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Carlson CJ, Garnier R, Tiu A, Luby SP, Bansal S. Strategic vaccine stockpiles for regional epidemics of emerging viruses: A geospatial modeling framework. Vaccine 2024:S0264-410X(24)00690-X. [PMID: 38902187 DOI: 10.1016/j.vaccine.2024.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 06/03/2024] [Accepted: 06/05/2024] [Indexed: 06/22/2024]
Abstract
Multinational epidemics of emerging infectious diseases are increasingly common, due to anthropogenic pressure on ecosystems and the growing connectivity of human populations. Early and efficient vaccination can contain outbreaks and prevent mass mortality, but optimal vaccine stockpiling strategies are dependent on pathogen characteristics, reservoir ecology, and epidemic dynamics. Here, we model major regional outbreaks of Nipah virus and Middle East respiratory syndrome, and use these to develop a generalized framework for estimating vaccine stockpile needs based on spillover geography, spatially-heterogeneous healthcare capacity and spatially-distributed human mobility networks. Because outbreak sizes were highly skewed, we found that most outbreaks were readily contained (median stockpile estimate for MERS-CoV: 2,089 doses; Nipah: 1,882 doses), but the maximum estimated stockpile need in a highly unlikely large outbreak scenario was 2-3 orders of magnitude higher (MERS-CoV: ∼87,000 doses; Nipah ∼ 1.1 million doses). Sensitivity analysis revealed that stockpile needs were more dependent on basic epidemiological parameters (i.e., death and recovery rate) and healthcare availability than any uncertainty related to vaccine efficacy or deployment strategy. Our results highlight the value of descriptive epidemiology for real-world modeling applications, and suggest that stockpile allocation should consider ecological, epidemiological, and social dimensions of risk.
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Affiliation(s)
- Colin J Carlson
- Department of Biology, Georgetown University; Department of Epidemiology of Microbial Diseases, Yale University School of Public Health
| | | | - Andrew Tiu
- Department of Biology, Georgetown University
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2
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Celone M, Beeman S, Han BA, Potter AM, Pecor DB, Okech B, Pollett S. Understanding transmission risk and predicting environmental suitability for Mayaro Virus in Central and South America. PLoS Negl Trop Dis 2024; 18:e0011859. [PMID: 38194417 PMCID: PMC10775973 DOI: 10.1371/journal.pntd.0011859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 12/12/2023] [Indexed: 01/11/2024] Open
Abstract
Mayaro virus (MAYV) is a mosquito-borne Alphavirus that is widespread in South America. MAYV infection often presents with non-specific febrile symptoms but may progress to debilitating chronic arthritis or arthralgia. Despite the pandemic threat of MAYV, its true distribution remains unknown. The objective of this study was to clarify the geographic distribution of MAYV using an established risk mapping framework. This consisted of generating evidence consensus scores for MAYV presence, modeling the potential distribution of MAYV in select countries across Central and South America, and estimating the population residing in areas suitable for MAYV transmission. We compiled a georeferenced compendium of MAYV occurrence in humans, animals, and arthropods. Based on an established evidence consensus framework, we integrated multiple information sources to assess the total evidence supporting ongoing transmission of MAYV within each country in our study region. We then developed high resolution maps of the disease's estimated distribution using a boosted regression tree approach. Models were developed using nine climatic and environmental covariates that are related to the MAYV transmission cycle. Using the output of our boosted regression tree models, we estimated the total population living in regions suitable for MAYV transmission. The evidence consensus scores revealed high or very high evidence of MAYV transmission in several countries including Brazil (especially the states of Mato Grosso and Goiás), Venezuela, Peru, Trinidad and Tobago, and French Guiana. According to the boosted regression tree models, a substantial region of South America is suitable for MAYV transmission, including north and central Brazil, French Guiana, and Suriname. Some regions (e.g., Guyana) with only moderate evidence of known transmission were identified as highly suitable for MAYV. We estimate that approximately 58.9 million people (95% CI: 21.4-100.4) in Central and South America live in areas that may be suitable for MAYV transmission, including 46.2 million people (95% CI: 17.6-68.9) in Brazil. Our results may assist in prioritizing high-risk areas for vector control, human disease surveillance and ecological studies.
