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      Spatiotemporal dynamics of the COVID-19 pandemic in the arctic: early data and emerging trends

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          ABSTRACT

          Since February 2020 the COVID-19 pandemic has been unfolding in the Arctic, placing many communities at risk due to remoteness, limited healthcare options, underlying health issues and other compounding factors. Preliminary analysis of available COVID-19 data in the Arctic at the regional (subnational) level suggests that COVID-19 infections and mortality were highly variable, but generally remained below respective national levels. Based on the trends and magnitude of the pandemic through July, we classify Arctic regions into four groups: Iceland, Faroe Islands, Northern Norway, and Northern Finland with elevated early incidence rates, but where strict quarantines and other measures promptly curtailed the pandemic; Northern Sweden and Alaska, where the initial wave of infections persisted amid weak (Sweden) or variable (Alaska) quarantine measures; Northern Russia characterised by the late start and subsequent steep growth of COVID-19 cases and fatalities and multiple outbreaks; and Northern Canada and Greenland with no significant proliferation of the pandemic. Despite limitations in available data, further efforts to track and analyse the pandemic at the pan-Arctic, regional and local scales are crucial. This includes understanding of the COVID-19 patterns, mortality and morbidity, the relationships with public-health conditions, socioeconomic characteristics, policies, and experiences of the Indigenous Peoples.

          Data used in this paper are available at https://arctic.uni.edu/arctic-covid-19.

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          Most cited references31

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          Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China

          Previous studies have showed clinical characteristics of patients with the 2019 novel coronavirus disease (COVID-19) and the evidence of person-to-person transmission. Limited data are available for asymptomatic infections. This study aims to present the clinical characteristics of 24 cases with asymptomatic infection screened from close contacts and to show the transmission potential of asymptomatic COVID-19 virus carriers. Epidemiological investigations were conducted among all close contacts of COVID-19 patients (or suspected patients) in Nanjing, Jiangsu Province, China, from Jan 28 to Feb 9, 2020, both in clinic and in community. Asymptomatic carriers were laboratory-confirmed positive for the COVID-19 virus by testing the nucleic acid of the pharyngeal swab samples. Their clinical records, laboratory assessments, and chest CT scans were reviewed. As a result, none of the 24 asymptomatic cases presented any obvious symptoms while nucleic acid screening. Five cases (20.8%) developed symptoms (fever, cough, fatigue, etc.) during hospitalization. Twelve (50.0%) cases showed typical CT images of ground-glass chest and 5 (20.8%) presented stripe shadowing in the lungs. The remaining 7 (29.2%) cases showed normal CT image and had no symptoms during hospitalization. These 7 cases were younger (median age: 14.0 years; P=0.012) than the rest. None of the 24 cases developed severe COVID-19 pneumonia or died. The median communicable period, defined as the interval from the first day of positive nucleic acid tests to the first day of continuous negative tests, was 9.5 days (up to 21 days among the 24 asymptomatic cases). Through epidemiological investigation, we observed a typical asymptomatic transmission to the cohabiting family members, which even caused severe COVID-19 pneumonia. Overall, the asymptomatic carriers identified from close contacts were prone to be mildly ill during hospitalization. However, the communicable period could be up to three weeks and the communicated patients could develop severe illness. These results highlighted the importance of close contact tracing and longitudinally surveillance via virus nucleic acid tests. Further isolation recommendation and continuous nucleic acid tests may also be recommended to the patients discharged. Electronic Supplementary Material Supplementary material is available for this article at 10.1007/s11427-020-1661-4 and is accessible for authorized users.
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            Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study

            Summary Background Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge. Methods We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county. Findings Our analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35–63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9–12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message. Interpretation This study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future. Funding None.
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              COVID-19: the First Documented Coronavirus Pandemic in History

              The novel human coronavirus disease COVID-19 has become the fifth documented pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and subsequently spread worldwide. The coronavirus was officially named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses based on phylogenetic analysis. SARS-CoV-2 is believed to be a spillover of an animal coronavirus and later adapted the ability of human-to-human transmission. Because the virus is highly contagious, it rapidly spreads and continuously evolves in the human population. In this review article, we discuss the basic properties, potential origin, and evolution of the novel human coronavirus. These factors may be critical for studies of pathogenicity, antiviral designs, and vaccine development against the virus.
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                Author and article information

                Journal
                Int J Circumpolar Health
                Int J Circumpolar Health
                International Journal of Circumpolar Health
                Taylor & Francis
                1239-9736
                2242-3982
                19 October 2020
                2020
                : 79
                : 1
                : 1835251
                Affiliations
                [a ]ARCTICenter, University of Northern Iowa; , Cedar Falls, IA, USA
                [b ]Department of Geography, University of Northern Iowa; , Cedar Falls, IA, USA
                [c ]Department of Geography, Texas State University; , Round Rock, TX, USA
                Author notes
                CONTACT Andrey N. Petrov andrey.petrov@ 123456uni.edu ARCTICenter, University of Northern Iowa; , Cedar Falls, IA, USA
                Author information
                https://orcid.org/0000-0003-1345-2842
                https://orcid.org/0000-0001-7849-7035
                Article
                1835251
                10.1080/22423982.2020.1835251
                7595240
                33074067
                97711c50-c62c-43df-8f46-5719759dcc95
                © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Figures: 5, Tables: 1, References: 35, Pages: 1
                Categories
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                Medicine
                covid-19,arctic,regions,pandemic,trends,mortality
                Medicine
                covid-19, arctic, regions, pandemic, trends, mortality

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