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      Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures

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          Significance

          The ongoing pandemic of COVID-19 challenges globalized societies. Scientific and technological cross-fertilization yields broad availability of georeferenced epidemiological data and of modeling tools that aid decisions on emergency management. To this end, spatially explicit models of the COVID-19 epidemic that include e.g. regional individual mobilities, the progression of social distancing, and local capacity of medical infrastructure provide significant information. Data-tailored spatial resolutions that model the disease spread geography can include details of interventions at the proper geographical scale. Based on them, it is possible to quantify the effect of local containment measures (like diachronic spatial maps of averted hospitalizations) and the assessment of the spatial and temporal planning of the needs of emergency measures and medical infrastructure as a major contingency planning aid.

          Abstract

          The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible–Exposed–Infected–Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number ( R 0 = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.

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          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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            Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China

            In December 2019, novel coronavirus (2019-nCoV)-infected pneumonia (NCIP) occurred in Wuhan, China. The number of cases has increased rapidly but information on the clinical characteristics of affected patients is limited.
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              Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

              Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                12 May 2020
                23 April 2020
                23 April 2020
                : 117
                : 19
                : 10484-10491
                Affiliations
                [1] aDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano , 20133 Milano, Italy;
                [2] bDipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia , 30172 Venezia-Mestre, Italy;
                [3] cScience of Complexity Research Unit, European Centre for Living Technology , 30123 Venice, Italy;
                [4] dDipartimento di Meccanica, Politecnico di Milano , 20133 Milano, Italy;
                [5] eDepartment of Aquatic Ecology, Swiss Federal Institute of Aquatic Science and Technology , 8600 Dübendorf, Switzerland;
                [6] fDepartment of Evolutionary Biology and Environmental Studies, University of Zurich , 8057 Zurich, Switzerland;
                [7] gLaboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne , 1015 Lausanne, Switzerland;
                [8] hDipartimento di Ingegneria Civile, Edile e Ambientale, Università di Padova , 35131 Padova, Italy
                Author notes
                1To whom correspondence may be addressed. Email: andrea.rinaldo@ 123456epfl.ch or marino.gatto@ 123456polimi.it .

                Contributed by Andrea Rinaldo, April 6, 2020 (sent for review March 26, 2020; reviewed by Andy P. Dobson and Giorgio Parisi)

                Author contributions: M.G., E.B., L.M., S.M., L.C., R.C., and A.R. designed research; M.G., E.B., L.M., S.M., L.C., R.C., and A.R. performed research; E.B., L.M., S.M., and L.C. analyzed data; and M.G., E.B., L.M., S.M., L.C., R.C., and A.R. wrote the paper.

                Reviewers: A.P.D., Princeton University; and G.P., Sapienza University of Rome.

                Author information
                http://orcid.org/0000-0001-8063-9178
                http://orcid.org/0000-0001-5872-0666
                http://orcid.org/0000-0003-1326-9992
                http://orcid.org/0000-0002-7447-049X
                http://orcid.org/0000-0002-3933-1144
                http://orcid.org/0000-0001-5177-803X
                Article
                202004978
                10.1073/pnas.2004978117
                7229754
                32327608
                9ba1d3f4-5b4b-4a10-9ac1-e289b2a80ef5
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 8
                Funding
                Funded by: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SNF) 501100001711
                Award ID: 200021_172578/1 “Optimal control of intervention strategies for waterborne disease epidemic”
                Award Recipient : Andrea Rinaldo
                Funded by: EC | H2020 | H2020 Priority Excellent Science | H2020 European Research Council (ERC) 100010663
                Award ID: RINEC-227612 "River networks as ecological corridors: species
                Award ID: populations
                Award ID: pathogens"
                Award Recipient : Andrea Rinaldo
                Categories
                530
                Biological Sciences
                Medical Sciences
                Custom metadata
                free

                sars-cov-2,spatially explicit epidemiology,disease outbreak scenarios,seir models,social contact restrictions

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