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Affiliation(s)
- Michael Celone
- Department of Preventive Medicine & Biostatistics, Uniformed Services University of the Health Sciences, F. Edward Hébert School of Medicine, Bethesda, Maryland, United States of America
| | - Sean Beeman
- Department of Preventive Medicine & Biostatistics, Uniformed Services University of the Health Sciences, F. Edward Hébert School of Medicine, Bethesda, Maryland, United States of America
| | - Barbara A. Han
- Cary Institute of Ecosystem Studies, Millbrook, New York, United States of America
| | - Alexander M. Potter
- One Health Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
- Walter Reed Biosystematics Unit, Smithsonian Museum Support Center, Suitland, Maryland, United States of America
- Department of Entomology, Smithsonian Institution—National Museum of Natural History (NMNH), Washington, DC, United States of America
| | - David B. Pecor
- One Health Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
- Walter Reed Biosystematics Unit, Smithsonian Museum Support Center, Suitland, Maryland, United States of America
- Department of Entomology, Smithsonian Institution—National Museum of Natural History (NMNH), Washington, DC, United States of America
| | - Bernard Okech
- Department of Preventive Medicine & Biostatistics, Uniformed Services University of the Health Sciences, F. Edward Hébert School of Medicine, Bethesda, Maryland, United States of America
| | - Simon Pollett
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, United States of America
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3
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Celone M, Potter AM, Han BA, Beeman SP, Okech B, Forshey B, Dunford J, Rutherford G, Mita-Mendoza NK, Estallo EL, Khouri R, de Siqueira IC, Petersen K, Maves RC, Anyamba A, Pollett S. A geopositioned and evidence-graded pan-species compendium of Mayaro virus occurrence. Sci Data 2023; 10:460. [PMID: 37452060 PMCID: PMC10349107 DOI: 10.1038/s41597-023-02302-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Mayaro Virus (MAYV) is an emerging health threat in the Americas that can cause febrile illness as well as debilitating arthralgia or arthritis. To better understand the geographic distribution of MAYV risk, we developed a georeferenced database of MAYV occurrence based on peer-reviewed literature and unpublished reports. Here we present this compendium, which includes both point and polygon locations linked to occurrence data documented from its discovery in 1954 until 2022. We describe all methods used to develop the database including data collection, georeferencing, management and quality-control. We also describe a customized grading system used to assess the quality of each study included in our review. The result is a comprehensive, evidence-graded database of confirmed MAYV occurrence in humans, non-human animals, and arthropods to-date, containing 262 geo-positioned occurrences in total. This database - which can be updated over time - may be useful for local spill-over risk assessment, epidemiological modelling to understand key transmission dynamics and drivers of MAYV spread, as well as identification of major surveillance gaps.
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Affiliation(s)
- Michael Celone
- Uniformed Services University of the Health Sciences, F. Edward Hébert School of Medicine, Department of Preventive Medicine & Biostatistics, Bethesda, Maryland, 20814, USA.
| | - Alexander M Potter
- Department of Entomology, Walter Reed Army Institute of Research, Silver Spring, Maryland, 20910, USA
- Walter Reed Biosystematics Unit, Suitland, Maryland, 20746, USA
| | - Barbara A Han
- Cary Institute of Ecosystem Studies, Millbrook, New York, 12545, USA
| | - Sean P Beeman
- Uniformed Services University of the Health Sciences, F. Edward Hébert School of Medicine, Department of Preventive Medicine & Biostatistics, Bethesda, Maryland, 20814, USA
| | - Bernard Okech
- Uniformed Services University of the Health Sciences, F. Edward Hébert School of Medicine, Department of Preventive Medicine & Biostatistics, Bethesda, Maryland, 20814, USA
| | - Brett Forshey
- Armed Forces Health Surveillance Division, Silver Spring, Maryland, 20904, USA
| | - James Dunford
- Uniformed Services University of the Health Sciences, F. Edward Hébert School of Medicine, Department of Preventive Medicine & Biostatistics, Bethesda, Maryland, 20814, USA
| | - George Rutherford
- Institute for Global Health Sciences, University of California San Francisco, San Francisco, California, 94158, USA
| | | | - Elizabet Lilia Estallo
- Instituto de Investigaciones Biológicas y Tecnológicas, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)-Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Ricardo Khouri
- Instituto Gonçalo Moniz-Fiocruz, R. Waldemar Falcão, Salvador-BA, Brazil
| | | | - Kyle Petersen
- Uniformed Services University of the Health Sciences, F. Edward Hébert School of Medicine, Department of Preventive Medicine & Biostatistics, Bethesda, Maryland, 20814, USA
| | - Ryan C Maves
- Section of Infectious Diseases, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Assaf Anyamba
- Geospatial Science and Human Security Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, Tennessee, 37830, USA
| | - Simon Pollett
- Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD, USA.
