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  1. AU=Poirier Canelle AU=Poirier Canelle
  2. AU="Joseph, Roberto"
  3. AU="Wenhong, Chen"
  4. AU="Alaverdyan, Zaruhi"
  5. AU="Steenland, Mathieu"
  6. AU="Ferreira, Regiane"
  7. AU="Coskun, Ozlem"
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  10. AU=Zou X AU=Zou X
  11. AU="Benyus, Janine"
  12. AU=Siddiqui Ruqaiyyah AU=Siddiqui Ruqaiyyah
  13. AU="Ruf, Hans-Georg"
  14. AU="Laginestra, Maria Antonella"
  15. AU="Novak, Jeff M"
  16. AU="Singh, Suvir"
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  27. AU="Chen, Zipeng"
  28. AU=Bilinski Alyssa
  29. AU="Honboh, Takuya"
  30. AU=Dobie David Robertson
  31. AU=Gagnon R F
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  33. AU=Freeman Alexandra F
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  1. Artikel ; Online: Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study.

    Poirier, Canelle / Bouzillé, Guillaume / Bertaud, Valérie / Cuggia, Marc / Santillana, Mauricio / Lavenu, Audrey

    JMIR public health and surveillance

    2023  Band 9, Seite(n) e34982

    Abstract: Background: Disease surveillance systems capable of producing accurate real-time and short-term forecasts can help public health officials design timely public health interventions to mitigate the effects of disease outbreaks in affected populations. In ...

    Abstract Background: Disease surveillance systems capable of producing accurate real-time and short-term forecasts can help public health officials design timely public health interventions to mitigate the effects of disease outbreaks in affected populations. In France, existing clinic-based disease surveillance systems produce gastroenteritis activity information that lags real time by 1 to 3 weeks. This temporal data gap prevents public health officials from having a timely epidemiological characterization of this disease at any point in time and thus leads to the design of interventions that do not take into consideration the most recent changes in dynamics.
    Objective: The goal of this study was to evaluate the feasibility of using internet search query trends and electronic health records to predict acute gastroenteritis (AG) incidence rates in near real time, at the national and regional scales, and for long-term forecasts (up to 10 weeks).
    Methods: We present 2 different approaches (linear and nonlinear) that produce real-time estimates, short-term forecasts, and long-term forecasts of AG activity at 2 different spatial scales in France (national and regional). Both approaches leverage disparate data sources that include disease-related internet search activity, electronic health record data, and historical disease activity.
    Results: Our results suggest that all data sources contribute to improving gastroenteritis surveillance for long-term forecasts with the prominent predictive power of historical data owing to the strong seasonal dynamics of this disease.
    Conclusions: The methods we developed could help reduce the impact of the AG peak by making it possible to anticipate increased activity by up to 10 weeks.
    Mesh-Begriff(e) Humans ; Electronic Health Records ; Disease Outbreaks ; Public Health/methods ; Internet ; France/epidemiology
    Sprache Englisch
    Erscheinungsdatum 2023-01-31
    Erscheinungsland Canada
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ISSN 2369-2960
    ISSN (online) 2369-2960
    DOI 10.2196/34982
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Higher training loads affect sleep in endurance runners: Can a high-heat-capacity mattress topper mitigate negative effects?

    Chauvineau, Maxime / Pasquier, Florane / Poirier, Canelle / Le Garrec, Sébastien / Duforez, François / Guilhem, Gaël / Nedelec, Mathieu

    Journal of sports sciences

    2023  Band 41, Heft 17, Seite(n) 1605–1616

    Abstract: This study investigates the impact of a training program on sleep among endurance runners and the benefits of chronically using a high-heat-capacity mattress topper (HMT). Twenty-one trained male athletes performed a 2-week usual training regimen, ... ...

    Abstract This study investigates the impact of a training program on sleep among endurance runners and the benefits of chronically using a high-heat-capacity mattress topper (HMT). Twenty-one trained male athletes performed a 2-week usual training regimen, sleeping on a Low-heat-capacity Mattress Topper (LMT), followed by 2-week overload and taper periods. From overload, participants were assigned into two groups based on the mattress topper used: HMT (
    Mesh-Begriff(e) Humans ; Male ; Hot Temperature ; Sleep ; Exercise ; Surveys and Questionnaires ; Nutritional Status
    Sprache Englisch
    Erscheinungsdatum 2023-12-21
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 995528-8
    ISSN 1466-447X ; 0264-0414
    ISSN (online) 1466-447X
    ISSN 0264-0414
    DOI 10.1080/02640414.2023.2285574
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States.

