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      Selected restrictions in specific places are better policy responses than full lockdown

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          Abstract

          The goal of this study is a comparative analysis of the first and second wave of the Coronavirus disease 2019 (COVID-19) to assess the impact on health of people for designing effective policy responses to constrain negative effects of future infectious diseases similar to COVID-19 in society. The research here focuses on a case study of Italy, one of the first countries to experience a rapid increase in numbers of COVID-19 related infected individuals and deaths. Statistical analyses, based on daily data from February 2020 to February 2021, suggest that the first wave of COVID-19 pandemic in Italy had a high negative impact on health of people over February-May 2020 that declined from June 2020 onwards. Second wave of COVID-19 pandemic started in August 2020, to February 2021, has a growing incidence of confirmed cases, whereas admissions to Intensive Care Units and total deaths have lower levels compared to first wave. Lessons learned from a comparative analysis between first and second wave of the COVID-19 pandemic can be generalized in similar geo-economic regions to support effective policy responses of crisis management to constrain the impact of recurring waves of COVID-19 pandemic and future infectious diseases on health of people.

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          The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study

          Summary Background In December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world. Methods To examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April). Findings Our projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66–97) and 24% (13–90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic. Interpretation Restrictions on activities in Wuhan, if maintained until April, would probably help to delay the epidemic peak. Our projections suggest that premature and sudden lifting of interventions could lead to an earlier secondary peak, which could be flattened by relaxing the interventions gradually. However, there are limitations to our analysis, including large uncertainties around estimates of R 0 and the duration of infectiousness. Funding Bill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.
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            COVID-19 Does Not Lead to a “Typical” Acute Respiratory Distress Syndrome

            To the Editor: In northern Italy, an overwhelming number of patients with coronavirus disease (COVID-19) pneumonia and acute respiratory failure have been admitted to our ICUs. Attention is primarily focused on increasing the number of beds, ventilators, and intensivists brought to bear on the problem, while the clinical approach to these patients is the one typically applied to severe acute respiratory distress syndrome (ARDS), namely, high positive end-expiratory pressure (PEEP) and prone positioning. However, the patients with COVID-19 pneumonia, despite meeting the Berlin definition of ARDS, present an atypical form of the syndrome. Indeed, the primary characteristic we are observing (and has been confirmed by colleagues in other hospitals) is a dissociation between their relatively well-preserved lung mechanics and the severity of hypoxemia. As shown in our first 16 patients (Figure 1), a respiratory system compliance of 50.2 ± 14.3 ml/cm H2O is associated with a shunt fraction of 0.50 ± 0.11. Such a wide discrepancy is virtually never seen in most forms of ARDS. Relatively high compliance indicates a well-preserved lung gas volume in this patient cohort, in sharp contrast to expectations for severe ARDS. Figure 1. (A) Distributions of the observations of the compliance values observed in our cohort of patients. (B) Distributions of the observations of the right-to-left shunt values observed in our cohort of patients. A possible explanation for such severe hypoxemia occurring in compliant lungs is a loss of lung perfusion regulation and hypoxic vasoconstriction. Actually, in ARDS, the ratio of the shunt fraction to the fraction of gasless tissue is highly variable, with a mean of 1.25 ± 0.80 (1). In eight of our patients with a computed tomography scan, however, we measured a ratio of 3.0 ± 2.1, suggesting a remarkable hyperperfusion of gasless tissue. If this is the case, the increases in oxygenation with high PEEP and/or prone positioning are not primarily due to recruitment, the usual mechanism in ARDS (2), but instead, in these patients with poorly recruitable lungs (3), result from the redistribution of perfusion in response to pressure and/or gravitational forces. We should consider that 1) in patients who are treated with continuous positive airway pressure or noninvasive ventilation and who present with clinical signs of excessive inspiratory efforts, intubation should be prioritized to avoid excessive intrathoracic negative pressures and self-inflicted lung injury (4); 2) high PEEP in a poorly recruitable lung tends to result in severe hemodynamic impairment and fluid retention; and 3) prone positioning of patients with relatively high compliance provides a modest benefit at the cost of a high demand for stressed human resources. Given the above considerations, the best we can do while ventilating these patients is to “buy time” while causing minimal additional damage, by maintaining the lowest possible PEEP and gentle ventilation. We need to be patient.
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              Mobility network models of COVID-19 explain inequities and inform reopening

              The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.
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                Author and article information

                Journal
                Environ Res
                Environ Res
                Environmental Research
                Elsevier Inc.
                0013-9351
                1096-0953
                2 April 2021
                2 April 2021
                : 111099
                Affiliations
                [1]CNR -- National Research Council of Italy, Via Real Collegio, N. 30 (Collegio Carlo Alberto), 10024 - Moncalieri (TO), Italy
                Article
                S0013-9351(21)00393-5 111099
                10.1016/j.envres.2021.111099
                8017951
                33819476
                583d69b7-5778-40bd-a474-951b6365dbae
                © 2021 Elsevier Inc. 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.

                History
                : 18 November 2020
                : 8 March 2021
                : 26 March 2021
                Categories
                Article

                General environmental science
                covid-19,public health,crisis management,containment measures,healthcare sector,public policy,public health capacity,health systems,country monitoring,pandemic response,preventing transmission

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