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4
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Badr HS, Zaitchik BF, Kerr GH, Nguyen NLH, Chen YT, Hinson P, Colston JM, Kosek MN, Dong E, Du H, Marshall M, Nixon K, Mohegh A, Goldberg DL, Anenberg SC, Gardner LM. Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic. Sci Data 2023; 10:367. [PMID: 37286690 PMCID: PMC10245354 DOI: 10.1038/s41597-023-02276-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/30/2023] [Indexed: 06/09/2023] Open
Abstract
An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.
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Affiliation(s)
- Hamada S Badr
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Benjamin F Zaitchik
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Gaige H Kerr
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA
| | - Nhat-Lan H Nguyen
- College of Arts and Sciences, University of Virginia, Charlottesville, VA, 22903, USA
| | - Yen-Ting Chen
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Patrick Hinson
- College of Arts and Sciences, University of Virginia, Charlottesville, VA, 22903, USA
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Josh M Colston
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Margaret N Kosek
- Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Ensheng Dong
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Hongru Du
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Maximilian Marshall
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kristen Nixon
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Arash Mohegh
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA
- Health & Exposure Assessment Branch, California Air Resources Board, Sacramento, CA, 95812, USA
| | - Daniel L Goldberg
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA
| | - Susan C Anenberg
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA
| | - Lauren M Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
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Gumanova NG, Gorshkov AU, Bogdanova NL, Korolev AI, Drapkina OM. Detection of Anti-SARS-CoV-2-S1 RBD-Specific Antibodies Prior to and during the Pandemic in 2011–2021 and COVID-19 Observational Study in 2019–2021. Vaccines (Basel) 2022; 10:vaccines10040581. [PMID: 35455330 PMCID: PMC9032149 DOI: 10.3390/vaccines10040581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/04/2022] [Accepted: 04/08/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Longitudinal surveys to monitor the seroprevalence are required to support efforts for assessment of the levels of endemic stability in certain countries. We investigated seroprevalence of anti-SARS-CoV-2-S1 receptor-binding domain (RBD)-specific antibodies in the serum samples in 2011–2021, including a cohort study of 2019–2021, to evaluate the vaccination and anti-IgG-SARS-CoV-2–S1 RBD-positive statuses to assess the resistance and severity of COVID-19. Materials and Methods: Anti-SARS-CoV-2-S1 RBD-specific antibodies were assayed in the serum samples (N = 565) randomly selected from various cohorts previously recruited from 2011 to 2021 from the city of Moscow and Moscow Region. Among them there were the participants (N = 310) recruited in 2019–2021 with an endpoint of 30 October 2021 when these participants were interviewed over phone with relevant questionnaire. Results: Obtained data indicated a percentage of 3–6% of SARS-CoV-2-S1 RBD-specific antibodies detected in participants recruited in 2011–2019. The percentage of SARS-CoV-2-S1 RBD-specific antibodies was increased to 16.5% in 2020 and to 46% in 2021. The vaccination rate of 238 respondents of this cohort was 58% from August 2020 to October 2021. In total, 12% of respondents were hospitalized. The morbidity rate in the subgroup of anti-SARS-CoV-2-S1 RBD-positive respondents was 5.4-fold higher than that in the subgroup of vaccinated respondents. Conclusions: A small percentage of SARS-CoV-2-S1 RBD-specific antibodies detected in 2011–2019 indicated possible spreading of coronaviruses during the pre-pandemic period. Collective immunity in Moscow and the Moscow region was able to reach 69% from August 2020 to October 2021 if this rate is added to the rate of not vaccinated SARS-CoV-2-S1 RBD-positive subjects.
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6
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Jelinek HF, Mousa M, Alefishat E, Osman W, Spence I, Bu D, Feng SF, Byrd J, Magni PA, Sahibzada S, Tay GK, Alsafar HS. Evolution, Ecology, and Zoonotic Transmission of Betacoronaviruses: A Review. Front Vet Sci 2021; 8:644414. [PMID: 34095271 PMCID: PMC8173069 DOI: 10.3389/fvets.2021.644414] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/25/2021] [Indexed: 12/18/2022] Open
Abstract
Coronavirus infections have been a part of the animal kingdom for millennia. The difference emerging in the twenty-first century is that a greater number of novel coronaviruses are being discovered primarily due to more advanced technology and that a greater number can be transmitted to humans, either directly or via an intermediate host. This has a range of effects from annual infections that are mild to full-blown pandemics. This review compares the zoonotic potential and relationship between MERS, SARS-CoV, and SARS-CoV-2. The role of bats as possible host species and possible intermediate hosts including pangolins, civets, mink, birds, and other mammals are discussed with reference to mutations of the viral genome affecting zoonosis. Ecological, social, cultural, and environmental factors that may play a role in zoonotic transmission are considered with reference to SARS-CoV, MERS, and SARS-CoV-2 and possible future zoonotic events.