    Stolerman, Lucas M / Clemente, Leonardo / Poirier, Canelle / Parag, Kris V / Majumder, Atreyee / Masyn, Serge / Resch, Bernd / Santillana, Mauricio

    Science advances

    2023  Band 9, Heft 3, Seite(n) eabq0199

    Abstract: Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to ... ...

    Abstract Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to anticipate sharp increases in COVID-19 activity in U.S. counties. In a complementary direction to the efforts led by the Centers for Disease Control and Prevention (CDC), our models are designed to detect the time when an uptrend in COVID-19 activity will occur. Motivated by the need for finer spatial resolution epidemiological insights, we build upon previous efforts conceived at the state level. Our methods-tested in an out-of-sample manner, as events were unfolding, in 97 counties representative of multiple population sizes across the United States-frequently anticipated increases in COVID-19 activity 1 to 6 weeks before local outbreaks, defined when the effective reproduction number
    Sprache Englisch
    Erscheinungsdatum 2023-01-18
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2810933-8
    ISSN 2375-2548 ; 2375-2548
    ISSN (online) 2375-2548
    ISSN 2375-2548
    DOI 10.1126/sciadv.abq0199
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel ; Online: Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.

    Canelle Poirier / Yulin Hswen / Guillaume Bouzillé / Marc Cuggia / Audrey Lavenu / John S Brownstein / Thomas Brewer / Mauricio Santillana

    PLoS ONE, Vol 16, Iss 5, p e

    2021  Band 0250890

    Abstract: Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza ... ...

    Abstract Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by one to three weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the twelve continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.
    Schlagwörter Medicine ; R ; Science ; Q
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2021-01-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Artikel ; Online: Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.

    Liu, Dianbo / Clemente, Leonardo / Poirier, Canelle / Ding, Xiyu / Chinazzi, Matteo / Davis, Jessica / Vespignani, Alessandro / Santillana, Mauricio

    Journal of medical Internet research

    2020  Band 22, Heft 9, Seite(n) e23996

    Abstract: This corrects the article DOI: 10.2196/20285.]. ...

    Abstract [This corrects the article DOI: 10.2196/20285.].
    Schlagwörter covid19
    Sprache Englisch
    Erscheinungsdatum 2020-09-22
    Erscheinungsland Canada
    Dokumenttyp Published Erratum
    ZDB-ID 2028830-X
    ISSN 1438-8871 ; 1439-4456
    ISSN (online) 1438-8871
    ISSN 1439-4456
    DOI 10.2196/23996
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak

    Poirier, Canelle / Luo, Wei / Majumder, Maimuna / Liu, Dianbo / Mandl, Kenneth / Mooring, Todd / Santillana, Mauricio

    SSRN Electronic Journal ; ISSN 1556-5068

    An Initial Assessment in Two Spatial Scales.

    2020  

    Schlagwörter covid19
    Sprache Englisch
    Verlag Elsevier BV
    Erscheinungsland us
    Dokumenttyp Artikel ; Online
    DOI 10.2139/ssrn.3552677
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Artikel ; Online: Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.

    Poirier, Canelle / Hswen, Yulin / Bouzillé, Guillaume / Cuggia, Marc / Lavenu, Audrey / Brownstein, John S / Brewer, Thomas / Santillana, Mauricio

    PloS one

    2021  Band 16, Heft 5, Seite(n) e0250890

    Abstract: Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza ... ...

    Abstract Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by one to three weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the twelve continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.
    Mesh-Begriff(e) Computer Systems/statistics & numerical data ; Disease Outbreaks/statistics & numerical data ; Electronic Health Records ; Epidemiological Monitoring ; Forecasting/methods ; France/epidemiology ; Humans ; Influenza, Human/epidemiology ; Information Storage and Retrieval ; Internet ; Machine Learning ; Models, Statistical ; Public Health Surveillance/methods ; Social Media/statistics & numerical data ; Weather
    Sprache Englisch
    Erscheinungsdatum 2021-05-19
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0250890
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel ; Online: Screening and vaccination against COVID-19 to minimise school closure: a modelling study.

    Colosi, Elisabetta / Bassignana, Giulia / Contreras, Diego Andrés / Poirier, Canelle / Boëlle, Pierre-Yves / Cauchemez, Simon / Yazdanpanah, Yazdan / Lina, Bruno / Fontanet, Arnaud / Barrat, Alain / Colizza, Vittoria

    The Lancet. Infectious diseases

    2022  Band 22, Heft 7, Seite(n) 977–989

    Abstract: Background: Schools were closed extensively in 2020-21 to counter SARS-CoV-2 spread, impacting students' education and wellbeing. With highly contagious variants expanding in Europe, safe options to maintain schools open are urgently needed. By ... ...

