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Wichaidit M, Nopsopon T, Sunan K, Phutrakool P, Ruchikachorn P, Wanvarie D, Pratanwanich PN, Cheewaruangroj N, Punyabukkana P, Pongpirul K. Breakthrough infections, hospital admissions, and mortality after major COVID-19 vaccination profiles: A prospective cohort study. Lancet Reg Health Southeast Asia 2023; 8:100106. [PMID: 36349259 PMCID: PMC9633626 DOI: 10.1016/j.lansea.2022.100106] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/07/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022]
Abstract
Background Several COVID-19 vaccination rollout strategies are implemented. Real-world data from the large-scale, government-mandated Central Vaccination Center (CVC), Thailand, could be used for comparing the breakthrough infection, across all available COVID-19 vaccination profiles. Methods This prospective cohort study combined the vaccine profiles from the CVC registry with three nationally validated outcome datasets to assess the breakthrough COVID-19 infection, hospitalization, and death among Thais individuals who received at least one dose of the COVID-19 vaccine. The outcomes were analyzed by comparing vaccine profiles to investigate the shot effect and homologous effect. Findings Of 2,407,315 Thais who had at least one dose of COVID-19 vaccine, 63,469 (2.75%) had breakthrough infection, 42,001 (1.79%) had been hospitalized, and 431 (0.02%) died. Per one vaccination shot added, there was an 18% risk reduction of breakthrough infection (adjusted hazard ratio [HR] 0.82, 95% confidence interval [CI] 0.80-0.82), a 25% risk reduction of hospitalization (HR 0.75, 95% CI 0.73-0.76), and a 96% risk reduction of mortality (HR 0.04, 95% CI 0.03-0.06). The heterologous two-shot vaccine profiles had a higher protective effect against infection, hospitalization, and mortality compared to the homologous counterparts. Interpretation COVID-19 breakthrough infection, hospitalization, and death differ across vaccination profiles that had a different number of shots and types of vaccines. Funding This study did not involve any funding.
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Key Words
- AZ, ChAdOx1 nCoV-19, Vaxzevria, Cambridge, AstraZeneca, UK
- CO-Ward, Thai COVID-19 hospitalization dataset
- CVC, central vaccination center
- Co-Lab, Thai COVID-19 infection dataset
- IN, inactivated vaccine
- IgG, immunoglobulin G
- MN, mRNA-1273, Moderna, NIAID, USA
- MR, mRNA vaccine
- PDPA, Personal Data Protection Act
- PEC, primary eligibiligy criteria
- PZ, BNT162b2, Comirnaty, BioNTech, Mainz, Germany
- RT-PCR, reverse transcription-polymerase chain reaction
- SP, Sinopharm, Beijing Institute of Biological Products, China
- SV, CoronaVac, Sinovac Biotech, Beijing, China
- VSDMC, Vaccine Safety and Data Monitoring Committee
- VV, viral vector vaccine
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Affiliation(s)
- Mingkwan Wichaidit
- Institute of Dermatology, Department of Medical Services, Ministry of Public Health, Bangkok, Thailand
| | - Tanawin Nopsopon
- School of Global Health and Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,Harvard T.H. Chan School of Public Health, Boston, MA, USA,Division of Allergy and Clinical Immunology, Brigham and Women’s and Harvard Medical School, Boston, MA, USA,Corresponding author
| | - Krittiyaporn Sunan
- Institute of Dermatology, Department of Medical Services, Ministry of Public Health, Bangkok, Thailand,School of Global Health and Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Phanupong Phutrakool
- School of Global Health and Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | | | - Dittaya Wanvarie
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Ploy Naruemon Pratanwanich
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Nontawit Cheewaruangroj
- Government Big Data Institute (GBDi), Digital Economy Promotion Agency, Ministry of Digital Economy and Society, Bangkok, Thailand
| | - Proadpran Punyabukkana
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Krit Pongpirul
- School of Global Health and Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA,Clinical Research Center, Bumrungrad International Hospital, Bangkok, Thailand,Corresponding author. 1873 Rama IV Rd., Patumwan, Bangkok 10330, Thailand
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Ruchikachorn P, Mueller K. Learning Visualizations by Analogy: Promoting Visual Literacy through Visualization Morphing. IEEE Trans Vis Comput Graph 2015; 21:1028-1044. [PMID: 26357285 DOI: 10.1109/tvcg.2015.2413786] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We propose the concept of teaching (and learning) unfamiliar visualizations by analogy, that is, demonstrating an unfamiliar visualization method by linking it to another more familiar one, where the in-betweens are designed to bridge the gap of these two visualizations and explain the difference in a gradual manner. As opposed to a textual description, our morphing explains an unfamiliar visualization through purely visual means. We demonstrate our idea by ways of four visualization pair examples: data table and parallel coordinates, scatterplot matrix and hyperbox, linear chart and spiral chart, and hierarchical pie chart and treemap. The analogy is commutative i.e. any member of the pair can be the unfamiliar visualization. A series of studies showed that this new paradigm can be an effective teaching tool. The participants could understand the unfamiliar visualization methods in all of the four pairs either fully or at least significantly better after they observed or interacted with the transitions from the familiar counterpart. The four examples suggest how helpful visualization pairings be identified and they will hopefully inspire other visualization morphings and associated transition strategies to be identified.
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Abstract
High-dimensional data visualization has been attracting much attention. To fully test related software and algorithms, researchers require a diverse pool of data with known and desired features. Test data do not always provide this, or only partially. Here we propose the paradigm WYDIWYGS (What You Draw Is What You Get). Its embodiment, SketchPadND, is a tool that allows users to generate high-dimensional data in the same interface they also use for visualization. This provides for an immersive and direct data generation activity, and furthermore it also enables users to interactively edit and clean existing high-dimensional data from possible artifacts. SketchPadND offers two visualization paradigms, one based on parallel coordinates and the other based on a relatively new framework using an N-D polygon to navigate in high-dimensional space. The first interface allows users to draw arbitrary profiles of probability density functions along each dimension axis and sketch shapes for data density and connections between adjacent dimensions. The second interface embraces the idea of sculpting. Users can carve data at arbitrary orientations and refine them wherever necessary. This guarantees that the data generated is truly high-dimensional. We demonstrate our tool's usefulness in real data visualization scenarios.
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Affiliation(s)
- Bing Wang
- Visual Analytics and Imaging Laboratory, Computer Science Department, Stony Brook University
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