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Affiliation(s)
- Herbert F. Jelinek
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, College of Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Center of Heath Engineering Innovation, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mira Mousa
- Nuffield Department of Women's and Reproduction Health, Oxford University, Oxford, United Kingdom
| | - Eman Alefishat
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Pharmacology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biopharmaceutics and Clinical Pharmacy, School of Pharmacy, The University of Jordan, Amman, Jordan
| | - Wael Osman
- Department of Chemistry, College of Arts and Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Ian Spence
- Discipline of Pharmacology, University of Sydney, Sydney, NSW, Australia
| | - Dengpan Bu
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing, China
| | - Samuel F. Feng
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Jason Byrd
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States
| | - Paola A. Magni
- Discipline of Medical, Molecular and Forensic Sciences, Murdoch University, Murdoch, WA, Australia
- Murdoch University Singapore, King's Centre, Singapore, Singapore
| | - Shafi Sahibzada
- Antimicrobial Resistance and Infectious Diseases Laboratory, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Guan K. Tay
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Division of Psychiatry, Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, WA, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Habiba S. Alsafar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, College of Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Genetics and Molecular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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7
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Mondal S, Ravishankar Rai V. Molecular profiling and anti-infective potency of endophytic actinomycetes inhabiting Madhuca insignis Radlk., from Western Ghats of India. J Genet Eng Biotechnol 2021; 19:36. [PMID: 33625604 PMCID: PMC7903210 DOI: 10.1186/s43141-021-00135-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 02/15/2021] [Indexed: 12/22/2022]
Abstract
Background Endophytic actinomycetes are well known for their diverse bioactive entities and considered as an important source for drug development research. Results We isolated and identified four potential endophytic Streptomyces species, i.e., Streptomyces misionensis MI22, Streptomyces roietensis MI24, Streptomyces glaucescens MI29, and Streptomyces sp. MI04 inhabiting Madhuca insignis by its characteristic morphological features and 16S rRNA gene sequence analysis. S. misionensis MI22 exhibits a broad spectrum of anti-microbial activity against methicillin-resistant Staphylococcus aureus (25.00 ± 1.00 mm) followed by Bacillus subtilis (23.66 ± 0.57 mm), Escherichia coli (22.00 ± 0.00 mm), and Candida albicans (18.00 ± 0.00 mm). Minimum inhibitory concentrations of the ethyl acetate fraction of S. misionensis MI22 against test pathogens were ranged from 25 to 100 μg/mL. Indeed, strain MI22 also exhibited significant anti-proliferative activity against HeLa cell line with IC50 value 98 μg/mL and showed no cytotoxicity effect to the normal human embryonic kidney cell line in the MTT assay. The anti-microbial metabolites from strain MI22 were detected at Rf 0.55 as depicted by the inhibition zone on the intensive band in TLC-bioautography assay. Conclusion The study indicates that, anti-microbial metabolites of these endophytic Streptomyces species, especially S. misionensis MI22 as a prolific source to discover novel bioactive metabolites to combat multidrug-resistant pathogens.
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Affiliation(s)
- Soma Mondal
- Department of Studies in Microbiology, University of Mysore, Manasagangotri, Mysuru, Karnataka, 570006, India
| | - V Ravishankar Rai
- Department of Studies in Microbiology, University of Mysore, Manasagangotri, Mysuru, Karnataka, 570006, India.
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8
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Rader B, Scarpino SV, Nande A, Hill AL, Adlam B, Reiner RC, Pigott DM, Gutierrez B, Zarebski AE, Shrestha M, Brownstein JS, Castro MC, Dye C, Tian H, Pybus OG, Kraemer MUG. Crowding and the shape of COVID-19 epidemics. Nat Med 2020. [PMID: 33020651 DOI: 10.1101/2020.04.15.20064980] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is straining public health systems worldwide, and major non-pharmaceutical interventions have been implemented to slow its spread1-4. During the initial phase of the outbreak, dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily determined by human mobility from Wuhan, China5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic transmission is lacking7. In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to investigate the role of climate, urbanization and variation in interventions. We show that the degree to which cases of COVID-19 are compressed into a short period of time (peakedness of the epidemic) is strongly shaped by population aggregation and heterogeneity, such that epidemics in crowded cities are more spread over time, and crowded cities have larger total attack rates than less populated cities. Observed differences in the peakedness of epidemics are consistent with a meta-population model of COVID-19 that explicitly accounts for spatial hierarchies. We paired our estimates with globally comprehensive data on human mobility and predict that crowded cities worldwide could experience more prolonged epidemics.
