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1st Workshop on Environmental Science, Society, and Technology, WESTECH 2018

作者： Abdullah, Dahlan Hartono, H. Saddhono, Kundharu Kurniasih, Nuning Iskandar, Akbar Rianita, Dian Purwarno Setyawasih, Rianti Satria, Erwinsyah Nuryanto, Tato Herawati, Lilik Sujinah Sudarsana, I Ketut Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Public Administrations Universitas Lancang Kuning Indonesia Faculty of Literature Universitas Islam Sumatera Utara Medan Indonesia Indonesia Department of Management Faculty of Economic Universitas Islam 45 Bekasi Indonesia Faculty of Teacher Training and Education Universitas Bung Hatta Padang Indonesia FITK IAIN Syekh Nurjati Cirebon Indonesia Department of Indonesian Language Teaching Institut Agama Islam Negeri Syekh Nurjati Cirebon Cirebon Indonesia Faculty of Education University Muhammadiyah of Surabaya Surabaya Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Indonesia

Decision making is sometimes faced with a situation where assessment is given in the form of linguistic variables, which have a certain range of values so that decision making cannot be given in the form of numerical values. One approach commonly used is Hesitant Fuzzy Linguistic Term Sets (HFLTS) which is a multi-criteria and multi-alternative decision making method. In TOPSIS which has the concept of distance measurement, basically the chosen one is the best alternative is an alternative that has the closest distance to a positive solution and the farthest distance with a negative solution. The incorporation of HFLTS-TOPSIS has the advantage that decision making can be done in the form of variable linguistics and also a situation where there are benefit criteria and cost criteria. This study will use the HFLTS-TOPSIS approach that has been modified in the distance calculation section so that distance calculation is based on the difference of preference degree between the two HFLTS. The results of research conducted by a number of researchers show that using pseudo-distance will provide advantages compared to the standard HLFTS-TOPSIS. Pseudo-distance HFLTS-TOPSIS will be applied in the selection of the best lecturers. The criteria used in this study are: Student Satisfaction, Research, Number of late, and number of complaints from students. © Published under licence by IOP Publishing Ltd.

关键词： Decision making

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Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

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NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

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Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2019年第4期46卷 670页

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of Data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

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Real-time optimization using gradient adaptive selection and classification from infrared sensors measurement for esterification oleic acid with glycerol

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International Journal of Intelligent Computing and Cybernetics 2017年第2期10卷 130-144页

作者： Iwan Aang Soenandi Taufik Djatna Ani Suryani Irzaman Post Graduate Program of Agro-industrial Technology Bogor Agricultural UniversityBogorIndonesia and Faculty of Engineering and Computer ScienceKrida Wacana Christian UniversityJakartaIndonesia Post Graduate Program of Agro-industrial Technology Bogor Agricultural UniversityBogorIndonesia Department of Physics Faculty of Mathematics and Natural SciencesBogor Agricultural UniversityBogorIndonesia

Purpose-The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process *** accurate monitoring and controlling of the process can improve production yield and *** purpose of this paper is to propose a real-time optimization(RTO)using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the ***/methodology/approach-The integration of the esterification process optimization using self-optimization(SO)was developed with classification process was combined with necessary condition optimum(NCO)as gradient adaptive selection,supported with laboratory scaled medium wavelength infrared(mid-IR)sensors,and measured the proposed optimization system indicator in the batch *** Process Modeling and Notation(BPMN 2.0)was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual ***,Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine(SVM)classification and Arduino microcontroller for ***-This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent,lower error measurement with percentage error 1.11 percent,reduced the process duration up to 22 minutes,with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210℃ which was more efficient,as it consumed less *** limitations/implications-In this research the authors just have an experiment for the esterification process using glycerol,but as a development concept of RTO,it would be possible to apply for another chemical reaction or *** implications-This research introduces new development of an RTO approach to optimal con

关键词： Gradient technique Infrared sensor Real-time optimization Simulation and modelling Support vector machine

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Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles

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CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY 2019年 18卷 16-22页

