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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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Handwritten text recognition results on the Bentham collection with improved classical N-Gram-HMM methods 15

Handwritten text recognition results on the Bentham collecti...

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3rd International Workshop on Historical Document Imaging and Processing, HIP 2015

作者： Toselli, Alejandro H. Vidal, Enrique Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Spain

ISBN: (纸本)9781450336024

Handwritten Text recognition experiments and results are presented on the historical Bentham text image dataset used in the ICFHR-2014 HTRtS competition. The successful segmentation-free holistic framework is adopted, using traditional modelling approaches based on Hidden Markov optical character models (HMM) and an N-gram language model (LM). Departing from the very basic N-gram-HMM baseline system provided in HTRtS, several improvements are made in text image feature extraction and LM and HMM modeling, including more accurate HMM training by means of discriminative training. As a result, we achieve similar recognition accuracy as some of the best performing (single, uncombined) systems based on (recurrent) Neural Networks (NN), using identical training and testing data. The traditional N-gram/HMM framework offers several advantages over modern approaches based on (hybrid, recurrent) NNs. Perhaps the most important is the much faster training of HMMs and the well understood stability of the results of Baum-Welch training. These advantages become very important when dealing with many historical document collections, which are typically huge and entail very high degrees of variability, making it generally diffcult to re-use models trained on previous collections. © 2015 ACM.

关键词： Recurrent neural networks

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ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 2016)

ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 20...

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International Workshop on Frontiers in Handwriting recognition

作者： Ioannis Pratikakis Konstantinos Zagoris Basilis Gatos Joan Puigcerver Alejandro H. Toselli Enrique Vidal Visual Computing Group Democritus University of Thrace Xanthi Greece Institute of Informatics and Telecommunications National Center for Scientific Research “Demokritos” Athens Greece Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

ISBN: (纸本)9781509009824

The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up an evaluation framework for benchmarking handwritten keyword spotting (KWS) examining both the Query by Example (QbE) and the Query by String (QbS) approaches. Both KWS approaches were hosted into two different tracks, which in turn were split into two distinct challenges, namely, a segmentation-based and a segmentation-free to accommodate different perspectives adopted by researchers in the KWS field. In addition, the competition aims to evaluate the submitted training-based methods under different amounts of training data. Four participants submitted at least one solution to one of the challenges, according to the capabilities and/or restrictions of their systems. The data used in the competition consisted of historical German and English documents with their own characteristics and complexities. This paper presents the details of the competition, including the data, evaluation metrics and results of the best run of each participating methods.

关键词： Image segmentation Training data Training Measurement Histograms Handwriting recognition Visualization

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Data Mapping by Restricted Boltzmann Machines for Social Circles Detection

Data Mapping by Restricted Boltzmann Machines for Social Cir...

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International Joint Conference on Neural Networks

作者： Jesús Alonso Roberto Paredes Paolo Rosso Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València

ISBN: (纸本)9781479919611

Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper, we propose a two-step technique, making emphasis on the mapping of the data by Restricted Boltzmann Machines (RBMs). Social circles are subsequently inferred by k-means over the preprocessed data. We define different vectorial representations from both structural egonet information and user profile features, and perform a set of tests to adjust the optimal parameters of the RBMs. We study and compare the performance on the ego-Facebook dataset of social circles from Facebook from the Stanford Large Network Dataset Collection. We compare our results with several different baselines.

关键词： Data Mapping Restricted Boltzmann Machines Social Circles Detection Boltzmann machines Circles Social Media MAPPING(MATHEMATICAL) Interest Research Facebook optimal parameter Social Networks

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Combining handwriting and speech recognition for transcribing historical handwritten documents

Combining handwriting and speech recognition for transcribin...

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International Conference on Document Analysis and recognition

作者： Emilio Granell Carlos-D. Martínez-Hinarejos Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

Transcription of historical documents is an interesting task for libraries in order to make available their funds. In the lasts years, the use of Handwritten Text recognition allowed paleographs to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is obtaining the draft transcription by dictating the contents to an Automatic Speech recognition system. When both sources (image and speech) are available, a multimodal combination is possible, and an iterative process can be used in order to refine the final hypothesis. In this work, a multimodal combination based on confusion networks is presented. Results on two different sets of data, with different difficulty level, show that the proposed technique provides similar or better draft transcriptions than a previously proposed approach, allowing for a faster transcription process.

