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      COVID-19 lockdown induces disease-mitigating structural changes in mobility networks

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          During the COVID-19 pandemic, mobility restrictions have proved to be an effective mitigation strategy in many countries. To apply these measures more efficiently in the future, it is important to understand their effects in detail. In this study, we use mobile phone data to uncover profound structural changes in mobility in Germany during the pandemic. We find that a strong reduction of long-distance travel rendered mobility to be more local, such that distant parts of the country became less connected. We demonstrate that due to this loss of connectivity, infectious diseases can be slowed down in their spatial spread. Our study provides important insights into the complex effects of mobility restrictions for policymakers and future research.

          Abstract

          In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the “small-world” effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions.

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          Most cited references36

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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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            World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)

            An unprecedented outbreak of pneumonia of unknown aetiology in Wuhan City, Hubei province in China emerged in December 2019. A novel coronavirus was identified as the causative agent and was subsequently termed COVID-19 by the World Health Organization (WHO). Considered a relative of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), COVID-19 is caused by a betacoronavirus named SARS-CoV-2 that affects the lower respiratory tract and manifests as pneumonia in humans. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 continues to rise, with 90,870 laboratory-confirmed cases and over 3,000 deaths worldwide. In response to this global outbreak, we summarise the current state of knowledge surrounding COVID-19.
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              Collective dynamics of 'small-world' networks.

              Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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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
                29 December 2020
                3 December 2020
                3 December 2020
                : 117
                : 52
                : 32883-32890
                Affiliations
                [1] aComputational Epidemiology Group, Robert Koch Institute, D-13353 Berlin, Germany;
                [2] bInstitute for Theoretical Biology, Humboldt University of Berlin, D-10115 Berlin, Germany
                Author notes
                1To whom correspondence may be addressed. Email: frank.schlosser@ 123456hu-berlin.de .

                Edited by Nils Chr. Stenseth, University of Oslo, Oslo, Norway, and approved October 20, 2020 (received for review June 26, 2020)

                Author contributions: F.S., B.F.M., and D.B. designed research; F.S. and B.F.M. performed research; F.S., B.F.M., O.J., D.H., and A.Z. analyzed data; and F.S., B.F.M., and D.B. wrote the paper.

                Author information
                http://orcid.org/0000-0003-1649-4300
                http://orcid.org/0000-0001-7414-8823
                http://orcid.org/0000-0002-7435-2119
                http://orcid.org/0000-0001-8525-5907
                http://orcid.org/0000-0001-9780-4651
                http://orcid.org/0000-0001-5708-2922
                Article
                202012326
                10.1073/pnas.2012326117
                7776901
                33273120
                377d199d-d612-4b46-bd8d-1e546dcc3c25
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 8
                Categories
                Physical Sciences
                Applied Physical Sciences
                Social Sciences
                Social Sciences
                From the Cover

                covid-19,human mobility,mobile phones
                covid-19, human mobility, mobile phones

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