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Affiliation(s)
- Benjamin Rader
- Computational Epidemiology Lab, Boston Children's Hospital, Boston MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston MA, USA
| | - Samuel V Scarpino
- Network Science Institute, Northeastern University, Boston MA, USA.
- ISI Foundation, Turin, Italy.
- Santa Fe Institute, Santa Fe NM, USA.
| | - Anjalika Nande
- Program for Evolutionary Dynamics, Harvard University, Cambridge MA, USA
| | - Alison L Hill
- Program for Evolutionary Dynamics, Harvard University, Cambridge MA, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore MD, USA
| | - Ben Adlam
- Program for Evolutionary Dynamics, Harvard University, Cambridge MA, USA
| | - Robert C Reiner
- Department of Health Metrics, University of Washington, Seattle WA, USA
- Institute for Health Metrics and Evaluation, University of Washington, Seattle WA, USA
| | - David M Pigott
- Department of Health Metrics, University of Washington, Seattle WA, USA
- Institute for Health Metrics and Evaluation, University of Washington, Seattle WA, USA
| | - Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, UK
- School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | | | - Munik Shrestha
- Network Science Institute, Northeastern University, Boston MA, USA
| | - John S Brownstein
- Computational Epidemiology Lab, Boston Children's Hospital, Boston MA, USA
- Harvard Medical School, Boston MA, USA
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston MA, USA
| | | | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Science, The Royal Veterinary College, London, UK.
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9
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Farooq S, Ngaini Z. Natural and Synthetic Drugs as Potential Treatment for Coronavirus Disease 2019 (COVID-2019). CHEMISTRY AFRICA 2020. [PMCID: PMC7682129 DOI: 10.1007/s42250-020-00203-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has become a global pandemic in a short period, where a tragically large number of human lives being lost. It is an infectious pandemic that recently infected more than two hundred countries in the world. Many potential treatments have been introduced, which are considered potent antiviral drugs and commonly reported as herbal or traditional and medicinal treatments. A variety of bioactive metabolites extracts from natural herbal have been reported for coronaviruses with some effective results. Food and Drug Administration (FDA) has approved numerous drugs to be introduced against COVID-19, which commercially available as antiviral drugs and vaccines. In this study, a comprehensive review is discussed on the potential antiviral remedies based on natural and synthetic drugs. This review highlighted the potential remedies of COVID-19 which successfully applied to patients with high cytopathic inhibition potency for cell-to-cell spread and replication of coronavirus.
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10
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Abstract
The coronavirus disease 2019 (COVID-19) pandemic is straining public health systems worldwide, and major non-pharmaceutical interventions have been implemented to slow its spread1-4. During the initial phase of the outbreak, dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily determined by human mobility from Wuhan, China5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic transmission is lacking7. In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to investigate the role of climate, urbanization and variation in interventions. We show that the degree to which cases of COVID-19 are compressed into a short period of time (peakedness of the epidemic) is strongly shaped by population aggregation and heterogeneity, such that epidemics in crowded cities are more spread over time, and crowded cities have larger total attack rates than less populated cities. Observed differences in the peakedness of epidemics are consistent with a meta-population model of COVID-19 that explicitly accounts for spatial hierarchies. We paired our estimates with globally comprehensive data on human mobility and predict that crowded cities worldwide could experience more prolonged epidemics.
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11
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Nasir A, Shaukat K, Hameed IA, Luo S, Alam TM, Iqbal F. A Bibliometric Analysis of Corona Pandemic in Social Sciences: A Review of Influential Aspects and Conceptual Structure. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:133377-133402. [PMID: 34812340 PMCID: PMC8545329 DOI: 10.1109/access.2020.3008733] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 07/06/2020] [Indexed: 05/07/2023]
Abstract
Corona pandemic has affected the whole world, and it is a highly researched area in biological sciences. As the current pandemic has affected countries socially and economically, the purpose of this bibliometric analysis is to provide a holistic review of the corona pandemic in the field of social sciences. This study aims to highlight significant, influential aspects, research streams, and themes. We have reviewed 395 journal articles related to coronavirus in the field of social sciences from 2003 to 2020. We have deployed 'biblioshiny' a web-interface of the 'bibliometrix 3.0' package of R-studio to conduct bibliometric analysis and visualization. In the field of social sciences, we have reported influential aspects of coronavirus literature. We have found that the 'Morbidity and Mortality Weekly Report' is the top journal. The core article of coronavirus literature is 'Guidelines for preventing health-care-associated pneumonia'. The most commonly used word, in titles, abstracts, author's keywords, and keywords plus, is 'SARS'. Top affiliation is 'The University of Hong Kong'. Hong Kong is a leading country based on citations, and the USA is on top based on total publications. We have used a conceptual framework to identify potential research streams and themes in coronavirus literature. Four research streams are found by deploying a co-occurrence network. These research streams are 'Social and economic effects of epidemic disease', 'Infectious disease calamities and control', 'Outbreak of COVID 19,' and 'Infectious diseases and the role of international organizations'. Finally, a thematic map is used to provide a holistic understanding by dividing significant themes into basic or transversal, emerging or declining, motor, highly developed, but isolated themes. These themes and subthemes have proposed future directions and critical areas of research.
