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      The “second wave” of the COVID-19 pandemic in the Arctic: regional and temporal dynamics

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          ABSTRACT

          This article focuses on the “second wave” of the COVID-19 pandemic in the Arctic and examines spatiotemporal patterns between July 2020 and January 2021. We analyse available COVID-19 data at the regional (subnational) level to elucidate patterns and typology of Arctic regions with respect to the COVID-19 pandemic. This article builds upon our previous research that examined the early phase of the COVID-19 pandemic between February and July 2020. The pandemic’s “second wave” observed in the Arctic between September 2020 and January 2021 was severe in terms of COVID-19 infections and fatalities, having particularly strong impacts in Alaska, Northern Russia and Northern Sweden. Based on the spatiotemporal patterns of the “second wave” dynamics, we identified 5 types of the pandemic across regions: Shockwaves (Iceland, Faroe Islands, Northern Norway, and Northern Finland), Protracted Waves (Northern Sweden), Tidal Waves (Northern Russia), Tsunami Waves (Alaska), and Isolated Splashes (Northern Canada and Greenland). Although data limitations and gaps persist, monitoring of COVID-19 is critical for developing a proper understanding of the pandemic in order to develop informed and effective responses to the current crisis and possible future pandemics in the Arctic. Data used in this paper are available at https://arctic.uni.edu/arctic-covid-19.

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          Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)

          Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%–90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%–62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.
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            The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?

            Abstract Objectives To provide an overview of the three major deadly coronaviruses and identify areas for improvement of future preparedness plans, as well as provide a critical assessment of the risk factors and actionable items for stopping their spread, utilizing lessons learned from the first two deadly coronavirus outbreaks, as well as initial reports from the current novel coronavirus (COVID-19) epidemic in Wuhan, China. Methods Utilizing the Centers for Disease Control and Prevention (CDC, USA) website, and a comprehensive review of PubMed literature, we obtained information regarding clinical signs and symptoms, treatment and diagnosis, transmission methods, protection methods and risk factors for Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and COVID-19. Comparisons between the viruses were made. Results Inadequate risk assessment regarding the urgency of the situation, and limited reporting on the virus within China has, in part, led to the rapid spread of COVID-19 throughout mainland China and into proximal and distant countries. Compared with SARS and MERS, COVID-19 has spread more rapidly, due in part to increased globalization and the focus of the epidemic. Wuhan, China is a large hub connecting the North, South, East and West of China via railways and a major international airport. The availability of connecting flights, the timing of the outbreak during the Chinese (Lunar) New Year, and the massive rail transit hub located in Wuhan has enabled the virus to perforate throughout China, and eventually, globally. Conclusions We conclude that we did not learn from the two prior epidemics of coronavirus and were ill-prepared to deal with the challenges the COVID-19 epidemic has posed. Future research should attempt to address the uses and implications of internet of things (IoT) technologies for mapping the spread of infection.
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              Beware of the second wave of COVID-19

