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      A strategy to assess spillover risk of bat SARS-related coronaviruses in Southeast Asia

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          Abstract

          Emerging diseases caused by coronaviruses of likely bat origin (e.g., SARS, MERS, SADS, COVID-19) have disrupted global health and economies for two decades. Evidence suggests that some bat SARS-related coronaviruses (SARSr-CoVs) could infect people directly, and that their spillover is more frequent than previously recognized. Each zoonotic spillover of a novel virus represents an opportunity for evolutionary adaptation and further spread; therefore, quantifying the extent of this spillover may help target prevention programs. We derive current range distributions for known bat SARSr-CoV hosts and quantify their overlap with human populations. We then use probabilistic risk assessment and data on human-bat contact, human viral seroprevalence, and antibody duration to estimate that a median of 66,280 people (95% CI: 65,351–67,131) are infected with SARSr-CoVs annually in Southeast Asia. These data on the geography and scale of spillover can be used to target surveillance and prevention programs for potential future bat-CoV emergence.

          Abstract

          Coronaviruses may spill over from bats to humans. This study uses epidemiological data, species distribution models, and probabilistic risk assessment to map overlap among people and SARSr-CoV bat hosts and estimate how many people are infected with bat-origin SARSr-CoVs in Southeast Asia annually.

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          A pneumonia outbreak associated with a new coronavirus of probable bat origin

          Since the outbreak of severe acute respiratory syndrome (SARS) 18 years ago, a large number of SARS-related coronaviruses (SARSr-CoVs) have been discovered in their natural reservoir host, bats 1–4 . Previous studies have shown that some bat SARSr-CoVs have the potential to infect humans 5–7 . Here we report the identification and characterization of a new coronavirus (2019-nCoV), which caused an epidemic of acute respiratory syndrome in humans in Wuhan, China. The epidemic, which started on 12 December 2019, had caused 2,794 laboratory-confirmed infections including 80 deaths by 26 January 2020. Full-length genome sequences were obtained from five patients at an early stage of the outbreak. The sequences are almost identical and share 79.6% sequence identity to SARS-CoV. Furthermore, we show that 2019-nCoV is 96% identical at the whole-genome level to a bat coronavirus. Pairwise protein sequence analysis of seven conserved non-structural proteins domains show that this virus belongs to the species of SARSr-CoV. In addition, 2019-nCoV virus isolated from the bronchoalveolar lavage fluid of a critically ill patient could be neutralized by sera from several patients. Notably, we confirmed that 2019-nCoV uses the same cell entry receptor—angiotensin converting enzyme II (ACE2)—as SARS-CoV.
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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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              Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China.

              Y Guan (2003)
              A novel coronavirus (SCoV) is the etiological agent of severe acute respiratory syndrome (SARS). SCoV-like viruses were isolated from Himalayan palm civets found in a live-animal market in Guangdong, China. Evidence of virus infection was also detected in other animals (including a raccoon dog, Nyctereutes procyonoides) and in humans working at the same market. All the animal isolates retain a 29-nucleotide sequence that is not found in most human isolates. The detection of SCoV-like viruses in small, live wild mammals in a retail market indicates a route of interspecies transmission, although the natural reservoir is not known.
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                Author and article information

                Contributors
                daszak@ecohealthalliance.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                9 August 2022
                9 August 2022
                2022
                : 13
                : 4380
                Affiliations
                [1 ]GRID grid.420826.a, ISNI 0000 0004 0409 4702, EcoHealth Alliance, ; New York, NY USA
                [2 ]GRID grid.22448.38, ISNI 0000 0004 1936 8032, Department of Environmental Science and Policy, , George Mason University, ; Fairfax, VA USA
                [3 ]GRID grid.428397.3, ISNI 0000 0004 0385 0924, Programme in Emerging Infectious Diseases, , Duke-NUS Medical School, ; Singapore, Singapore
                [4 ]GRID grid.9227.e, ISNI 0000000119573309, Wuhan Institute of Virology, , Chinese Academy of Sciences, ; Wuhan, China
                Author information
                http://orcid.org/0000-0002-1141-6816
                http://orcid.org/0000-0002-5089-1134
                http://orcid.org/0000-0002-3120-4802
                http://orcid.org/0000-0002-5614-7496
                http://orcid.org/0000-0003-2752-0535
                http://orcid.org/0000-0001-8089-163X
                http://orcid.org/0000-0003-3211-1875
                http://orcid.org/0000-0002-2046-5695
                Article
                31860
                10.1038/s41467-022-31860-w
                9363439
                35945197
                829413b0-a3dd-4899-aeb6-4f963e9bc42f
                © The Author(s) 2022

                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 August 2021
                : 15 June 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000774, United States Department of Defense | Defense Threat Reduction Agency (DTRA);
                Award ID: HDTRA1-17-0064
                Award ID: HDTRA1-17-0064
                Award ID: HDTRA1-17-0064
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001349, MOH | National Medical Research Council (NMRC);
                Award ID: MOH-OFIRG19MAY-0011
                Award ID: COVID19RF-003
                Award ID: COVID19RF-003
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001459, Ministry of Education - Singapore (MOE);
                Award ID: MOE2019-T2-2-130
                Award ID: MOE2019-T2-2-130
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);
                Award ID: R01AI110964
                Award ID: NIAID-CREID U01AI151797
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)
                Funded by: FundRef https://doi.org/10.13039/501100001381, National Research Foundation Singapore (National Research Foundation-Prime Minister's office, Republic of Singapore);
                Award ID: NRF2012NRF-CRP001-056
                Award ID: NRF2016NRF-NSFC002-013
                Award Recipient :
                Funded by: National Research Foundation Singapore (National Research Foundation-Prime Minister's office, Republic of Singapore)
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                ecological modelling,ecological epidemiology,viral infection
                Uncategorized
                ecological modelling, ecological epidemiology, viral infection

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