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Natraj S, Bhide M, Yap N, Liu M, Seth A, Berman J, Glorioso C. COVID-19 activity risk calculator as a gamified public health intervention tool. Sci Rep 2023; 13:13056. [PMID: 37567913 PMCID: PMC10421890 DOI: 10.1038/s41598-023-40338-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 08/09/2023] [Indexed: 08/13/2023] Open
Abstract
The Coronavirus disease 2019 (COVID-19) pandemic, caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has impacted over 200 countries leading to hospitalizations and deaths of millions of people. Public health interventions, such as risk estimators, can reduce the spread of pandemics and epidemics through influencing behavior, which impacts risk of exposure and infection. Current publicly available COVID-19 risk estimation tools have had variable effectiveness during the pandemic due to their dependency on rapidly evolving factors such as community transmission levels and variants. There has also been confusion surrounding certain personal protective strategies such as risk reduction by mask-wearing and vaccination. In order to create a simple easy-to-use tool for estimating different individual risks associated with carrying out daily-life activity, we developed COVID-19 Activity Risk Calculator (CovARC). CovARC is a gamified public health intervention as users can "play with" how different risks associated with COVID-19 can change depending on several different factors when carrying out routine daily activities. Empowering the public to make informed, data-driven decisions about safely engaging in activities may help to reduce COVID-19 levels in the community. In this study, we demonstrate a streamlined, scalable and accurate COVID-19 risk calculation system. Our study also demonstrates the quantitative impact of vaccination and mask-wearing during periods of high case counts. Validation of this impact could inform and support policy decisions regarding case thresholds for mask mandates, and other public health interventions.
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Affiliation(s)
- Shreyasvi Natraj
- Department of Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Malhar Bhide
- Academics for the Future of Science Inc., Cambridge, MA, USA
| | - Nathan Yap
- Academics for the Future of Science Inc., Cambridge, MA, USA
| | - Meng Liu
- Department of Industrial and Manufacturing Engineering, Penn State University, State College, PA, USA
| | - Agrima Seth
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan Berman
- Department of Basic Science, New York Institute of Technology College of Osteopathic Medicine at Arkansas State University, Jonesboro, AR, USA
| | - Christin Glorioso
- Department of Anatomy, University of California, San Francisco, CA, USA.
- Academics for the Future of Science Inc., Cambridge, MA, USA.
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Paulino-Ramírez R, López P, Mueses S, Cuevas P, Jabier M, Rivera-Amill V. Genomic Surveillance of SARS-CoV-2 Variants in the Dominican Republic and Emergence of a Local Lineage. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20085503. [PMID: 37107785 PMCID: PMC10138544 DOI: 10.3390/ijerph20085503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/24/2023] [Accepted: 04/03/2023] [Indexed: 05/11/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an RNA virus that evolves over time, leading to new variants. In the current study, we assessed the genomic epidemiology of SARS-CoV-2 in the Dominican Republic. A total of 1149 SARS-CoV-2 complete genome nucleotide sequences from samples collected between March 2020 and mid-February 2022 in the Dominican Republic were obtained from the Global Initiative on Sharing All Influenza Data (GISAID) database. Phylogenetic relationships and evolution rates were analyzed using the maximum likelihood method and the Bayesian Markov chain Monte Carlo (MCMC) approach. The genotyping details (lineages) were obtained using the Pangolin web application. In addition, the web tools Coronapp, and Genome Detective Viral Tools, among others, were used to monitor epidemiological characteristics. Our results show that the most frequent non-synonymous mutation over the study period was D614G. Of the 1149 samples, 870 (75.74%) were classified into 8 relevant variants according to Pangolin/Scorpio. The first Variants Being Monitored (VBM) were detected in December 2020. Meanwhile, in 2021, the variants of concern Delta and Omicron were identified. The mean mutation rate was estimated to be 1.5523 × 10-3 (95% HPD: 1.2358 × 10-3, 1.8635 × 10-3) nucleotide substitutions per site. We also report the emergence of an autochthonous SARS-CoV-2 lineage, B.1.575.2, that circulated from October 2021 to January 2022, in co-circulation with the variants of concern Delta and Omicron. The impact of B.1.575.2 in the Dominican Republic was minimal, but it then expanded rapidly in Spain. A better understanding of viral evolution and genomic surveillance data will help to inform strategies to mitigate the impact on public health.
