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      Calling for pan-European commitment for rapid and sustained reduction in SARS-CoV-2 infections

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          Abstract

          Across Europe, the COVID-19 pandemic is causing excess deaths, placing a burden on societies and health systems and harming the economy. European governments have yet to develop a common vision to guide the management of the pandemic. Overwhelming evidence shows that not only public health, but also society and the economy benefit greatly from reducing cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Vaccines will help control the virus, but not until late 2021. If European governments do not act now, further waves of infection are to be expected, with consequential damage to health, society, jobs, and businesses. With open borders across Europe, a single country alone cannot keep the number of COVID-19 cases low; joint action and common goals among countries are therefore essential. We therefore call for a strong, coordinated European response and clearly defined goals for the medium and long term. Achieving and maintaining low case numbers should be the common, pan-European goal for the following reasons. First, low case numbers save lives, and fewer people will die or suffer from long-term effects of COVID-19. In addition, medical resources will not be diverted from other patients in need. Second, low case numbers save jobs and businesses. The economic impact of COVID-19 is driven by viral circulation within the population, and economies can and will recover quickly once the virus is greatly reduced or eliminated. China and Australia have shown this is possible. In contrast, the economic costs of lockdowns increase with their duration. 1 Third, the control of the spread is most effective at low case numbers. Easing restrictions while accepting higher case numbers is a short-sighted strategy that will lead to another wave, and thus to higher costs for society as a whole. Testing and tracing capacities are limited: only with sufficiently low case numbers can the test–trace–isolate–support strategy quickly and efficiently help mitigate the spread.2, 3 Hence, milder and more targeted physical distancing measures are sufficient, and schools and businesses can stay open. Fourth, contact tracing and quarantine is not feasible at high infection prevalence. Assuming a state with 300 new cases per million per day, ten contacts per case, and 10 days quarantine: then 3% of the population would need to be in quarantine, resulting in strong reductions of the workforce. Fifth, aiming for naturally acquired population immunity is not an option. 4 The heavy burden in terms of morbidity and mortality, reflected also in the current excess mortality, and the uncertain duration of immunity should strongly discourage this approach. Sixth, planning is possible. When case numbers are low, there is no need for rapid policy changes. This reduces the economic damage and the uncertainty and strain on mental health. However, if case numbers rise too high, preventive measures must be taken decisively to bring them down again—and the earlier, the better.5, 6, 7 To better manage the COVID-19 pandemic, we propose a strategy with three core elements (panel ). Panel A joint European strategy for the COVID-19 pandemic 1 Achieve low case numbers (i) Aim for a target of no more than ten new COVID-19 cases per million people per day. This target has been reached in many countries, and can be reached again throughout Europe by spring, 2021, at the latest. (ii) Take firm action to reduce case numbers quickly. Strong interventions have proven efficient and balance the rapid achievement of low case numbers against the strain on mental health and the economy. (iii) To avoid a ping-pong effect of importing and reimporting severe acute respiratory syndrome coronavirus 2 infections, the reduction should be synchronised across all European countries and start as soon as possible. This synchronisation will allow European borders to stay open. 2 Keep case numbers low (i) When case numbers are low, easing of restrictions is possible but should be carefully monitored. Continue and improve targeted mitigation measures, such as mask wearing, hygiene, moderate contact reduction, testing, and contact tracing. (ii) Even if case numbers are low, a strategy for surveillance testing (at the very least 300 tests per million people per day) should be in place so that an increase in case numbers can be detected in time. (iii) Local outbreaks require a rapid and rigorous response, including travel restrictions, targeted testing, and possibly regional lockdowns, to achieve a rapid reduction in prevalence. 3 Develop a longer-term common vision Develop context-sensitive regional and national action plans as well as European-level goals, depending on the COVID-19 prevalence. Devise strategies for elimination, screening, vaccination, protection of those at high risk, and support for those most affected by the COVID-19 pandemic. 8 It is crucial to communicate the goal and the advantage of low case numbers clearly to foster public cooperation. The success of these measures depends crucially on the cooperation and involvement of the public. Making the case for the economic and social benefits of reducing case numbers will, if clearly communicated, greatly foster public cooperation. Controlling COVID-19 will become easier. In the near future, increased immunisation, more testing, and an improved understanding of mitigation strategies will further facilitate the control of COVID-19. We urge governments throughout Europe to agree on clearly formulated common goals, coordinate their efforts, develop regionally adapted strategies to reach the goals, and thereby work resolutely towards low case numbers.

