and post-diagnosis isolation reduce transmission at the population scale-and a model that incorporates test specifications and usage, within-host pathogen dynamics, and human behaviors to estimate TE. Using TE to guide recommendations, we show that today's rapid diagnostics should be used immediately upon symptom onset to control influenza A and respiratory syncytial virus (RSV), but delayed by up to 2d to control omicron-era SARS-CoV-2. Furthermore, while rapid tests are superior to RT-qPCR for control of founder-strain SARS-CoV-2, omicron-era changes in viral kinetics and rapid test sensitivity cause a reversal, with higher TE for RT-qPCR despite longer turnaround times. Finally, we illustrate the model's flexibility by quantifying tradeoffs in the use of post-diagnosis testing to shorten isolation times.
    Language English
    Publishing date 2024-03-05
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.09.22.23295983
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Bayesian estimation of community size and overlap from random subsamples.

    Johnson, Erik K / Larremore, Daniel B

    PLoS computational biology

    2022  Volume 18, Issue 9, Page(s) e1010451

    Abstract: Counting the number of species, items, or genes that are shared between two groups, sets, or communities is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two communities ... ...

    Abstract Counting the number of species, items, or genes that are shared between two groups, sets, or communities is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two communities becomes an estimation problem. Furthermore, to calculate normalized measures of β-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the communities being compared. Previous efforts to address these problems have assumed knowledge of total community sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address communities of unknown size and show that this produces systematically better estimates-both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species, item, or gene count data to refine estimates of community size in a Bayesian joint model of community size and overlap.
    MeSH term(s) Bayes Theorem ; Uncertainty
    Language English
    Publishing date 2022-09-19
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010451
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  3. Article ; Online: Bayes-optimal estimation of overlap between populations of fixed size.

    Larremore, Daniel B

    PLoS computational biology

    2019  Volume 15, Issue 3, Page(s) e1006898

    Abstract: Measuring the overlap between two populations is, in principle, straightforward. Upon fully sampling both populations, the number of shared objects-species, taxonomical units, or gene variants, depending on the context-can be directly counted. In ... ...

    Abstract Measuring the overlap between two populations is, in principle, straightforward. Upon fully sampling both populations, the number of shared objects-species, taxonomical units, or gene variants, depending on the context-can be directly counted. In practice, however, only a fraction of each population's objects are likely to be sampled due to stochastic data collection or sequencing techniques. Although methods exists for quantifying population overlap under subsampled conditions, their bias is well documented and the uncertainty of their estimates cannot be quantified. Here we derive and validate a method to rigorously estimate the population overlap from incomplete samples when the total number of objects, species, or genes in each population is known, a special case of the more general β-diversity problem that is particularly relevant in the ecology and genomic epidemiology of malaria. By solving a Bayesian inference problem, this method takes into account the rates of subsampling and produces unbiased and Bayes-optimal estimates of overlap. In addition, it provides a natural framework for computing the uncertainty of its estimates, and can be used prospectively in study planning by quantifying the tradeoff between sampling effort and uncertainty.
    MeSH term(s) Bayes Theorem ; Plasmodium falciparum/genetics ; Population Density ; Reproducibility of Results ; Stochastic Processes ; Uncertainty
    Language English
    Publishing date 2019-03-29
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1006898
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  4. Article ; Online: Field-specific ability beliefs as an explanation for gender differences in academics' career trajectories: Evidence from public profiles on ORCID.Org.

    Hannak, Aniko / Joseph, Kenneth / Larremore, Daniel B / Cimpian, Andrei

    Journal of personality and social psychology

    2023  Volume 125, Issue 4, Page(s) 681–698

    Abstract: Academic fields exhibit substantial levels of gender segregation. Here, we investigated differences ... ...

    Abstract Academic fields exhibit substantial levels of gender segregation. Here, we investigated differences in
    MeSH term(s) Male ; Humans ; Female ; Sex Factors ; Occupations ; Sexism
    Language English
    Publishing date 2023-06-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 3103-3
    ISSN 1939-1315 ; 0022-3514
    ISSN (online) 1939-1315
    ISSN 0022-3514
    DOI 10.1037/pspa0000348
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    In stock of ZB MED Cologne/Königswinter

    Zs.B 1451: Show issues Location:
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  5. Article ; Online: Author Correction: Quantifying hierarchy and dynamics in US faculty hiring and retention.

