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      A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave

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      Nature Communications
      Nature Publishing Group UK
      Computational models, SARS-CoV-2, Epidemiology, Statistics

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          Abstract

          Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

          Abstract

          Forecasting models have been used extensively to inform decision making during the COVID-19 pandemic. In this preregistered and prospective study, the authors evaluated 14 short-term models for Germany and Poland, finding considerable heterogeneity in predictions and highlighting the benefits of combined forecasts.

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          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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            The spread of awareness and its impact on epidemic outbreaks.

            When a disease breaks out in a human population, changes in behavior in response to the outbreak can alter the progression of the infectious agent. In particular, people aware of a disease in their proximity can take measures to reduce their susceptibility. Even if no centralized information is provided about the presence of a disease, such awareness can arise through first-hand observation and word of mouth. To understand the effects this can have on the spread of a disease, we formulate and analyze a mathematical model for the spread of awareness in a host population, and then link this to an epidemiological model by having more informed hosts reduce their susceptibility. We find that, in a well-mixed population, this can result in a lower size of the outbreak, but does not affect the epidemic threshold. If, however, the behavioral response is treated as a local effect arising in the proximity of an outbreak, it can completely stop a disease from spreading, although only if the infection rate is below a threshold. We show that the impact of locally spreading awareness is amplified if the social network of potential infection events and the network over which individuals communicate overlap, especially so if the networks have a high level of clustering. These findings suggest that care needs to be taken both in the interpretation of disease parameters, as well as in the prediction of the fate of future outbreaks.
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              Strictly Proper Scoring Rules, Prediction, and Estimation

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                Author and article information

