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arXiv 2021年

作者： Sadiq, Ismail Perez-Alday, Erick A. Shah, Amit J. Rad, Ali Bahrami Sameni, Reza Clifford, Gari D. Department of Electrical & Computer Engineering Georgia Institute of Technology United States Department of Biomedical Informatics Emory University United States Department of Epidemiology Rollins School of Public Health Emory University United States Department of Biomedical Engineering Georgia Institute of Technology Emory University United States

MSC Codes 92C30, 92C32, 03H10, 62H30, 68Q07, 8T07, 78-10, 92-10, 62R07, 68T09, 68T10Objective: Transfer learning has been shown to be an effective strategy for pre-training a deep neural network (DNN) to boost performance on smaller data-sets. This is particularly useful when it is difficult or costly to acquire high quality data, such as in medicine. In this study we evaluated the additional utility of pre-training on a computationally efficient but realistic physiological model which can be tuned to explore a wide variety of states for a specific condition. In this current work, we focused on the existence of T wave alternans (TWAs) as a marker of post traumatic stress disorder (PTSD) in a small cohort of 36 veteran twins. Approach: Using a previously validated artificial ECG model, we generated 180,000 artificial ECGs with or without TWAs, with varying heart rate, breathing rate, TWA amplitude, and ECG morphology. We then took a previously developed state-of-the-art DNN, trained on over 70,000 patients to classify 25 different rhythms, modified the output layer to a binary class (TWA or no-TWA, or equivalently, PTSD or no-PTSD), and performed transfer learning on the artificial data. In a final transfer learning step, real the DNN was trained on ECGs from 12 individuals with PTSD and 24 controls, and evaluated using leave-one-subject-out cross-validation. The training and testing processes were repeated with and without each of the three data sets (rhythm data, artificial data, PTSD data) to evaluate the contribution of each transfer learning step. The area under receiver operating characteristic (AUROC) curve, accuracy (Acc), F1-score for PTSD and balanced accuracy (BAcc.) classification was reported for each trained model. Main results: The best performing approach (AUROC = 0.77, Acc = 0.72, F1-score = 0.64, BAcc. = 0.73) was found by performing both transfer learning steps, using the pre-trained arrhythmia DNN, the artificial data and the real PTSD-related ECG

关键词： Electrocardiography

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Patients with obstructive sleep apnea are at great risk of flavor disorders: a 15-year population-based cohort study

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Clinical Oral Investigations 2023年第1期27卷 183-192页

作者： Huang, Hsin-Hsin Ma, Kevin Sheng-Kai Wu, Min-You Hung, Yao-Min Tsao, Chien-Han Wei, James Cheng-Chung Wen, Wei-Sheng Wang, Yu-Hsun Hung, Shih-Yen Chao, Max Min Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering & Computer Science National Taiwan University Taipei Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine National Yang Ming University Taipei Taiwan Department of Otolaryngology Chung Shan Medical University Hospital Taichung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Division of Allergy Immunology and Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Chi-Mei Medical Center Tainan Taiwan

Objective: Obstructive sleep apnea (OSA) results from upper airway remodeling, which has been suggested to alter sensory and motor neuron function due to hypoxia or snore vibration. This study investigated whether OSA was associated with the risk of flavor disorder (FD). Materials and methods: Seven thousand and eight hundred sixty-five patients with OSA and 7865 propensity score-matched controls without OSA were enrolled between 1999 and 2013 through a nationwide cohort study. The propensity score matching was based on age, sex, comorbidities including hypertension, hyperlipidemia, chronic obstructive pulmonary disease (COPD), asthma, ankylosing spondylitis, and Charlson comorbidity index, and co-medications during the study period, including statins and angiotensin-converting enzyme (ACE) inhibitors. The adjusted hazard ratio (aHR) of incident FD following OSA was derived using a Cox proportional hazard model. A log-rank test was used to evaluate the time-dependent effect of OSA on FD. Age, sex, comorbidities, and co-medications were stratified to identify subgroups susceptible to OSA-associated FD. Results: Patients with OSA were at a significantly great risk of FD (aHR = 1.91, 95% CI = 1.08–3.38), which was time-dependent (log-rank test p = 0.013). Likewise, patients with hyperlipidemia were at a significant great risk of FD (aHR = 2.99, 95% CI = 1.33–6.69). Subgroup analysis revealed that female patients with OSA were at higher risks of FD (aHR = 2.39, 95%CI = 1.05–5.47). Conclusions: Patients with OSA were at significantly great risk of incident FD during the 15-year follow-up period, especially in female patients with OSA. Clinical relevance: Timely interventions for OSA may prevent OSA-associated FD. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Flavor disorder Obstructive sleep apnea Population-based cohort study Smell disorder Taste disorder

