A Deep Recurrent Reinforced Learning model to compare the efficacy of targeted local vs. national measures on the spread of COVID-19 in the UK

ObjectivesWe have developed a deep learning model that provides predictions of the COVID-19 related number of cases and mortality in the upcoming 5 weeks and simulates the effect of policy changes targeting COVID-19 spread. MethodsWe developed a Deep Recurrent Reinforced Learning (DRRL) based model. The data used to train the DRRL model was based on various available datasets that have the potential to influence the trend in the number of COVID-19 cases and mortality. Analyses were performed based on the simulation of policy changes targeting COVID-19 spread, and the geographical representation of these effects. ResultsModel predictions of the number of cases and mortality of COVID-19 in the upcoming 5 weeks closely matched the actual values. Local lockdown with social distancing (LD_SD) was found to be ineffective compared to national lockdown. The ranking of effectiveness of supplementary measures for LD_SD were found to be consistent across national hotspots and local areas. Measure effectiveness were ranked from most effective to least effective: 1) full lockdown; 2) LD_SD with international travel -50%; 3) LD_SD with 100% quarantine; 4) LD_SD with closing school -50%; 5) LD_SD with closing pubs -50%. There were negligible differences observed between LD_SD, LD_SD with -50% food & Accommodation and LD_SD with -50% Retail. ConclusionsThe second national lockdown should be followed by measures which are more effective than LD_SD alone. Our model suggests the importance of restrictions on international travel and travel quarantines, thus suggesting that follow-up policies should consist of the combination of LD_SD and a reduction in the number of open airports within close proximity of the hotspot regions. Stricter measures should be placed in terms travel quarantine to increase the impact of this measure. It is also recommended that restrictions should be placed on the number of schools and pubs open. O_TEXTBOXStrengths and limitations of this study - The proposed Deep Recurrent Reinforced Learning (DRRL)-based model takes into account of both relationships of variables across local authorities and across time, using ideas from reinforcement learning to improve predictions. - Whilst, predicting the geographical trend in COVID-19 cases based on the simulation of different measures in the UK at both the national and local levels in the UK has proved challenging, this study has provided a methodology by which useful predictions and simulations can be obtained. - The Office for National Statistics only released data on UK international travel up to March 2019 at the time of this study, and therefore this study used the amount of UK tourists in Spain as a reference variable for understanding the effect of international travel on COVID-19 spread. C_TEXTBOX

A Nationwide Survey of UK cardiac surgeons view on clinical decision making during the COVID-19 pandemic

BackgroundNo firm recommendations are currently available to guide decision making for patients requiring cardiac surgery during the COVID-19 pandemic. Systematic appraisal of national expert consensus can be used to generate interim recommendations until data from clinical observations will become available. Hence, we aimed to collect and quantitatively appraise nationwide UK senior surgeons opinion on clinical decision making for patients requiring cardiac surgery during the COVID-19 pandemic. MethodsWe mailed a web-based questionnaire to all consultant cardiac surgeons through the Society for Cardiothoracic Surgery in Great Britain and Ireland (SCTS) mailing list on the 17th April 2020 and we pre-determined to close the survey on the 21st April 2020. This survey was primarily designed to gather information on UK surgeons opinion using 12 items. Strong consensus was predefined as an opinion shared by at least 60% of responding consultants. ResultsA total of 86 consultant surgeons undertook the survey. All UK cardiac units were represented by at least one consultant. Strong consensus was achieved for the following key questions:1) before hospital admission every patient should receive nasopharyngeal swab, PCR and chest CT; 2) the use of full PPE should to be adopted in every case by the theatre team regardless patients COVID-19 status; 3) the risk of COVID-19 exposure for patients undergoing heart surgery should be considered moderate to high and likely to increase mortality if it occurs; 4) cardiac procedure should be decided based on ad-hoc multidisciplinary team discussion for every patient. The majority believed that both aortic and mitral surgery should be considered in selected cases. The role of CABG surgery during the pandemic was more controversial. ConclusionsIn the current unprecedented scenario, the present survey provides information for generating interim recommendations until data from clinical observations will become available. Perspective statementSystematic appraisal of national expert consensus can be used to generate interim recommendations for patients undergoing cardiac surgery during COVID-19 pandemic until data from clinical observations will become available. Central messageNo firm recommendations are currently available to guide decision making for patients requiring cardiac surgery during the pandemic. This can translate into significant variability in clinical practice and patients outcomes across cardiac units. Systematic appraisal of national expert consensus can represent a rapid and efficient instrument to provide support to heath policy makers and other stakeholders in generating interim recommendations until data from clinical observations will become available.