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          Most cited references7

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          Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy

          In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.
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            Ranking the effectiveness of worldwide COVID-19 government interventions

            Assessing the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate the spread of SARS-CoV-2 is critical to inform future preparedness response plans. Here we quantify the impact of 6,068 hierarchically coded NPIs implemented in 79 territories on the effective reproduction number, Rt, of COVID-19. We propose a modelling approach that combines four computational techniques merging statistical, inference and artificial intelligence tools. We validate our findings with two external datasets recording 42,151 additional NPIs from 226 countries. Our results indicate that a suitable combination of NPIs is necessary to curb the spread of the virus. Less disruptive and costly NPIs can be as effective as more intrusive, drastic, ones (for example, a national lockdown). Using country-specific 'what-if' scenarios, we assess how the effectiveness of NPIs depends on the local context such as timing of their adoption, opening the way for forecasting the effectiveness of future interventions.
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              Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study

              Summary Background In countries with declining numbers of confirmed cases of COVID-19, lockdown measures are gradually being lifted. However, even if most physical distancing measures are continued, other public health measures will be needed to control the epidemic. Contact tracing via conventional methods or mobile app technology is central to control strategies during de-escalation of physical distancing. We aimed to identify key factors for a contact tracing strategy to be successful. Methods We evaluated the impact of timeliness and completeness in various steps of a contact tracing strategy using a stochastic mathematical model with explicit time delays between time of infection and symptom onset, and between symptom onset, diagnosis by testing, and isolation (testing delay). The model also includes tracing of close contacts (eg, household members) and casual contacts, followed by testing regardless of symptoms and isolation if testing positive, with different tracing delays and coverages. We computed effective reproduction numbers of a contact tracing strategy (R CTS) for a population with physical distancing measures and various scenarios for isolation of index cases and tracing and quarantine of their contacts. Findings For the most optimistic scenario (testing and tracing delays of 0 days and tracing coverage of 100%), and assuming that around 40% of transmissions occur before symptom onset, the model predicts that the estimated effective reproduction number of 1·2 (with physical distancing only) will be reduced to 0·8 (95% CI 0·7–0·9) by adding contact tracing. The model also shows that a similar reduction can be achieved when testing and tracing coverage is reduced to 80% (R CTS 0·8, 95% CI 0·7–1·0). A testing delay of more than 1 day requires the tracing delay to be at most 1 day or tracing coverage to be at least 80% to keep R CTS below 1. With a testing delay of 3 days or longer, even the most efficient strategy cannot reach R CTS values below 1. The effect of minimising tracing delay (eg, with app-based technology) declines with decreasing coverage of app use, but app-based tracing alone remains more effective than conventional tracing alone even with 20% coverage, reducing the reproduction number by 17·6% compared with 2·5%. The proportion of onward transmissions per index case that can be prevented depends on testing and tracing delays, and given a 0-day tracing delay, ranges from up to 79·9% with a 0-day testing delay to 41·8% with a 3-day testing delay and 4·9% with a 7-day testing delay. Interpretation In our model, minimising testing delay had the largest impact on reducing onward transmissions. Optimising testing and tracing coverage and minimising tracing delays, for instance with app-based technology, further enhanced contact tracing effectiveness, with the potential to prevent up to 80% of all transmissions. Access to testing should therefore be optimised, and mobile app technology might reduce delays in the contact tracing process and optimise contact tracing coverage. Funding ZonMw, Fundação para a Ciência e a Tecnologia, and EU Horizon 2020 RECOVER.
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                Author and article information

                Journal
                Lancet
                Lancet
                Lancet (London, England)
                Elsevier Ltd.
                0140-6736
                1474-547X
                18 December 2020
                9-15 January 2021
                18 December 2020
                : 397
                : 10269
                : 92-93
                Affiliations
                [a ]Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany
                [b ]Technische Universität Braunschweig, Helmholtz Zentrum für Infektionsforschung, Braunschweig, Germany
                [c ]University Hospital, Goethe-University Frankfurt, Frankfurt, Germany
                [d ]Faculty of Medicine & Surgery, University of Malta, Msida, Malta
                [e ]Institute for Advanced Studies, Vienna, Austria
                [f ]London School of Economics, London, UK
                [g ]University of Trento, Trento, Italy
                [h ]Queen Mary University of London, London, UK
                [i ]London School of Hygiene & Tropical Medicine, London, UK
                [j ]Karolinska Institute, Stockholm, Sweden
                [k ]I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
                [l ]Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
                [m ]Department of Epidemiology, Center for Public Health, Medical University of Vienna, Austria
                [n ]University Medical Center Utrecht, Utrecht, Netherlands
                [o ]Inserm-University Toulouse III Paul Sabatier, Toulouse, France
                [p ]Medical University of Vienna, Vienna, Austria
                [q ]Complexity Science Hub Vienna, Vienna, Austria
                [r ]ifo Institute, Leibniz Institute for Economic Research at the University of Munich, Munich, Germany
                [s ]University of Maribor, Maribor, Slovenia
                [t ]Federico II University of Napoli, Napoli, Italy
                [u ]Centre of Excellence for Particle Physics and Cosmology and Danish Institute for Advanced Study, University of Southern Denmark, Aarhus, Denmark
                [v ]Campus Institute for Dynamics of Biological Networks, Göttingen, Germany
                [w ]School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland
                [x ]Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
                Article
                S0140-6736(20)32625-8
                10.1016/S0140-6736(20)32625-8
                7833270
                33347811
                62c3523f-3ded-460d-8be9-e9b6d2ab90ac
                © 2020 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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