关键词： Iterative decoding Acoustics Proposals Laplace equations Integrated optics Optical imaging

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Online optimisation of log-linear weights in interactive machine translation 9

Online optimisation of log-linear weights in interactive mac...

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9th International Conference on language Resources and Evaluation, LREC 2014

作者： Rios, Mara Chinea Sanchis-Trilles, Germán Ortiz-Martínez, Daniel Casacuberta, Francisco Pattern Recognition and Human Language Technologies Group Universitat Politècnica de València Camino de Vera s/n Valencia Spain

ISBN: (纸本)9782951740884

Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In such a setup, it is crucial that the system is able to learn from the errors that have already been corrected. In this paper, we analyse the applicability of discriminative ridge regression for learning the log-linear weights of a state-of-the - art machine translation system underlying an interactive machine translation framework, with encouraging results.

关键词： Machine translation

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Offline Features for Classifying Handwritten Math Symbols with Recurrent Neural Networks

Offline Features for Classifying Handwritten Math Symbols wi...

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International Conference on pattern recognition

作者： Francisco Álvaro Joan-Andreu Sánchez José-Miguel Benedí Pattern Recognition and Human Language Technologies Universitat Politècnica de València Spain

In mathematical expression recognition, symbol classification is a crucial step. Numerous approaches for recognizing handwritten math symbols have been published, but most of them are either an online approach or a hybrid approach. There is an absence of a study focused on offline features for handwritten math symbol recognition. Furthermore, many papers provide results difficult to compare. In this paper we assess the performance of several well-known offline features for this task. We also test a novel set of features based on polar histograms and the vertical repositioning method for feature extraction. Finally, we report and analyze the results of several experiments using recurrent neural networks on a large public database of online handwritten math expressions. The combination of online and offline features significantly improved the recognition rate.

关键词： Feature extraction Text recognition Vectors Handwriting recognition Hidden Markov models Databases Training

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ICFHR2014 Competition on Handwritten Text recognition on Transcriptorium Datasets (HTRtS)

ICFHR2014 Competition on Handwritten Text Recognition on Tra...

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International Workshop on Frontiers in Handwriting recognition

作者： Joan Andreu Sánchez Verónica Romero Alejandro H. Toselli Enrique Vidal Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

A contest on Handwritten Text recognition organised in the context of the ICFHR 2014 conference is described. Two tracks with increased freedom on the use of training data were proposed and three research groups participated in these two tracks. The handwritten images for this contest were drawn from an English data set which is currently being considered in the Tran scriptorium project. The goal of this project is to develop innovative, efficient and cost-effective solutions for the transcription of historical handwritten document images, focusing on four languages: English, Spanish, German and Dutch. For the English language, the so-called "Bentham collection" is being considered in Tran scriptorium. It encompasses a large set of manuscripts written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). A small subset of this collection has been chosen for the present HTR competition. The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant variabilities and difficulties regarding the quality of text images and writing styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts. The three participants achieved very good results, with transcription word error rates ranging from 15.0% down to 8.6%.

关键词： Training Hidden Markov models Histograms Artificial neural networks Text recognition Training data Adaptive optics

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Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic

Morpheme-based feature-rich language models using Deep Neura...

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2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013

作者： El-Desoky Mousa, Amr Kuo, Hong-Kwang Jeff Mangu, Lidia Soltau, Hagen Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany IBM T. J. Watson Research Center Yorktown Heights NY 10598 United States

ISBN: (纸本)9781479903566

Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs. © 2013 IEEE.

关键词： Deep neural networks

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MORPHEME-BASED FEATURE-RICH language MODELS USING DEEP NEURAL NETWORKS FOR LVCSR OF EGYPTIAN ARABIC

MORPHEME-BASED FEATURE-RICH LANGUAGE MODELS USING DEEP NEURA...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Amr El-Desoky Mousa Hong-Kwang Jeff Kuo Lidia Mangu Hagen Soltau Human Language Technology and Pattern Recognition - Computer Science Department RWTH Aachen University IBM T. J. Watson Research Center

ISBN: (纸本)9781479903573

关键词： language model Morpheme Feature-rich Deep neural network Egyptian Arabic modelling languages Neural network Arabic language additional features Word Religious Missions Continuous speech recognition

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