作者： Christopherson, Kaitlin M. Ghosh, Alokananda Sherif, Abdallah Mohamed, Radwan Kamal, Mona Fuller, David Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Department of Biostatistics and Data Science University of Texas School of Public Health Houston TX United States Department of Clinical Oncology University of Alexandria Alexandria Egypt Department of Emergency Medicine The University of Texas MD Anderson Cancer Center Houston TX United States Baylor College of Medicine Houston TX United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA United States Department of Radiation Oncology The University of Texas Medical Branch Galveston TX United States Department of Head and Neck Surgery The University of Texas MD Anderson Cancer Center Houston TX United States Department of Thoracic & Head and Neck Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Medical Physics Program The University of Texas Graduate School of Biomedical Sciences Houston TX United States Department of Computer Science University of Illinois at Chicago Chicago IL United States Department of Electrical and Computer Engineering The University of Iowa Iowa City IA United States

Objectives: We sought to model chronic radiation-associated dysphagia (RAD) in patients given intensity-modulated radiation therapy (IMRT) for oropharyngeal squamous cell cancer (OPSCC) as a function of age and dose to non-target swallowing muscles. Methods: We reviewed 300 patients with T1-T4 N0-3 M0 OPSCC given definitive IMRT with concurrent chemotherapy. Chronic RAD was defined as aspiration or stricture on videoflouroscopy/endoscopy, gastrostomy tube, or aspiration pneumonia at >= 12 months after IMRT. Doses to autosegmented regions of interest (ROIs;inferior, middle and superior constrictors, anterior and posterior digastrics, mylo/geniohyoid complex, intrinsic tongue, and gengioglossus) were obtained from DICOM-RT plans and dose-volume histograms. The probability of chronic RAD as a function of mean ROI dose, stratified by age (<50, 50-59, 60-69, or >70 years), was estimated with logistic probability models and subsequent unsupervised nonlinear curves. Results: Chronic RAD was observed in 34 patients (11%). Age was a significant correlate of chronic RAD, both independently and with dose for all muscle groups examined. Distinct muscle-specific dose-response profiles were observed as a function of age (e.g., 5% of patients in their 50 s [but 20% of those 70 +] who received 60 Gy to the superior constrictor had chronic RAD). This effect was stable across all observed muscle ROIs, with a false discovery rate-corrected p < 0.05, for all dose/muscle/age models, suggesting that including age as a covariate improves modeling of chronic RAD. Conclusions: Age at treatment moderates the probability of chronic RAD after chemo-IMRT for OPSCC, with aging muscles showing lower dose thresholds. Uniform dose constraints may not predict toxicity in older patients. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.

关键词： Oropharynx IMRT Radiation Toxicity Presbyphagia

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Design approach for additive manufacturing in spare part supply chains

arXiv

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arXiv 2020年

作者： de Brito, Filipe Marinho da Cruz Júnior, Gélson Frazzon, Enzo Morosini Basto, João Pedro Tavares Vieira Alcalá, Symone Gomes Soares School of Electrical Mechanical and Computer Engineering Federal University of Goiás GoiâniaGoiás74605-010 Brazil INESC P&D Brasil São Paulo11055-300 Brazil Graduate Program in Production Engineering Department of Production Engineering and Systems Federal University of Santa Catarina Florianópolis88040-900 Brazil Department of Industrial Engineering and Management Faculty of Engineering University of Porto Porto4200-465 Portugal INESC TEC - Technology Faculty of Engineering University of Porto Porto4200-465 Portugal Production Engineering Faculty of Sciences and Technology Federal University of Goiás Aparecida de Goiânia Goiás74968-755 Brazil

In the current industrial revolution, additive manufacturing (AM) embodies a promising technology that can enhance the effectiveness, adaptability, and competitiveness of supply chains (SCs). Moreover, it facilitates the development of distributed SCs, thereby enhancing product availability, inventory levels, and lead time. However, the wide adoption of AM in industrial SCs creates various challenges, leading to new difficulties for SC design. In this context, this paper proposes a new design approach to AM SCs using optimization methods. More specifically, the proposed approach, comprising the pmedian and mixed-integer linear programming models, considers the decision of deploying productive resources (3D printers) in specific locations of generic spare part SCs. The approach was evaluated in a real-world use case of an elevator maintenance service provider. The obtained results demonstrated the promising capabilities of the proposed design approach in managing the challenges arising from the forthcoming widespread use of 3D printers in manufacturing SCs. © 2020, CC-BY-NC-ND.

