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1.
Comput Biol Med ; 138: 104868, 2021 11.
Article in English | MEDLINE | ID: covidwho-1401386

ABSTRACT

COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the most interesting and widely used. However, only using artificial intelligence methods for prediction cannot capture the time change pattern of the transmission of infectious diseases. To solve this problem, this paper proposes a COVID-19 prediction model based on time-dependent SIRVD by using deep learning. This model combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the mathematical model of infectious diseases by fusing deep learning models such as LSTM and other time prediction methods. In the current situation of mass vaccination, we analyzed COVID-19 data from January 15, 2021, to May 27, 2021 in seven countries - India, Argentina, Brazil, South Korea, Russia, the United Kingdom, France, Germany, and Italy. The experimental results show that the prediction model not only has a 50% improvement in single-day predictions compared to pure deep learning methods, but also can be adapted to short- and medium-term predictions, which makes the overall prediction more interpretable and robust.


Subject(s)
COVID-19 , Deep Learning , Artificial Intelligence , Humans , Neural Networks, Computer , SARS-CoV-2
2.
Front Public Health ; 9: 628333, 2021.
Article in English | MEDLINE | ID: covidwho-1247940

ABSTRACT

Introduction: High levels of physical, cognitive, and psychosocial impairments are anticipated for those recovering from the COVID-19. In the UK, ~50% of survivors will require additional rehabilitation. Despite this, there is currently no evidence-based guideline available in England and Wales that addresses the identification, timing and nature of effective interventions to manage the morbidity associated following COVID-19. It is now timely to accelerate the development and evaluation of a rehabilitation service to support patients and healthcare services. Nuffield Health have responded by configuring a scalable rehabilitation pathway addressing the immediate requirements for those recovering from COVID-19 in the community. Methods and Analysis: This long-term evaluation will examine the effectiveness of a 12-week community rehabilitation programme for COVID-19 patients who have been discharged following in-patient treatment. Consisting of two distinct 6-week phases; Phase 1 is an entirely remote service, delivered via digital applications. Phase 2 sees the same patients transition into a gym-based setting for supervised group-based rehabilitation. Trained rehabilitation specialists will coach patients across areas such as goal setting, exercise prescription, symptom management and emotional well-being. Outcomes will be collected at 0, 6, and 12 weeks and at 6- and 12-months. Primary outcome measures will assess changes in health-related quality of life (HR-QOL) and COVID-19 symptoms using EuroQol Five Dimension Five Level Version (EQ-5D-5L) and Dyspnea-12, respectively. Secondary outcome measures of the Duke Activity Status Questionnaire (DASI), 30 s sit to stand test, General Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Patient Experience Questionnaire (PEQ) and Quality Adjusted Life Years (QALY) will allow for the evaluation of outcomes, mediators and moderators of outcome, and cost-effectiveness of treatment. Discussion: This evaluation will investigate the immediate and long-term impact, as well as the cost effectiveness of a blended rehabilitation programme for COVID-19 survivors. This evaluation will provide a founding contribution to the literature, evaluating one of the first programmes of this type in the UK. The evaluation has international relevance, with the potential to show how a new model of service provision can support health services in the wake of COVID-19. Trial Registration: Current Trials ISRCTN ISRCTN14707226 Web: http://www.isrctn.com/ISRCTN14707226.


Subject(s)
COVID-19 , Quality of Life , England/epidemiology , Humans , SARS-CoV-2 , Wales
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