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Affiliation(s)
- Adeel Nasir
- Department of Management SciencesLahore College for Women UniversityLahore54000Pakistan
| | - Kamran Shaukat
- School of Electrical Engineering and ComputingThe University of NewcastleCallaghanNSW2308Australia
- Punjab University College of Information Technology, University of the PunjabLahore54590Pakistan
| | - Ibrahim A. Hameed
- Department of ICT and Natural SciencesNorwegian University of Science and Technology7491TrondheimNorway
| | - Suhuai Luo
- School of Electrical Engineering and ComputingThe University of NewcastleCallaghanNSW2308Australia
| | - Talha Mahboob Alam
- Department of Computer ScienceUniversity of Engineering and TechnologyLahore54890Pakistan
| | - Farhat Iqbal
- Punjab University College of Information Technology, University of the PunjabLahore54590Pakistan
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12
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Kraemer MUG, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM, du Plessis L, Faria NR, Li R, Hanage WP, Brownstein JS, Layan M, Vespignani A, Tian H, Dye C, Pybus OG, Scarpino SV. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 2020. [PMID: 32213647 DOI: 10.1126/science:abb4218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
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Affiliation(s)
- Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.
- Harvard Medical School, Harvard University, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Chia-Hung Yang
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, UK
- School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, Southampton, UK
| | - Brennan Klein
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - David M Pigott
- Institute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, WA, USA
| | | | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - Ruoran Li
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - John S Brownstein
- Harvard Medical School, Harvard University, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Maylis Layan
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Sorbonne Université, Paris, France
| | - Alessandro Vespignani
- Network Science Institute, Northeastern University, Boston, MA, USA
- ISI Foundation, Turin, Italy
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | | | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK
| | - Samuel V Scarpino
- Network Science Institute, Northeastern University, Boston, MA, USA.
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13
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Kraemer MUG, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM, du Plessis L, Faria NR, Li R, Hanage WP, Brownstein JS, Layan M, Vespignani A, Tian H, Dye C, Pybus OG, Scarpino SV. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 2020; 368:493-497. [PMID: 32213647 PMCID: PMC7146642 DOI: 10.1126/science.abb4218] [Citation(s) in RCA: 1418] [Impact Index Per Article: 354.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 03/23/2020] [Indexed: 12/14/2022]
Abstract
The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
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Affiliation(s)
- Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.
- Harvard Medical School, Harvard University, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Chia-Hung Yang
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, UK
- School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, Southampton, UK
| | - Brennan Klein
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - David M Pigott
- Institute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, WA, USA
| | | | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - Ruoran Li
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - John S Brownstein
- Harvard Medical School, Harvard University, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Maylis Layan
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Sorbonne Université, Paris, France
| | - Alessandro Vespignani
- Network Science Institute, Northeastern University, Boston, MA, USA
- ISI Foundation, Turin, Italy
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | | | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK
| | - Samuel V Scarpino
- Network Science Institute, Northeastern University, Boston, MA, USA.
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14
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Kraemer MUG, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM, du Plessis L, Faria NR, Li R, Hanage WP, Brownstein JS, Layan M, Vespignani A, Tian H, Dye C, Pybus OG, Scarpino SV. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 2020; 368:493-497. [PMID: 32213647 DOI: 10.5281/zenodo.3714914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 03/23/2020] [Indexed: 05/21/2023]
Abstract
The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
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Affiliation(s)
- Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.
- Harvard Medical School, Harvard University, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Chia-Hung Yang
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, UK
- School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, Southampton, UK
| | - Brennan Klein
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - David M Pigott
- Institute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, WA, USA
| | | | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - Ruoran Li
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - John S Brownstein
- Harvard Medical School, Harvard University, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Maylis Layan
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
- Sorbonne Université, Paris, France
| | - Alessandro Vespignani
- Network Science Institute, Northeastern University, Boston, MA, USA
- ISI Foundation, Turin, Italy
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | | | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK
| | - Samuel V Scarpino
- Network Science Institute, Northeastern University, Boston, MA, USA.
