LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 31

Search options

  1. Article ; Online: Using a Dynamic Causal Model to validate previous predictions and offer a 12-month forecast of the long-term effects of the COVID-19 epidemic in the UK.

    Bowie, Cam / Friston, Karl

    Frontiers in public health

    2023  Volume 10, Page(s) 1108886

    Abstract: Background: Predicting the future UK COVID-19 epidemic provides a baseline of a vaccine-only mitigation policy from which to judge the effects of additional public health interventions. A previous 12-month prediction of the size of the epidemic to ... ...

    Abstract Background: Predicting the future UK COVID-19 epidemic provides a baseline of a vaccine-only mitigation policy from which to judge the effects of additional public health interventions. A previous 12-month prediction of the size of the epidemic to October 2022 underestimated its sequelae by a fifth. This analysis seeks to explain the reasons for the underestimation before offering new long-term predictions.
    Methods: A Dynamic Causal Model was used to identify changes in COVID-19 transmissibility and the public's behavioral response in the 12-months to October 2022. The model was then used to predict the future trends in infections, long-COVID, hospital admissions and deaths over 12-months to October 2023.
    Findings: The model estimated that the secondary attack rate increased from 0.4 to 0.5, the latent period shortened from 2.7 to 2.6 and the incubation period shortened from 2.0 to 1.95 days between October 2021 and October 2022. During this time the model also estimated that antibody immunity waned from 177 to 160 days and T-cell immunity from 205 to 180 days. This increase in transmissibility was associated with a reduction in pathogenicity with the proportion of infections developing acute respiratory distress syndrome falling for 6-2% in the same twelve-month period. Despite the wave of infections, the public response was to increase the tendency to expose themselves to a high-risk environment (e.g., leaving home) each day from 33-58% in the same period.The predictions for October 2023 indicate a wave of infections three times larger this coming year than last year with significant health and economic consequences such as 120,000 additional COVID-19 related deaths, 800,000 additional hospital admissions and 3.5 million people suffering acute-post-COVID-19 syndrome lasting more than 12 weeks.
    Interpretation: The increase in transmissibility together with the public's response provide plausible explanations for why the model underestimated the 12-month predictions to October 2022. The 2023 projection could well-underestimate the predicted substantial next wave of COVID-19 infection. Vaccination alone will not control the epidemic. The UK COVID-19 epidemic is not over. The results call for investment in precautionary public health interventions.
    MeSH term(s) Humans ; COVID-19/epidemiology ; Post-Acute COVID-19 Syndrome ; Epidemics ; Models, Theoretical ; United Kingdom/epidemiology
    Language English
    Publishing date 2023-01-06
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2711781-9
    ISSN 2296-2565 ; 2296-2565
    ISSN (online) 2296-2565
    ISSN 2296-2565
    DOI 10.3389/fpubh.2022.1108886
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  2. Article ; Online: Modelling the effect of an improved trace and isolate system in the wake of a highly transmissible Covid-19 variant with potential vaccine escape

    Bowie, Cam

    medRxiv

    Abstract: Objective: How helpful would a properly functioning find, test, trace, isolate and support (FTTIS) system be now in the UK with new Covid19 infections at a low level and half the adult population immunised but with a highly transmissible variant becoming ...

    Abstract Objective: How helpful would a properly functioning find, test, trace, isolate and support (FTTIS) system be now in the UK with new Covid19 infections at a low level and half the adult population immunised but with a highly transmissible variant becoming predominant? Design: a dynamic causal model of Covid-19 supplied with the latest available empirical data is used to assess the impact of a new highly transmissible variant. Setting: the United Kingdom. Participants: a population based study. Interventions: scenarios are used to explore a Covid-19 transmission rate 50% more and twice the current rate with or without a more effective FTTIS system. Main outcome measures: incidence, death rate and reproductive ratio Results: a small short third wave of infections occurs which does not occur if FTTIS effectiveness is improved from 25% to 30%. Conclusion: a modest improvement in FTTIS would prevent a third wave caused by a highly transmissible virus.
    Keywords covid19
    Language English
    Publishing date 2021-06-10
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.06.07.21258451
    Database COVID19

    Kategorien

  3. Article ; Online: A 12-month projection to September 2022 of the COVID-19 epidemic in the UK using a dynamic causal model.

    Bowie, Cam / Friston, Karl

    Frontiers in public health

    2022  Volume 10, Page(s) 999210

    Abstract: Objectives: Predicting the future UK COVID-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine program.: Methods: A Dynamic Causal Model was used to estimate key model ... ...