    Wapman, K Hunter / Zhang, Sam / Clauset, Aaron / Larremore, Daniel B

    Nature

    2023  Volume 619, Issue 7970, Page(s) E49

    Language English
    Publishing date 2023-07-05
    Publishing country England
    Document type Published Erratum
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/s41586-023-06379-9
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    In stock of ZB MED Cologne/Königswinter

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  6. Article ; Online: Community detection in bipartite networks with stochastic block models.

    Yen, Tzu-Chi / Larremore, Daniel B

    Physical review. E

    2020  Volume 102, Issue 3-1, Page(s) 32309

    Abstract: In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type. This makes the stochastic block model (SBM), a highly ... ...

    Abstract In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type. This makes the stochastic block model (SBM), a highly flexible generative model for networks with block structure, an intuitive choice for bipartite community detection. However, typical formulations of the SBM do not make use of the special structure of bipartite networks. Here we introduce a Bayesian nonparametric formulation of the SBM and a corresponding algorithm to efficiently find communities in bipartite networks which parsimoniously chooses the number of communities. The biSBM improves community detection results over general SBMs when data are noisy, improves the model resolution limit by a factor of sqrt[2], and expands our understanding of the complicated optimization landscape associated with community detection tasks. A direct comparison of certain terms of the prior distributions in the biSBM and a related high-resolution hierarchical SBM also reveals a counterintuitive regime of community detection problems, populated by smaller and sparser networks, where nonhierarchical models outperform their more flexible counterpart.
    Language English
    Publishing date 2020-10-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2844562-4
    ISSN 2470-0053 ; 2470-0045
    ISSN (online) 2470-0053
    ISSN 2470-0045
    DOI 10.1103/PhysRevE.102.032309
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  7. Article ; Online: Labor advantages drive the greater productivity of faculty at elite universities.

    Zhang, Sam / Wapman, K Hunter / Larremore, Daniel B / Clauset, Aaron

    Science advances

    2022  Volume 8, Issue 46, Page(s) eabq7056

    Abstract: Faculty at prestigious institutions dominate scientific discourse, producing a disproportionate share of all research publications. Environmental prestige can drive such epistemic disparity, but the mechanisms by which it causes increased faculty ... ...

    Abstract Faculty at prestigious institutions dominate scientific discourse, producing a disproportionate share of all research publications. Environmental prestige can drive such epistemic disparity, but the mechanisms by which it causes increased faculty productivity remain unknown. Here, we combine employment, publication, and federal survey data for 78,802 tenure-track faculty at 262 PhD-granting institutions in the American university system to show through multiple lines of evidence that the greater availability of funded graduate and postdoctoral labor at more prestigious institutions drives the environmental effect of prestige on productivity. In particular, greater environmental prestige leads to larger faculty-led research groups, which drive higher faculty productivity, primarily in disciplines with group collaboration norms. In contrast, productivity does not increase substantially with prestige for faculty publications without group members or for group members themselves. The disproportionate scientific productivity of elite researchers can be largely explained by their substantial labor advantage rather than inherent differences in talent.
    Language English
    Publishing date 2022-11-18
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2810933-8
    ISSN 2375-2548 ; 2375-2548
    ISSN (online) 2375-2548
    ISSN 2375-2548
    DOI 10.1126/sciadv.abq7056
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  8. Article ; Online: Quantifying hierarchy and dynamics in US faculty hiring and retention.

    Wapman, K Hunter / Zhang, Sam / Clauset, Aaron / Larremore, Daniel B

    Nature

    2022  Volume 610, Issue 7930, Page(s) 120–127

    Abstract: Faculty hiring and retention determine the composition of the US academic workforce and directly shape educational ... ...