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15
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Thabti I, Albert Q, Philippot S, Dupire F, Westerhuis B, Fontanay S, Risler A, Kassab T, Elfalleh W, Aferchichi A, Varbanov M. Advances on Antiviral Activity of Morus spp. Plant Extracts: Human Coronavirus and Virus-Related Respiratory Tract Infections in the Spotlight. Molecules 2020; 25:molecules25081876. [PMID: 32325742 PMCID: PMC7221944 DOI: 10.3390/molecules25081876] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 04/10/2020] [Accepted: 04/15/2020] [Indexed: 12/13/2022] Open
Abstract
(1) Background: Viral respiratory infections cause life-threatening diseases in millions of people worldwide every year. Human coronavirus and several picornaviruses are responsible for worldwide epidemic outbreaks, thus representing a heavy burden to their hosts. In the absence of specific treatments for human viral infections, natural products offer an alternative in terms of innovative drug therapies. (2) Methods: We analyzed the antiviral properties of the leaves and stem bark of the mulberry tree (Morus spp.). We compared the antiviral activity of Morus spp. on enveloped and nonenveloped viral pathogens, such as human coronavirus (HCoV 229E) and different members of the Picornaviridae family-human poliovirus 1, human parechovirus 1 and 3, and human echovirus 11. The antiviral activity of 12 water and water-alcohol plant extracts of the leaves and stem bark of three different species of mulberry-Morus alba var. alba, Morus alba var. rosa, and Morus rubra-were evaluated. We also evaluated the antiviral activities of kuwanon G against HCoV-229E. (3) Results: Our results showed that several extracts reduced the viral titer and cytopathogenic effects (CPE). Leaves' water-alcohol extracts exhibited maximum antiviral activity on human coronavirus, while stem bark and leaves' water and water-alcohol extracts were the most effective on picornaviruses. (4) Conclusions: The analysis of the antiviral activities of Morus spp. offer promising applications in antiviral strategies.
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Affiliation(s)
- Inès Thabti
- L2CM, Université de Lorraine, CNRS, F-54000 Nancy, France; (I.T.); (Q.A.); (S.P.); (F.D.); (S.F.); (A.R.); (T.K.)
- Laboratoire d’Aridoculture et Cultures Oasiennes, Institut des régions Arides de Médenine, Route el Djorf, Médenine 4119, Tunisia; (W.E.); (A.A.)
| | - Quentin Albert
- L2CM, Université de Lorraine, CNRS, F-54000 Nancy, France; (I.T.); (Q.A.); (S.P.); (F.D.); (S.F.); (A.R.); (T.K.)
| | - Stéphanie Philippot
- L2CM, Université de Lorraine, CNRS, F-54000 Nancy, France; (I.T.); (Q.A.); (S.P.); (F.D.); (S.F.); (A.R.); (T.K.)
| | - François Dupire
- L2CM, Université de Lorraine, CNRS, F-54000 Nancy, France; (I.T.); (Q.A.); (S.P.); (F.D.); (S.F.); (A.R.); (T.K.)
| | - Brenda Westerhuis
- Department of Medical Microbiology, Academic Medical Center, Meibergdreef 15, 1105 AZ Amsterdam, The Netherlands;
- Department of Viroscience, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| | - Stéphane Fontanay
- L2CM, Université de Lorraine, CNRS, F-54000 Nancy, France; (I.T.); (Q.A.); (S.P.); (F.D.); (S.F.); (A.R.); (T.K.)
- INSA de Lyon, Université de Lyon, CNRS, UMR5240, F-69622 Villeurbanne, France
| | - Arnaud Risler
- L2CM, Université de Lorraine, CNRS, F-54000 Nancy, France; (I.T.); (Q.A.); (S.P.); (F.D.); (S.F.); (A.R.); (T.K.)
| | - Thomas Kassab
- L2CM, Université de Lorraine, CNRS, F-54000 Nancy, France; (I.T.); (Q.A.); (S.P.); (F.D.); (S.F.); (A.R.); (T.K.)
| | - Walid Elfalleh
- Laboratoire d’Aridoculture et Cultures Oasiennes, Institut des régions Arides de Médenine, Route el Djorf, Médenine 4119, Tunisia; (W.E.); (A.A.)