              The outbreak of coronavirus disease 2019 (COVID-19), which began in Wuhan, China, in late 2019, has spread to 203 countries as of March 30, 2020, and has been officially declared a global pandemic. 1 With unprecedented public health interventions, local transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appears now to have been contained in China. Multiple countries are now experiencing the first wave of the COVID-19 epidemic; thus, gaining an understanding of how these interventions prevented the transmission of SARS-CoV-2 in China is urgent. In The Lancet, Kathy Leung and colleagues 2 report their assessment of the transmissibility and severity of COVID-19 during the first wave in four cities and ten provinces in China outside Hubei. The study estimated the instantaneous reproduction number in the selected locations decreased substantially after non-pharmaceutical control measures were implemented on Jan 23, 2020, and has since remained lower than 1. The transmission of SARS-CoV-2 in these locations was mainly driven by imported cases from Hubei until late January, which is, to some extent, similar to the transmission in January in several countries. The epidemics in Chinese provinces outside Hubei were believed to be driven by local transmission dynamics after Jan 31; 3 therefore, the findings of Leung and colleagues' study highlight the fact that the package of non-pharmaceutical interventions in China has the ability to contain transmission—not only imported cases, but also local transmission. The epidemic is accelerating rapidly in multiple countries, indicating shortfalls in preparedness. Given that multiple countries imposed travel restrictions against China in late January, there is a need to model whether earlier implementation of interventions such as social distancing, population behavioural change, and contact tracing would have been able to contain or mitigate the epidemic. Leung and colleagues also modelled the potential adverse consequences of premature relaxation of interventions, and found that such a decision might lead to transmissibility exceeding 1 again—ie, a second wave of infections. The finding is critical to governments globally, because it warns against premature relaxation of strict interventions. However, the effect of each intervention, or which one was the most effective in containing the spread of the virus, was not addressed in the study. While interventions to control the spread of SARS-CoV-2 are in place, countries will need to work toward returning to normalcy; thus, knowledge of the effect of each intervention is urgently required. Air travel data were used to model the effect of travel restrictions on delaying overall epidemic progression, and were found to have a marked effect at the international scale, but only a 3–5 day delay within China. 4 A study 5 focused on the effects of extending or relaxing physical distancing control measures in Wuhan has suggested that if the measures are gradually relaxed in March, a second wave of cases might occur in the northern hemisphere mid-summer. Country-specific models of the effects of travel restrictions and social distancing, as well as the alternative strategies after the relaxation of these interventions, such as the use of face masks, temperature checks, and contact tracing, are now needed. Case fatality rate (CFR) is one of the important unknowns of COVID-19. Leung and colleagues estimated the confirmed CFR (cCFR) outside Hubei was 0·98% (95% CI 0·82–1·16), which was consistent with the report from the Chinese Center for Disease Control and Prevention. 6 Since the epidemics in the studied locations did not overwhelm the health-care capacities, the data on the number of confirmed cases are believed to be reliable. Leung and colleagues also found the cCFR was correlated with provincial per capita gross domestic product and the availability of hospital beds per 10 000. In Wuhan, the CFR was up to 5·08% by March 28, 2020. 7 The remarkable difference in the CFRa between these locations and Wuhan might be attributed to the difference in the degrees of health-care capacity. Therefore, consideration should be given to the variations in health-care capacity when implementing interventions. While the epidemic is growing exponentially, the health-care system will face severe burdens. Governments should act and prepare immediately to ensure that the health-care system has adequate labour, resources, and facilities to minimise the mortality risk of COVID-19. © 2020 Tingshu Wang/Reuters 2020 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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                Author and article information

                Journal
                Int J Circumpolar Health
                Int J Circumpolar Health
                International Journal of Circumpolar Health
                Taylor & Francis
                1239-9736
                2242-3982
                14 June 2021
                2021
                : 80
                : 1
                : 1925446
                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 Health, Recreation, and Community Services, University of Northern Iowa; , Cedar Falls, IA, USA
                [d ]Department of Geography, Texas State University; , Round Rock, TX, USA
                Author notes
                CONTACT Andrey N. Petrov andrey.petrov@ 123456uni.edu 1227 W 27th Street Cedar Falls; , IA 50614, USA
                Author information
                https://orcid.org/0000-0003-1345-2842
                https://orcid.org/0000-0002-5201-7555
                https://orcid.org/0000-0001-7849-7035
                Article
                1925446
                10.1080/22423982.2021.1925446
                8205071
                34125008
                92192451-2b5d-450a-bff3-4e8215a2b687
                © 2021 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.

                History
                Page count
                Figures: 5, Tables: 2, References: 63, Pages: 1
                Categories
                Research Article
                Short Communication

                Medicine
                covid-19,arctic,regions,pandemic,trends,mortality,waves
                Medicine
                covid-19, arctic, regions, pandemic, trends, mortality, waves

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