    Abstract Faculty hiring and retention determine the composition of the US academic workforce and directly shape educational outcomes
    MeSH term(s) Education, Graduate/statistics & numerical data ; Employment/statistics & numerical data ; Faculty/statistics & numerical data ; Female ; Humans ; Male ; Personnel Selection/statistics & numerical data ; Racial Groups/statistics & numerical data ; Socioeconomic Factors ; United States ; Universities/statistics & numerical data ; Women ; Workforce/statistics & numerical data
    Language English
    Publishing date 2022-09-21
    Publishing country England
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/s41586-022-05222-x
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 26: Show issues Location:
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    ab Jg. 2022: Lesesaal (EG)
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    Zs.MO 244: Show issues

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  9. Article ; Online: Optimizing prevalence estimates for a novel pathogen by reducing uncertainty in test characteristics.

    Larremore, Daniel B / Fosdick, Bailey K / Zhang, Sam / Grad, Yonatan H

    Epidemics

    2022  Volume 41, Page(s) 100634

    Abstract: Emergence of a novel pathogen drives the urgent need for diagnostic tests that can aid in defining disease prevalence. The limitations associated with rapid development and deployment of these tests result in a dilemma: In efforts to optimize prevalence ... ...

    Abstract Emergence of a novel pathogen drives the urgent need for diagnostic tests that can aid in defining disease prevalence. The limitations associated with rapid development and deployment of these tests result in a dilemma: In efforts to optimize prevalence estimates, would tests be better used in the lab to reduce uncertainty in test characteristics or to increase sample size in field studies? Here, we provide a framework to address this question through a joint Bayesian model that simultaneously analyzes lab validation and field survey data, and we define the impact of test allocation on inferences of sensitivity, specificity, and prevalence. In many scenarios, prevalence estimates can be most improved by apportioning additional effort towards validation rather than to the field. The joint model provides superior estimation of prevalence, sensitivity, and specificity, compared with typical analyses that model lab and field data separately, and it can be used to inform sample allocation when testing is limited.
    Language English
    Publishing date 2022-09-27
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2467993-8
    ISSN 1878-0067 ; 1755-4365
    ISSN (online) 1878-0067
    ISSN 1755-4365
    DOI 10.1016/j.epidem.2022.100634
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  10. Article ; Online: Modeling the effectiveness of olfactory testing to limit SARS-CoV-2 transmission.

    Larremore, Daniel B / Toomre, Derek / Parker, Roy

    Nature communications

    2021  Volume 12, Issue 1, Page(s) 3664

    Abstract: A central problem in the COVID-19 pandemic is that there is not enough testing to prevent infectious spread of SARS-CoV-2, causing surges and lockdowns with human and economic toll. Molecular tests that detect viral RNAs or antigens will be unable to ... ...

    Abstract A central problem in the COVID-19 pandemic is that there is not enough testing to prevent infectious spread of SARS-CoV-2, causing surges and lockdowns with human and economic toll. Molecular tests that detect viral RNAs or antigens will be unable to rise to this challenge unless testing capacity increases by at least an order of magnitude while decreasing turnaround times. Here, we evaluate an alternative strategy based on the monitoring of olfactory dysfunction, a symptom identified in 76-83% of SARS-CoV-2 infections-including those with no other symptoms-when a standardized olfaction test is used. We model how screening for olfactory dysfunction, with reflexive molecular tests, could be beneficial in reducing community spread of SARS-CoV-2 by varying testing frequency and the prevalence, duration, and onset time of olfactory dysfunction. We find that monitoring olfactory dysfunction could reduce spread via regular screening, and could reduce risk when used at point-of-entry for single-day events. In light of these estimated impacts, and because olfactory tests can be mass produced at low cost and self-administered, we suggest that screening for olfactory dysfunction could be a high impact and cost-effective method for broad COVID-19 screening and surveillance.
    MeSH term(s) Anosmia/diagnosis ; Anosmia/epidemiology ; Anosmia/virology ; COVID-19/etiology ; COVID-19/prevention & control ; COVID-19/transmission ; COVID-19 Nucleic Acid Testing ; Communicable Disease Control ; Cost-Benefit Analysis ; Humans ; Mass Screening/economics ; Mass Screening/methods ; Models, Theoretical ; Prevalence ; Time Factors ; Viral Load
    Language English
    Publishing date 2021-06-16
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-021-23315-5
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