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Affiliation(s)
- Robert Paulino-Ramírez
- Instituto de Medicina Tropical y Salud Global, Universidad Iberoamericana, Research Hub, Santo Domingo 22333, Dominican Republic
- Correspondence:
| | - Pablo López
- RCMI Center for Research Resources, Ponce Research Institute, Ponce, PR 00716-2348, USA (V.R.-A.)
| | - Sayira Mueses
- Instituto de Medicina Tropical y Salud Global, Universidad Iberoamericana, Research Hub, Santo Domingo 22333, Dominican Republic
| | - Paula Cuevas
- Instituto de Medicina Tropical y Salud Global, Universidad Iberoamericana, Research Hub, Santo Domingo 22333, Dominican Republic
| | - Maridania Jabier
- Instituto de Medicina Tropical y Salud Global, Universidad Iberoamericana, Research Hub, Santo Domingo 22333, Dominican Republic
- Servicio Nacional de Salud (SNS), Ministry of Health, Santo Domingo 10201, Dominican Republic
| | - Vanessa Rivera-Amill
- RCMI Center for Research Resources, Ponce Research Institute, Ponce, PR 00716-2348, USA (V.R.-A.)
- Basic Sciences Department, School of Medicine, Ponce Health Sciences University, Ponce, PR 00716-2348, USA
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3
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Comparative genomics, evolutionary epidemiology, and RBD-hACE2 receptor binding pattern in B.1.1.7 (Alpha) and B.1.617.2 (Delta) related to their pandemic response in UK and India. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2022; 101:105282. [PMID: 35427787 PMCID: PMC9005225 DOI: 10.1016/j.meegid.2022.105282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 04/04/2022] [Accepted: 04/08/2022] [Indexed: 02/07/2023]
Abstract
Background The massive increase in COVID-19 infection had generated a second wave in India during May–June 2021 with a critical pandemic situation. The Delta variant (B.1.617.2) was a significant factor during the second wave. Conversely, the UK had passed through the crucial phase of the pandemic from November to December 2020 due to B.1.1.7. The study tried to comprehend the pandemic response in the UK and India to the spread of the B.1.1.7 (Alpha, UK) variant and B.1.617.2 (Delta, India) variant. Methods This study was performed in three directions to understand the pandemic response of the two emerging variants. First, we served comparative genomics, such as genome sequence submission patterns, mutational landscapes, and structural landscapes of significant mutations (N501Y, D614G, L452R, E484Q, and P681R). Second, we performed evolutionary epidemiology using molecular phylogenetics, scatter plots of the cluster evaluation, country-wise transmission pattern, and frequency pattern. Third, the receptor binding pattern was analyzed using the Wuhan reference strain and the other two variants. Results The study analyzed the country-wise and region-wise genome sequences and their submission pattern, molecular phylogenetics, scatter plot of the cluster evaluation, country-wise geographical distribution and transmission pattern, frequency pattern, entropy diversity, and mutational landscape of the two variants. The structural pattern was analyzed in the N501Y, D614G L452R, E484Q, and P681R mutations. The study found increased molecular interactivity between hACE2-RBD binding of B.1.1.7 and B.1.617.2 compared to the Wuhan reference strain. Our receptor binding analysis showed a similar indication pattern for hACE2-RBD of these two variants. However, B.1.617.2 offers slightly better stability in the hACE2-RBD binding pattern through MD simulation than B.1.1.7. Conclusion The increased hACE2-RBD binding pattern of B.1.1.7 and B.1.617.2 might help to increase the infectivity compared to the Wuhan reference strain.
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Zhou W, Xu C, Luo M, Wang P, Xu Z, Xue G, Jin X, Huang Y, Li Y, Nie H, Jiang Q, Anashkina AA. MutCov: A pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2. Comput Biol Med 2022; 145:105509. [PMID: 35421792 PMCID: PMC8993498 DOI: 10.1016/j.compbiomed.2022.105509] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 12/16/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), is a major threat to public health worldwide. Previous studies have shown that the spike protein of SARS-CoV-2 determines viral infectivity and major antigenicity. However, the spike protein has been undergoing various mutations, which bring a great challenge to the prevention and treatment of COVID-19. Here we present the MutCov, a pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2 by calculating the binding free energy between spike protein and angiotensin-converting enzyme 2 (ACE2) or neutralizing monoclonal antibody (mAb). The predicted infectivity and antigenicity were highly consistent with biologically experimental results, and demonstrated that the MutCov achieved good prediction performance. In conclusion, the MutCov is of high importance for systematically evaluating the effect of novel mutations and improving the prevention and treatment of COVID-19. The source code and installation instruction of MutCov are freely available at http://jianglab.org.cn/MutCov.