    Abstract Objectives: Predicting the future UK COVID-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine program.
    Methods: A Dynamic Causal Model was used to estimate key model parameters of the UK epidemic, such as vaccine effectiveness and increased transmissibility of Alpha and Delta variants, the effectiveness of the vaccine program roll-out and changes in contact rates. The model predicts the future trends in infections, long-COVID, hospital admissions and deaths.
    Results: Two-dose vaccination given to 66% of the UK population prevents transmission following infection by 44%, serious illness by 86% and death by 93%. Despite this, with no other public health measures used, cases will increase from 37 million to 61 million, hospital admissions from 536,000 to 684,000 and deaths from 136,000 to 142,000 over 12 months. A retrospective analysis (conducted after the original submission of this report) allowed a comparison of these predictions of morbidity and mortality with actual outcomes.
    Conclusion: Vaccination alone will not control the epidemic. Relaxation of mitigating public health measures carries several risks, which include overwhelming the health services, the creation of vaccine resistant variants and the economic cost of huge numbers of acute and chronic cases.
    MeSH term(s) Humans ; COVID-19/epidemiology ; Retrospective Studies ; SARS-CoV-2 ; United Kingdom/epidemiology ; Post-Acute COVID-19 Syndrome
    Language English
    Publishing date 2022-11-15
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2711781-9
    ISSN 2296-2565 ; 2296-2565
    ISSN (online) 2296-2565
    ISSN 2296-2565
    DOI 10.3389/fpubh.2022.999210
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  4. Article ; Online: A 12-month projection to September 2022 of the COVID-19 epidemic in the UK using a dynamic causal model

    Cam Bowie / Karl Friston

    Frontiers in Public Health, Vol

    2022  Volume 10

    Abstract: ObjectivesPredicting the future UK COVID-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine program.MethodsA Dynamic Causal Model was used to estimate key model parameters of ... ...

    Abstract ObjectivesPredicting the future UK COVID-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine program.MethodsA Dynamic Causal Model was used to estimate key model parameters of the UK epidemic, such as vaccine effectiveness and increased transmissibility of Alpha and Delta variants, the effectiveness of the vaccine program roll-out and changes in contact rates. The model predicts the future trends in infections, long-COVID, hospital admissions and deaths.ResultsTwo-dose vaccination given to 66% of the UK population prevents transmission following infection by 44%, serious illness by 86% and death by 93%. Despite this, with no other public health measures used, cases will increase from 37 million to 61 million, hospital admissions from 536,000 to 684,000 and deaths from 136,000 to 142,000 over 12 months. A retrospective analysis (conducted after the original submission of this report) allowed a comparison of these predictions of morbidity and mortality with actual outcomes.ConclusionVaccination alone will not control the epidemic. Relaxation of mitigating public health measures carries several risks, which include overwhelming the health services, the creation of vaccine resistant variants and the economic cost of huge numbers of acute and chronic cases.
    Keywords Coronavirus ; post-acute COVID-19 syndrome ; incidence ; vaccine effectiveness ; compartmental models ; public health methodology ; Public aspects of medicine ; RA1-1270
    Subject code 360
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  5. Article ; Online: Exit strategy to control covid-19 and relaunch the economy.

    Bowie, Cam / Hill, Tony

    BMJ (Clinical research ed.)

    2020  Volume 369, Page(s) m1851

    MeSH term(s) Betacoronavirus ; COVID-19 ; Commerce ; Communicable Disease Control ; Contact Tracing ; Coronavirus Infections/economics ; Coronavirus Infections/epidemiology ; Health Policy ; Humans ; Infection Control/methods ; Pandemics/economics ; Pneumonia, Viral/economics ; Pneumonia, Viral/epidemiology ; Public Health ; Quarantine/economics ; Quarantine/methods ; SARS-CoV-2 ; United Kingdom/epidemiology
    Keywords covid19
    Language English
    Publishing date 2020-05-11
    Publishing country England
    Document type Letter
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj.m1851
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  6. Book ; Online: Exit strategy to control covid-19 and relaunch the economy

    Bowie, Cam / Hill, Tony

    2020  

    Keywords LETTERS ; covid19
    Language English
    Publishing date 2020-05-11 05:36:09.0
    Publisher BMJ Publishing Group Ltd
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  7. Article ; Online: A twelve-month projection to September 2022 of the Covid-19 epidemic in the UK using a Dynamic Causal Model

    Bowie, Cam / Friston, Karl

    medRxiv

    Abstract: Objectives Predicting the future UK Covid-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine programme. Methods A Dynamic Causal Model (DCM) is used to estimate the model ... ...