                Contributors
                johannes.bracher@kit.edu
                melanie.schienle@kit.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                27 August 2021
                27 August 2021
                2021
                : 12
                : 5173
                Affiliations
                [1 ]GRID grid.7892.4, ISNI 0000 0001 0075 5874, Chair of Statistics and Econometrics, , Karlsruhe Institute of Technology (KIT), ; Karlsruhe, Germany
                [2 ]GRID grid.424699.4, ISNI 0000 0001 2275 2842, Computational Statistics Group, , Heidelberg Institute for Theoretical Studies (HITS), ; Heidelberg, Germany
                [3 ]GRID grid.13652.33, ISNI 0000 0001 0940 3744, Robert Koch Institute (RKI), ; Berlin, Germany
                [4 ]GRID grid.8991.9, ISNI 0000 0004 0425 469X, London School of Hygiene and Tropical Medicine, ; London, UK
                [5 ]GRID grid.417999.b, Frankfurt Institute for Advanced Studies, ; Frankfurt, Germany
                [6 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Sloan School of Management, , Massachusetts Institute of Technology, ; Cambridge, MA USA
                [7 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), , Imperial College London, ; London, UK
                [8 ]GRID grid.7005.2, ISNI 0000 0000 9805 3178, Wroclaw University of Science and Technology, ; Wroclaw, Poland
                [9 ]GRID grid.12391.38, ISNI 0000 0001 2289 1527, Economic and Social Statistics Department, , University of Trier, ; Trier, Germany
                [10 ]GRID grid.148313.c, ISNI 0000 0004 0428 3079, Information Systems and Modeling, , Los Alamos National Laboratory, ; Los Alamos, NM USA
                [11 ]GRID grid.8385.6, ISNI 0000 0001 2297 375X, Jülich Supercomputing Centre, , Forschungszentrum Jülich, ; Jülich, Germany
                [12 ]GRID grid.12847.38, ISNI 0000 0004 1937 1290, Institute of Informatics, , University of Warsaw, ; Warsaw, Poland
                [13 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Computer Science, , University of California, ; Los Angeles, CA USA
                [14 ]GRID grid.6553.5, ISNI 0000 0001 1087 7453, Institute of Mathematics, , Technische Universität Ilmenau, ; Ilmenau, Germany
                [15 ]GRID grid.9647.c, ISNI 0000 0004 7669 9786, Institute for Medical Informatics, Statistics and Epidemiology, , University of Leipzig, ; Leipzig, Germany
                [16 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Swiss Data Science Center, , ETH Zurich and EPFL, ; Lausanne, Switzerland
                [17 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Operations Research Center, , Massachusetts Institute of Technology, ; Cambridge, MA USA
                [18 ]GRID grid.148313.c, ISNI 0000 0004 0428 3079, Statistical Sciences Group, , Los Alamos National Laboratory, ; Los Alamos, NM USA
                [19 ]GRID grid.12847.38, ISNI 0000 0004 1937 1290, Interdisciplinary Centre for Mathematical and Computational Modeling, , University of Warsaw, ; Warsaw, Poland
                [20 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, Ming Hsieh Department of Computer and Electrical Engineering, , University of Southern California, ; Los Angeles, CA USA
                [21 ]GRID grid.7892.4, ISNI 0000 0001 0075 5874, Institute for Stochastics, , Karlsruhe Institute of Technology (KIT), ; Karlsruhe, Germany
                [22 ]GRID grid.8591.5, ISNI 0000 0001 2322 4988, Institute of Global Health, Faculty of Medicine, , University of Geneva, ; Geneva, Switzerland
                [23 ]GRID grid.5333.6, ISNI 0000000121839049, Center for Intelligent Systems, , EPFL, ; Lausanne, Switzerland
                [24 ]GRID grid.37179.3b, ISNI 0000 0001 0664 8391, Institute of Psychology, , John Paul II Catholic University of Lublin, ; Lublin, Poland
                [25 ]GRID grid.25588.32, ISNI 0000 0004 0620 6106, Faculty of Physics, , University of Białystok, ; Białystok, Poland
                [26 ]GRID grid.1035.7, ISNI 0000000099214842, Warsaw University of Technology, ; Warsaw, Poland
                [27 ]Polish National Institute of Public Health—National Institute of Hygiene, Wroclaw, Poland
                [28 ]Nokia Solutions and Networks, Wroclaw, Poland
                [29 ]GRID grid.12847.38, ISNI 0000 0004 1937 1290, University of Warsaw, ; Warsaw, Poland
                [30 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, University of Southern California, ; Los Angeles, USA
                Author information
                http://orcid.org/0000-0002-3777-1410
                http://orcid.org/0000-0003-0318-3669
                http://orcid.org/0000-0001-7070-2241
                http://orcid.org/0000-0002-7223-6085
                http://orcid.org/0000-0002-1192-7165
                http://orcid.org/0000-0002-4894-6124
                http://orcid.org/0000-0001-6525-101X
                http://orcid.org/0000-0001-6390-0338
                http://orcid.org/0000-0002-7750-5280
                http://orcid.org/0000-0002-5771-6179
                http://orcid.org/0000-0002-9778-570X
                http://orcid.org/0000-0001-5500-8120
                http://orcid.org/0000-0002-2842-3406
                http://orcid.org/0000-0001-5523-5198
                http://orcid.org/0000-0001-9830-793X
                http://orcid.org/0000-0002-3371-4072
                http://orcid.org/0000-0002-3126-7950
                http://orcid.org/0000-0002-5313-3451
                http://orcid.org/0000-0002-2456-4834
                http://orcid.org/0000-0003-2831-9761
                http://orcid.org/0000-0003-3349-0467
                http://orcid.org/0000-0001-7405-2207
                http://orcid.org/0000-0003-3331-6637
                http://orcid.org/0000-0002-4059-1779
                http://orcid.org/0000-0002-8898-5726
                http://orcid.org/0000-0002-8706-5717
                http://orcid.org/0000-0001-8935-8137
                http://orcid.org/0000-0001-6000-5653
                Article
                25207
                10.1038/s41467-021-25207-0
                8397791
                34453047
                00e03ae5-ff75-4e14-925c-40dd40c887c4
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 December 2020
                : 28 July 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100009318, Helmholtz Association;
                Award ID: SIMCARD Information and Data Science Pilot Project
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                computational models,sars-cov-2,epidemiology,statistics
                Uncategorized
                computational models, sars-cov-2, epidemiology, statistics

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