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  1. Article ; Online: Improving epidemic surveillance and response: big data is dead, long live big data.

    Buckee, Caroline

    The Lancet. Digital health

    2020  Volume 2, Issue 5, Page(s) e218–e220

    MeSH term(s) Big Data ; Epidemics ; Humans ; Information Dissemination ; Population Surveillance
    Keywords covid19
    Language English
    Publishing date 2020-03-17
    Publishing country England
    Document type Journal Article
    ISSN 2589-7500
    ISSN (online) 2589-7500
    DOI 10.1016/S2589-7500(20)30059-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Genomic framework for malaria parasites: challenging but necessary.

    Camponovo, Flavia / Buckee, Caroline O / Taylor, Aimee R

    Trends in parasitology

    2023  Volume 39, Issue 4, Page(s) 231

    MeSH term(s) Animals ; Humans ; Parasites ; Malaria/prevention & control ; Plasmodium falciparum/genetics ; Genomics
    Language English
    Publishing date 2023-02-17
    Publishing country England
    Document type Letter ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Comment
    ZDB-ID 2036227-4
    ISSN 1471-5007 ; 1471-4922
    ISSN (online) 1471-5007
    ISSN 1471-4922
    DOI 10.1016/j.pt.2023.01.011
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Making data for good better.

    Buckee, Caroline / Balsari, Satchit / Schroeder, Andrew

    PLOS digital health

    2022  Volume 1, Issue 1, Page(s) e0000010

    Language English
    Publishing date 2022-01-18
    Publishing country United States
    Document type Journal Article
    ISSN 2767-3170
    ISSN (online) 2767-3170
    DOI 10.1371/journal.pdig.0000010
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  5. Article ; Online: Measurably recombining malaria parasites.

    Camponovo, Flavia / Buckee, Caroline O / Taylor, Aimee R

    Trends in parasitology

    2022  Volume 39, Issue 1, Page(s) 17–25

    Abstract: Genomic epidemiology has guided research and policy for various viral pathogens and there has been a parallel effort towards using genomic epidemiology to combat diseases that are caused by eukaryotic pathogens, such as the malaria parasite. However, the ...

    Abstract Genomic epidemiology has guided research and policy for various viral pathogens and there has been a parallel effort towards using genomic epidemiology to combat diseases that are caused by eukaryotic pathogens, such as the malaria parasite. However, the central concept of viral genomic epidemiology, namely that of measurably mutating pathogens, does not apply easily to sexually recombining parasites. Here we introduce the related but different concept of measurably recombining malaria parasites to promote convergence around a unifying theoretical framework for malaria genomic epidemiology. Akin to viral phylodynamics, we anticipate that an inferential framework developed around recombination will help guide practical research and thus realize the full public health potential of genomic epidemiology for malaria parasites and other sexually recombining pathogens.
    MeSH term(s) Animals ; Humans ; Parasites ; Malaria/epidemiology ; Malaria/prevention & control ; Genomics ; Eukaryota
    Language English
    Publishing date 2022-11-23
    Publishing country England
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2036227-4
    ISSN 1471-5007 ; 1471-4922
    ISSN (online) 1471-5007
    ISSN 1471-4922
    DOI 10.1016/j.pt.2022.11.002
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  6. Article ; Online: Individual model forecasts can be misleading, but together they are useful.

    Buckee, Caroline O / Johansson, Michael A

    European journal of epidemiology

    2020  Volume 35, Issue 8, Page(s) 731–732

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections/epidemiology ; Disease Outbreaks/prevention & control ; Forecasting/methods ; Humans ; Models, Statistical ; Models, Theoretical ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; SARS-CoV-2 ; United States/epidemiology
    Keywords covid19
    Language English
    Publishing date 2020-08-11
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 632614-6
    ISSN 1573-7284 ; 0393-2990
    ISSN (online) 1573-7284
    ISSN 0393-2990
    DOI 10.1007/s10654-020-00667-8
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  7. Article ; Online: Wrong but Useful - What Covid-19 Epidemiologic Models Can and Cannot Tell Us.

    Holmdahl, Inga / Buckee, Caroline

    The New England journal of medicine

    2020  Volume 383, Issue 4, Page(s) 303–305

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/immunology ; Coronavirus Infections/transmission ; Forecasting ; Humans ; Models, Statistical ; Models, Theoretical ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/immunology ; Pneumonia, Viral/transmission ; SARS-CoV-2 ; Uncertainty
    Keywords covid19
    Language English
    Publishing date 2020-05-15
    Publishing country United States
    Document type Journal Article ; Video-Audio Media
    ZDB-ID 207154-x
    ISSN 1533-4406 ; 0028-4793
    ISSN (online) 1533-4406
    ISSN 0028-4793
    DOI 10.1056/NEJMp2016822
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  8. Article ; Online: Making data for good better.

    Caroline Buckee / Satchit Balsari / Andrew Schroeder

    PLOS Digital Health, Vol 1, Iss 1, p e

    2022  Volume 0000010

    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Language English
    Publishing date 2022-01-01T00:00:00Z
    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: Data in Crisis - Rethinking Disaster Preparedness in the United States.

    Balsari, Satchit / Kiang, Mathew V / Buckee, Caroline O

    The New England journal of medicine

    2021  Volume 385, Issue 16, Page(s) 1526–1530

    MeSH term(s) COVID-19 ; Data Mining ; Disaster Planning/methods ; Humans ; Information Dissemination/legislation & jurisprudence ; Natural Disasters ; United States
    Language English
    Publishing date 2021-09-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 207154-x
    ISSN 1533-4406 ; 0028-4793
    ISSN (online) 1533-4406
    ISSN 0028-4793
    DOI 10.1056/NEJMms2104654
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  10. Article ; Online: Deconstructing the parasite multiplication rate of Plasmodium falciparum.

    Gnangnon, Bénédicte / Duraisingh, Manoj T / Buckee, Caroline O

    Trends in parasitology

    2021  Volume 37, Issue 10, Page(s) 922–932

    Abstract: Epidemiological indicators describing population-level malaria transmission dynamics are widely used to guide policy recommendations. However, the determinants of malaria outcomes within individuals are still poorly understood. This conceptual gap partly ...

    Abstract Epidemiological indicators describing population-level malaria transmission dynamics are widely used to guide policy recommendations. However, the determinants of malaria outcomes within individuals are still poorly understood. This conceptual gap partly reflects the fact that there are few indicators that robustly predict the trajectory of individual infections or clinical outcomes. The parasite multiplication rate (PMR) is a widely used indicator for the Plasmodium intraerythrocytic development cycle (IDC), for example, but its relationship to clinical outcomes is complex. Here, we review its calculation and use in P. falciparum malaria research, as well as the parasite and host factors that impact it. We also provide examples of metrics that can help to link within-host dynamics to malaria clinical outcomes when used alongside the PMR.
    MeSH term(s) Animals ; Erythrocytes/parasitology ; Host-Parasite Interactions ; Humans ; Malaria, Falciparum/parasitology ; Plasmodium falciparum/growth & development ; Protozoan Proteins/metabolism
    Chemical Substances Protozoan Proteins
    Language English
    Publishing date 2021-06-10
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2036227-4
    ISSN 1471-5007 ; 1471-4922
    ISSN (online) 1471-5007
    ISSN 1471-4922
    DOI 10.1016/j.pt.2021.05.001
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