- Energy, Water, Environment and Process Laboratory, (LR18ES35), National Engineering School of Gabes, University of Gabes, Gabes 6072, Tunisia
| | - Ali Aferchichi
- Laboratoire d’Aridoculture et Cultures Oasiennes, Institut des régions Arides de Médenine, Route el Djorf, Médenine 4119, Tunisia; (W.E.); (A.A.)
| | - Mihayl Varbanov
- L2CM, Université de Lorraine, CNRS, F-54000 Nancy, France; (I.T.); (Q.A.); (S.P.); (F.D.); (S.F.); (A.R.); (T.K.)
- Correspondence:
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16
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Kraemer MU, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM, du Plessis L, Faria NR, Li R, Hanage WP, Brownstein JS, Layan M, Vespignani A, Tian H, Dye C, Cauchemez S, Pybus OG, Scarpino SV. The effect of human mobility and control measures on the COVID-19 epidemic in China. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.03.02.20026708. [PMID: 32511452 PMCID: PMC7239080 DOI: 10.1101/2020.03.02.20026708] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.
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Affiliation(s)
- Moritz U.G. Kraemer
- Department of Zoology, University of Oxford, United Kingdom
- Harvard Medical School, Harvard University, Boston, United States
- Boston Children’s Hospital, Boston, United States
| | - Chia-Hung Yang
- Network Science Institute, Northeastern University, Boston, United States
| | - Bernardo Gutierrez
- Department of Zoology, University of Oxford, United Kingdom
- School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, Southampton, United Kingdom
| | - Brennan Klein
- Network Science Institute, Northeastern University, Boston, United States
| | - David M. Pigott
- Institute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, United States
| | | | | | - Nuno R. Faria
- Department of Zoology, University of Oxford, United Kingdom
| | - Ruoran Li
- Harvard T.H. Chan School of Public Health, Boston, United States
| | | | - John S. Brownstein
- Harvard Medical School, Harvard University, Boston, United States
- Boston Children’s Hospital, Boston, United States
| | - Maylis Layan
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | | | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | | | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | | | - Samuel V. Scarpino
- Network Science Institute, Northeastern University, Boston, United States
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17
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Ramshaw RE, Letourneau ID, Hong AY, Hon J, Morgan JD, Osborne JCP, Shirude S, Van Kerkhove MD, Hay SI, Pigott DM. A database of geopositioned Middle East Respiratory Syndrome Coronavirus occurrences. Sci Data 2019; 6:318. [PMID: 31836720 PMCID: PMC6911100 DOI: 10.1038/s41597-019-0330-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/15/2019] [Indexed: 12/21/2022] Open
Abstract
As a World Health Organization Research and Development Blueprint priority pathogen, there is a need to better understand the geographic distribution of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and its potential to infect mammals and humans. This database documents cases of MERS-CoV globally, with specific attention paid to zoonotic transmission. An initial literature search was conducted in PubMed, Web of Science, and Scopus; after screening articles according to the inclusion/exclusion criteria, a total of 208 sources were selected for extraction and geo-positioning. Each MERS-CoV occurrence was assigned one of the following classifications based upon published contextual information: index, unspecified, secondary, mammal, environmental, or imported. In total, this database is comprised of 861 unique geo-positioned MERS-CoV occurrences. The purpose of this article is to share a collated MERS-CoV database and extraction protocol that can be utilized in future mapping efforts for both MERS-CoV and other infectious diseases. More broadly, it may also provide useful data for the development of targeted MERS-CoV surveillance, which would prove invaluable in preventing future zoonotic spillover. Measurement(s) | Middle East Respiratory Syndrome • geographic location | Technology Type(s) | digital curation | Factor Type(s) | geographic distribution of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) • year | Sample Characteristic - Organism | Middle East respiratory syndrome-related coronavirus | Sample Characteristic - Location | Earth (planet) |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11108801
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Affiliation(s)
- Rebecca E Ramshaw
- Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States
| | - Ian D Letourneau
- Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States
| | - Amy Y Hong
- Bloomberg School of Public Health, Johns Hopkins University, 615N Wolfe St, Baltimore, MD, 21205, United States
| | - Julia Hon
- Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States
| | - Julia D Morgan
- Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States
| | - Joshua C P Osborne
- Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States
| | - Shreya Shirude
- Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States
| | - Maria D Van Kerkhove
- Department of Infectious Hazards Management, Health Emergencies Programme, World Health Organization, Avenue Appia 20, 1211, Geneva, Switzerland
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States.,Department of Health Metrics Sciences, School of Medicine, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States. .,Department of Health Metrics Sciences, School of Medicine, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA, United States.
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