关键词： 3D printers

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A Machine Learning-Based Approach for Classification of Alzheimer’s Disease and its Risk Prediction

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Alzheimer's & Dementia 2022年第S11期18卷

作者： Payam Hosseinzadeh Kasani Jung-Kyeom Kim Peyman Hosseinzadeh Kassani Yeshin Kim Jeong-Ah Kim Chihyun Park Cheol-Heui Yun Seong Hye Choi Sang-Ah Lee Seo-Young Lee Jae-Won Jang Department of Neurology Kangwon National University Hospital Chuncheon Korea Republic of (South) Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Chuncheon Korea Republic of (South) Department of Neurology and Neurological Sciences Stanford University Stanford CA USA College of Medicine Kangwon National University Chuncheon Korea Republic of (South) Department of Ophthalmology Kangwon National University Hospital Chuncheon Korea Republic of (South) Department of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of (South) Department of Agricultural Biotechnology Seoul National University Seoul Korea Republic of (South) Research Institute of Agriculture and Life Sciences Seoul National University Seoul Korea Republic of (South) Department of Neurology Inha University School of Medicine Incheon Korea Republic of (South) Department of Preventive Medicine Kangwon National University School of Medicine Chuncheon Korea Republic of (South)

Background Dementia, a clinical syndrome with progressive neurodegenerative disease, is one of the inevitable aging consequences. Alzheimer’s disease (AD) is the most common cause of dementia. Accurate detection and classification of Alzheimer’s disease alongside its conversion from Mild Cognitive Impairment (MCI) to AD at an early stage is of significant clinical importance. However, a reliable diagnosis with a multi-label classification approach remains a challenging task. This study aimed to measure the effectiveness of machine learning algorithm integrated with feature importance techniques to constitutes a multi-label classification of AD prediction for clinical practice. Method The machine learning algorithms are validated using a nationwide cohort dataset, the Korean Brain Aging Study for the early diagnosis and prediction of AD (KBASE) to identify the most important risk factors, where a database is classified into three classes namely, normal cognition (NC, n = 91), mild cognitive impairment (MCI, n = 61), and Alzheimer’s disease (AD, n = 49). Applying eight state-of-the-art supervised machine learning algorithms namely Support Vector Machine, Naive Bayes, XGBoost, Decision Tree, Logistic Regression, Random Forest, Bagging, and AdaBoost. A stratified 10-fold cross-validation was used for every train/test split and several performance measures, including accuracy, precision, recall and f1-score were used to compare the classifier performances. Result Based on the performance evaluation, the XGBoost classifier significantly outperformed all other models and achieved 82.55% accuracy followed by the Bagging classifier (81%). According to the XGBoost model, calculating risk factors to predict performance, the Consortium to Establish a Registry for AD (CERAD) subsets is among the highest ranked feature. It was intriguing that the parameters including job, job levels and retirement achieved a minimal, if any, impact in the final performance. Conclusion Collectivel

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Erratum: “Two-dimensional porous graphitic carbon nitride C6N7 monolayer: First-principles calculations” [Appl. Phys. Lett. 119, 142102 (2021)]

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Applied Physics Letters 2022年第18期120卷

作者： A. Bafekry M. Faraji M. M. Fadlallah I. Abdolhosseini Sarsari H. R. Jappor S. Fazeli M. Ghergherehchi 1Department of Radiation Application Shahid Beheshti University Tehran Iran 2Micro and Nanotechnology Graduate Program TOBB University of Economics and Technology Sogutozu Caddesi No 43 Sogutozu 06560 Ankara Turkey 3Department of Physics Faculty of Science Benha University 13518 Benha Egypt 4Department of Physics Isfahan University of Technology Isfahan 84156-83111 Iran 5Department of Physics College of Education for Pure Sciences University of Babylon Hilla Iraq 6Department of Material Science and Engineering Sharif University of Technology PO Box 11155-9466 Tehran Iran 7Department of Electrical and Computer Engineering Sungkyunkwan University 16419 Suwon South Korea