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Affiliation(s)
- Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Chang Xu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Meng Luo
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Zhaochun Xu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Guangfu Xue
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Xiyun Jin
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Yan Huang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Yiqun Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Huan Nie
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China,Corresponding author
| | - Anastasia A. Anashkina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia,Corresponding author
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Saravanan KA, Panigrahi M, Kumar H, Rajawat D, Nayak SS, Bhushan B, Dutt T. Role of genomics in combating COVID-19 pandemic. Gene 2022; 823:146387. [PMID: 35248659 PMCID: PMC8894692 DOI: 10.1016/j.gene.2022.146387] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 12/20/2022]
Abstract
The coronavirus disease 2019 (COVID-19) quickly swept over the world, becoming one of the most devastating outbreaks in human history. Being the first pandemic in the post-genomic era, advancements in genomics contributed significantly to scientific understanding and public health response to COVID-19. Genomic technologies have been employed by researchers all over the world to better understand the biology of SARS-CoV-2 and its origin, genomic diversity, and evolution. Worldwide genomic resources have greatly aided in the investigation of the COVID-19 pandemic. The pandemic has ushered in a new era of genomic surveillance, wherein scientists are tracking the changes of the SARS-CoV-2 genome in real-time at the international and national levels. Availability of genomic and proteomic information enables the rapid development of molecular diagnostics and therapeutics. The advent of high-throughput sequencing and genome editing technologies led to the development of modern vaccines. We briefly discuss the impact of genomics in the ongoing COVID-19 pandemic in this review.
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Affiliation(s)
- K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
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Dong R, Hu T, Zhang Y, Li Y, Zhou XH. Assessing the Transmissibility of the New SARS-CoV-2 Variants: From Delta to Omicron. Vaccines (Basel) 2022; 10:vaccines10040496. [PMID: 35455246 PMCID: PMC9026126 DOI: 10.3390/vaccines10040496] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/15/2022] [Accepted: 03/21/2022] [Indexed: 02/06/2023] Open
Abstract
Omicron, the latest SARS-CoV-2 Variant of Concern (VOC), first appeared in Africa in November 2021. At present, the question of whether a new VOC will out-compete the currently predominant variant is important for governments seeking to determine if current surveillance strategies and responses are appropriate and reasonable. Based on both virus genomes and daily-confirmed cases, we compare the additive differences in growth rates and reproductive numbers (R0) between VOCs and their predominant variants through a Bayesian framework and phylo-dynamics analysis. Faced with different variants, we evaluate the effects of current policies and vaccinations against VOCs and predominant variants. The model also predicts the date on which a VOC may become dominant based on simulation and real data in the early stage. The results suggest that the overall additive difference in growth rates of B.1.617.2 and predominant variants was 0.44 (95% confidence interval, 95% CI: −0.38, 1.25) in February 2021, and that the VOC had a relatively high R0. The additive difference in the growth rate of BA.1 in the United Kingdom was 6.82 times the difference between Delta and Alpha, and the model successfully predicted the dominating process of Alpha, Delta and Omicron. Current vaccination strategies remain similarly effective against Delta compared to the previous variants. Our model proposes a reliable Bayesian framework to predict the spread trends of VOCs based on early-stage data, and evaluates the effects of public health policies, which may help us better prepare for the upcoming Omicron variant, which is now spreading at an unprecedented speed.
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Affiliation(s)
- Rui Dong
- Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China;
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Taojun Hu
- Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (T.H.); (Y.Z.)
| | - Yunjun Zhang
- Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (T.H.); (Y.Z.)
| | - Yang Li
- Chongqing School, University of Chinese Academy of Sciences, Chongqing 400020, China;
| | - Xiao-Hua Zhou
- Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (T.H.); (Y.Z.)
- Beijing International Center for Mathematical Research, Peking University, Beijing 100191, China
- Correspondence:
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