    Abstract Objectives Predicting the future UK Covid-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine programme. Methods A Dynamic Causal Model (DCM) is used to estimate the model parameters of the epidemic such as vaccine effectiveness and increased transmissibility of alpha and delta variants, the vaccine programme roll-out and changes in contact rates. The model predicts the future trends in infections, long-Covid, hospital admissions and deaths. Results Two dose vaccination given to 66% of the UK population prevents transmission following infection by 44%, serious illness by 86% and death by 93%. Despite this, with no other public health measures used, cases will increase from 37 million to 61 million, hospital admission from 536,000 to 684,000 and deaths from 136,000 to 142,000 over twelve months. Discussion Vaccination alone will not control the epidemic. Relaxation of mitigating public health measures carries several risks including overwhelming the health services, the creation of vaccine resistant variants and the economic cost of huge numbers of acute and chronic cases.
    Keywords covid19
    Language English
    Publishing date 2021-10-07
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.10.04.21262827
    Database COVID19

    Kategorien

  8. Book ; Online: CAManim

    Kaczmarek, Emily / Miguel, Olivier X. / Bowie, Alexa C. / Ducharme, Robin / Dingwall-Harvey, Alysha L. J. / Hawken, Steven / Armour, Christine M. / Walker, Mark C. / Dick, Kevin

    Animating end-to-end network activation maps

    2023  

    Abstract: ... to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network ... at the final layer activation. Herein, we demonstrate that CAManim works with any CAM-based method and various ... literature, including a collection of methods denoted Class Activation Maps (CAMs), that seek to demystify ...

    Abstract Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users. This challenge arises due to the absence of interpretable and transparent tools to make sense of black-box models. There exists a growing body of Explainable Artificial Intelligence (XAI) literature, including a collection of methods denoted Class Activation Maps (CAMs), that seek to demystify what representations the model learns from the data, how it informs a given prediction, and why it, at times, performs poorly in certain tasks. We propose a novel XAI visualization method denoted CAManim that seeks to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network activation maps through all layers, effectively depicting from end-to-end how a model progressively arrives at the final layer activation. Herein, we demonstrate that CAManim works with any CAM-based method and various CNN architectures. Beyond qualitative model assessments, we additionally propose a novel quantitative assessment that expands upon the Remove and Debias (ROAD) metric, pairing the qualitative end-to-end network visual explanations assessment with our novel quantitative "yellow brick ROAD" assessment (ybROAD). This builds upon prior research to address the increasing demand for interpretable, robust, and transparent model assessment methodology, ultimately improving an end-user's trust in a given model's predictions.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-12-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  9. Article ; Online: Safety of prenatal opioid analgesics: Do results differ between public health insurance beneficiary and population-based cohorts?

    Brogly, Susan B / Bowie, Alexa C / Li, Wenbin / Camden, Andi / Velez, Maria P / Guttmann, Astrid / Werler, Martha M

    Birth defects research

    2023  Volume 115, Issue 5, Page(s) 555–562

    Abstract: Background: Pregnant patients with particular types of health insurance may have distinct demographic and medical characteristics that have a biologic effect on associations between opioid analgesics and congenital anomalies (CA).: Methods: We ... ...

    Abstract Background: Pregnant patients with particular types of health insurance may have distinct demographic and medical characteristics that have a biologic effect on associations between opioid analgesics and congenital anomalies (CA).
    Methods: We followed 199,884 pregnant prescription beneficiaries in Ontario, Canada (1996-2018). Opioid analgesics dispensed in the first trimester and CA were identified from universal-access administrative health records. We estimated propensity score adjusted risk ratios (RR) between first trimester exposure and CA (any, major, minor, specific). RRs were compared to those published from an Ontario population-based cohort (N = 599,579, 2013-2018).
    Results: 15,724 (7.9%) were exposed to first trimester opioid analgesics, mainly codeine (58.1%) or oxycodone (21.3%); CA prevalence in exposed was 3.1%. RRs in the beneficiary cohort appeared higher than the population-based cohort for any CA with hydromorphone (RR = 2.34, 95% CI: 1.65, 3.30) and oxycodone (RR = 1.73, 95% CI: 1.46, 2.05) and major CA with hydromorphone (RR = 2.74, 95% CI: 1.91, 3.94) and oxycodone (RR = 1.72, 95% CI: 1.42, 2.08). Other RRs that appeared higher in the beneficiary cohort included cardiovascular (codeine, oxycodone), gastrointestinal (oxycodone), musculoskeletal (any, hydromorphone, oxycodone), CNS (oxycodone), chromosomal (codeine), and neoplasm and tumor (oxycodone) anomalies. The beneficiary cohort had higher opioid doses, was younger, had lower socioeconomic status, and greater comorbidities.
    Conclusions: Increased risks of CA after first trimester opioid analgesics were observed in low-income prescription beneficiaries, and some estimates were higher than a population-based cohort from the same setting. Biological differences associated with younger age, lower socioeconomic status and greater comorbidity may affect generalizability of results from pregnant low-income beneficiaries.
    MeSH term(s) Pregnancy ; Female ; Humans ; Analgesics, Opioid ; Oxycodone ; Hydromorphone ; Insurance Benefits ; Public Health ; Codeine
    Chemical Substances Analgesics, Opioid ; Oxycodone (CD35PMG570) ; Hydromorphone (Q812464R06) ; Codeine (UX6OWY2V7J)
    Language English
    Publishing date 2023-01-11
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2104792-3
    ISSN 2472-1727
    ISSN (online) 2472-1727
    DOI 10.1002/bdr2.2147
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

To top