    Abstract Background: Schools were closed extensively in 2020-21 to counter SARS-CoV-2 spread, impacting students' education and wellbeing. With highly contagious variants expanding in Europe, safe options to maintain schools open are urgently needed. By estimating school-specific transmissibility, our study evaluates costs and benefits of different protocols for SARS-CoV-2 control at school.
    Methods: We developed an agent-based model of SARS-CoV-2 transmission in schools. We used empirical contact data in a primary and a secondary school and data from pilot screenings in 683 schools during the alpha variant (B.1.1.7) wave in March-June, 2021, in France. We fitted the model to observed school prevalence to estimate the school-specific effective reproductive number for the alpha (R
    Findings: We estimated R
    Interpretation: The COVID-19 pandemic will probably continue to pose a risk to the safe and normal functioning of schools. Extending vaccination coverage in students, complemented by regular testing with good adherence, are essential steps to keep schools open when highly transmissible variants are circulating.
    Funding: EU Framework Programme for Research and Innovation Horizon 2020, Horizon Europe Framework Programme, Agence Nationale de la Recherche, ANRS-Maladies Infectieuses Émergentes.
    Mesh-Begriff(e) COVID-19/epidemiology ; COVID-19/prevention & control ; Child ; Humans ; Pandemics/prevention & control ; SARS-CoV-2/genetics ; Schools ; Vaccination
    Sprache Englisch
    Erscheinungsdatum 2022-04-01
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2061641-7
    ISSN 1474-4457 ; 1473-3099
    ISSN (online) 1474-4457
    ISSN 1473-3099
    DOI 10.1016/S1473-3099(22)00138-4
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Buch ; Online: Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States

    Stolerman, Lucas M. / Clemente, Leonardo / Poirier, Canelle / Parag, Kris V. / Majumder, Atreyee / Masyn, Serge / Resch, Bernd / Santillana, Mauricio

    2022  

    Abstract: The ongoing COVID-19 pandemic continues to affect communities around the world. To date, almost 6 million people have died as a consequence of COVID-19, and more than one-quarter of a billion people are estimated to have been infected worldwide. The ... ...

    Abstract The ongoing COVID-19 pandemic continues to affect communities around the world. To date, almost 6 million people have died as a consequence of COVID-19, and more than one-quarter of a billion people are estimated to have been infected worldwide. The design of appropriate and timely mitigation strategies to curb the effects of this and future disease outbreaks requires close monitoring of their spatio-temporal trajectories. We present machine learning methods to anticipate sharp increases in COVID-19 activity in US counties in real-time. Our methods leverage Internet-based digital traces -- e.g., disease-related Internet search activity from the general population and clinicians, disease-relevant Twitter micro-blogs, and outbreak trajectories from neighboring locations -- to monitor potential changes in population-level health trends. Motivated by the need for finer spatial-resolution epidemiological insights to improve local decision-making, we build upon previous retrospective research efforts originally conceived at the state level and in the early months of the pandemic. Our methods -- tested in real-time and in an out-of-sample manner on a subset of 97 counties distributed across the US -- frequently anticipated sharp increases in COVID-19 activity 1-6 weeks before the onset of local outbreaks (defined as the time when the effective reproduction number $R_t$ becomes larger than 1 consistently). Given the continued emergence of COVID-19 variants of concern -- such as the most recent one, Omicron -- and the fact that multiple countries have not had full access to vaccines, the framework we present, while conceived for the county-level in the US, could be helpful in countries where similar data sources are available.
    Schlagwörter Quantitative Biology - Quantitative Methods
    Erscheinungsdatum 2022-03-12
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Artikel: A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

    Dianbo Liu / Leonardo Clemente / Canelle Poirier / Xiyu Ding / Matteo Chinazzi / Jessica Davis T / Alessandro Vespignani / Mauricio Santillana

    Abstract: We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. ... ...

    Abstract We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. Specifically, our method is able to produce stable and accurate forecasts 2 days ahead of current time, and uses as inputs (a) official health reports from Chinese Center Disease for Control and Prevention (China CDC), (b) COVID-19-related internet search activity from Baidu, (c) news media activity reported by Media Cloud, and (d) daily forecasts of COVID-19 activity from GLEAM, an agent-based mechanistic model. Our machine-learning methodology uses a clustering technique that enables the exploitation of geo-spatial synchronicities of COVID-19 activity across Chinese provinces, and a data augmentation technique to deal with the small number of historical disease activity observations, characteristic of emerging outbreaks. Our model's predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces, and could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.
    Schlagwörter covid19
    Verlag arxiv
    Dokumenttyp Artikel
    Datenquelle COVID19

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