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      A multivariate statistical approach to predict COVID‐19 count data with epidemiological interpretation and uncertainty quantification

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          Abstract

          For the analysis of COVID‐19 pandemic data, we propose Bayesian multinomial and Dirichlet‐multinomial autoregressive models for time‐series of counts of patients in mutually exclusive and exhaustive observational categories, defined according to the severity of the patient status and the required treatment. Categories include hospitalized in regular wards (H) and in intensive care units (ICU), together with deceased (D) and recovered (R). These models explicitly formulate assumptions on the transition probabilities between these categories across time, thanks to a flexible formulation based on parameters that a priori follow normal distributions, possibly truncated to incorporate specific hypotheses having an epidemiological interpretation. The posterior distribution of model parameters and the transition matrices are estimated by a Markov chain Monte Carlo algorithm that also provides predictions and allows us to compute the reproduction number R t . All estimates and predictions are endowed with an accuracy measure obtained thanks to the Bayesian approach. We present results concerning data collected during the first wave of the pandemic in Italy and Lombardy and study the effect of nonpharmaceutical interventions. Suitable discrepancy measures defined to check and compare models show that the Dirichlet‐multinomial model has an adequate fit and provides good predictive performance in particular for H and ICU patients.

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          COVID-19 and Italy: what next?

          Summary The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected. There is now grave concern regarding the Italian national health system's capacity to effectively respond to the needs of patients who are infected and require intensive care for SARS-CoV-2 pneumonia. The percentage of patients in intensive care reported daily in Italy between March 1 and March 11, 2020, has consistently been between 9% and 11% of patients who are actively infected. The number of patients infected since Feb 21 in Italy closely follows an exponential trend. If this trend continues for 1 more week, there will be 30 000 infected patients. Intensive care units will then be at maximum capacity; up to 4000 hospital beds will be needed by mid-April, 2020. Our analysis might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. If the Italian outbreak follows a similar trend as in Hubei province, China, the number of newly infected patients could start to decrease within 3–4 days, departing from the exponential trend. However, this cannot currently be predicted because of differences between social distancing measures and the capacity to quickly build dedicated facilities in China.
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            Equation of State Calculations by Fast Computing Machines

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              Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations

              Summary As coronavirus disease 2019 (COVID-19) spreads across the world, the intensive care unit (ICU) community must prepare for the challenges associated with this pandemic. Streamlining of workflows for rapid diagnosis and isolation, clinical management, and infection prevention will matter not only to patients with COVID-19, but also to health-care workers and other patients who are at risk from nosocomial transmission. Management of acute respiratory failure and haemodynamics is key. ICU practitioners, hospital administrators, governments, and policy makers must prepare for a substantial increase in critical care bed capacity, with a focus not just on infrastructure and supplies, but also on staff management. Critical care triage to allow the rationing of scarce ICU resources might be needed. Researchers must address unanswered questions, including the role of repurposed and experimental therapies. Collaboration at the local, regional, national, and international level offers the best chance of survival for the critically ill.
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                Author and article information

                Contributors
                francesco.bartolucci@unipg.it
                Journal
                Stat Med
                Stat Med
                10.1002/(ISSN)1097-0258
                SIM
                Statistics in Medicine
                John Wiley and Sons Inc. (Hoboken )
                0277-6715
                1097-0258
                10 August 2021
                10 August 2021
                : 10.1002/sim.9129
                Affiliations
                [ 1 ] Department of Economics University of Perugia Perugia Italy
                [ 2 ] Department of Statistics and Quantitative Methods University of Milano‐Bicocca Milan Italy
                [ 3 ] Faculty of Economics Università della Svizzera italiana (CH) Lugano Italy
                [ 4 ] University of Insubria Varese Italy
                Author notes
                [*] [* ] Correspondence Francesco Bartolucci, Department of Economics, University of Perugia, Via A. Pascoli, 20, 06123 Perugia, Italy.

                Email: francesco.bartolucci@ 123456unipg.it

                Author information
                https://orcid.org/0000-0001-7057-1421
                https://orcid.org/0000-0002-6331-7211
                https://orcid.org/0000-0002-5609-7935
                Article
                SIM9129
                10.1002/sim.9129
                8441832
                34374438
                8c96b717-2675-43c2-b717-a346323e6e37
                © 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 June 2021
                : 25 July 2020
                : 16 June 2021
                Page count
                Figures: 4, Tables: 19, Pages: 22, Words: 9848
                Funding
                Funded by: Eurpean Union
                Award ID: 101016233
                Funded by: Regione Lombardia , doi 10.13039/501100009882;
                Award ID: InPresa
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                corrected-proof
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.7 mode:remove_FC converted:15.09.2021

                Biostatistics
                dirichlet‐multinomial distribution,epidemic modeling,model diagnostics,multinomial distribution,pandemic predictions,reproduction number

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