The original article1 contains misprints in the values of the elastic parameters. The values of C11 = 258.6 GPa, C22 = 290.8 GPa, and C12 = 70.73 GPa and C13 =

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191 Dose prescription variability in Oropharynx Cancer Radiotherapy

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Radiotherapy and Oncology 2024年 192卷 S225-S228页

作者： Christian R Hansen Tony Tadic Andrea McNiven Jens Petersen Sharma Manju Gareth Price Mohamed A Naser Pernille Lassen Jens Overgaard Lachlan McDowell Clifton David Fuller David Thomsen Sue S Yom J⊘rgen Johansen Jeppe Friborg Andrew Hope Laboratory of Radiation Physics Odense University Hospital Odense Denmark. Institute of Clinical Research University of Southern Denmark Odense Denmark. Danish Center of Particle Therapy Aarhus University Hospital Aarhus Denmark. Department of Medical Physics Princess Margaret Cancer Centre Toronto Canada. Department of Oncology Copenhagen University Hospital Rigshospitalet Copenhagen Denmark. Department of Computer Science University of Copenhagen Copenhagen Denmark. Department of Radiation Oncology University of California San Francisco San Francisco USA. Division of Cancer Sciences University of Manchester The Christie NHS Foundation Trust Manchester United Kingdom. Department of Radiation Oncology Baylor College of Medicine Houston USA. Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston USA. Medical Physics Program The University of Texas Graduate School of Biomedical Sciences Houston USA. Department of Experimental Clinical Oncology Aarhus University Hospital Aarhus Denmark. Department of Oncology Aarhus University Hospital Aarhus Denmark. Department of Radiation Oncology Princess Alexandra Hospital Brisbane Australia. Department of Oncology Odense University Hospital Odense Denmark. Department of Otolaryngology-Head Neck Surgery Princess Margaret Cancer Centre Toronto Canada

Purpose/Objective Radiotherapy treatment planning hinges on a critical factor: the prescribed dose. Surprisingly, no consistent, standardised global approach to interpreting this prescription exists. This study aimed to examine and illustrate the variations in prescribed doses for the same treatment across North European and North American centres. Material/Methods The study analysed consecutively treated oropharynx cancer patients from six globally recognised radiotherapy departments. The criteria for inclusion encompassed curative IMRT or VMAT radiotherapy administered in 2017 or later. These centres were divided into three North American and three North European centres. Dose-volume histogram (DVH) data were extracted from the local treatment planning system for the Gross Tumour Volume (GTV), the high-dose Clinical Target Volume (CTV), and Planning Target Volume (PTV) for each patient. The DVH was sampled in 1 cGy dose bins across the 0 to 100 Gy range. All DVHs were scaled to a standard prescription of 70 Gy delivered in 35 fractions to facilitate straightforward comparisons across centres. No biological corrections were applied. For the three target volumes (GTV, CTV, PTV), we extracted and compared metrics such as D95% (Dose to 95% of the volume), D98%, D99%, V95% (Volume receiving 95% of the prescription dose), V105%, and V107%. We visually compared these metrics and conducted statistical testing using the Mann-Whitney U-test. Results Our study encompassed 1,375 patients treated across six centres, revealing a spectrum of 38 different dose prescriptions, ranging from 55 Gy in 20 to 70 Gy in 35 fractions. When normalised to 70 Gy, the median mean CTV dose exhibited a 4% difference, ranging from 70.12 Gy to 72.93 Gy across centres. Notably, the three European centres showed a high consistency, deviating by only 0.4%, while the three North American centres showed a slight variation within 2%. Figure 1 presents the mean CTV dose and the D98% boxplots. The interqu

关键词： HPV or EBV related cancers Dose prescription Treatment planning Trials

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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