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Assessing gender fairness in EEG-based machine learning detection of Parkinson’s disease: A multi-center study

arXiv

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arXiv 2023年

作者： Kurbatskaya, Anna Jaramillo-Jimenez, Alberto Ochoa-Gomez, John Fredy Brønnick, Kolbjørn Fernandez-Quilez, Alvaro Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Stavanger University Hospital Stavanger Norway Department of Public Health Faculty of Health Sciences University of Stavanger Stavanger Norway Grupo de Neurociencias de Antioquia Universidad de Antioquia School of Medicine Medellín Colombia Grupo Neuropsicología y Conducta Universidad de Antioquia School of Medicine Medellín Colombia Department of Radiology Stavanger University Hospital Stavanger Norway

As the number of automatic tools based on machine learning (ML) and resting-state electroencephalography (rs-EEG) for Parkinson’s disease (PD) detection keeps growing, the assessment of possible exacerbation of health disparities by means of fairness and bias analysis becomes more relevant. Protected attributes, such as gender, play an important role in PD diagnosis development. However, analysis of sub-group populations stemming from different genders is seldom taken into consideration in ML models’ development or the performance assessment for PD detection. In this work, we perform a systematic analysis of the detection ability for gender sub-groups in a multi-center setting of a previously developed ML algorithm based on power spectral density (PSD) features of rs-EEG. We find significant differences in the PD detection ability for males and females at testing time (80.5% vs. 63.7% accuracy) and significantly higher activity for a set of parietal and frontal EEG channels and frequency sub-bands for PD and non-PD males that might explain the differences in the PD detection ability for the gender subgroups. © 2023, CC BY.

关键词： Electroencephalography

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Grid connections and inequitable access to electricity in African cities

Nature Cities

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Nature Cities 2025年 1-9页

作者： Jessica Kersey Daniel M. Kammen Civian Kiki Massa June Lukuyu Judith Mbabazi Jay Taneja Veronica Jacome Energy and Resources Group University of California Berkeley CA USA Goldman School of Public Policy University of California Berkeley CA USA Electrical and Computer Engineering University of Massachusetts Amherst MA USA Department of Electrical and Computer Engineering University of Washington Seattle WA USA Urban Action Lab Makerere University Kampala Uganda Manning College of Information and Computer Sciences University of Massachusetts Amherst MA USA Geography Environment and Urban Studies College of Liberal Arts Temple University Philadelphia PA USA

As the Global South rapidly and inequitably urbanizes, cities are at the forefront of efforts to address energy-related dimensions of poverty. In many African cities, electricity via a connection to a grid—the gold standard of electrification—has not substantially displaced smoky, polluting fuels. Among low-income communities, connections tend to be based on improvised, informal arrangements with local intermediaries. The nature and level of access these connections provide is not well understood and cannot be easily measured using existing energy access metrics such as the Multi-Tier Framework. This study provides a grounded, empirical analysis of grid connections across 25 informal settlements in the Ugandan capital city of Kampala. Using a mixed-methods approach that incorporates surveys, interviews and remote power quality monitoring, we trace electrical and financial flows between the utility, supply intermediaries and end users. We identify 29 unique configurations of these flows—which we term service arrangements—that provide electricity of varying and overall limited levels of affordability, reliability, voltage stability, precarity, autonomy and safety. Our evidence suggests that the grid delivers highly inequitable electricity services that fall short of aspirations of modern energy for the city’s most vulnerable communities.

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Prediction of Occupational Physical Activities using Inertial Measurement Units and Deep Learning Models

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2021年第1期64卷 879 - 881页

作者： Yishu Yan Hao Fan Yibin Li Elias Hoeglinger Alexander Wiesinger Alan Barr Carisa Harris Adamson Department of Mechanical Engineering University of California Berkeley Department of Industrial Design Northwestern Polytechnic ChinaDepartment of Medicine University of California San Francisco Department of Electrical Engineering and Computer Science University of California Berkeley University of Applied Sciences Upper Austria Linz AustriaSchool of Public Health University of California Berkeley Department of Medicine University of California San Francisco Department of Medicine University of California San FranciscoSchool of Public Health University of California Berkeley

Many workers engaged in manual material handling (MMH) jobs experience high physical demands that are associated with work-related musculoskeletal disorders (WMSD). Quantifying the physical demands of a job is an important legal requirement in the US that is used by human resources in the job hiring process. Most physical demands analysis (PDA) are performed using observational and semi-quantitative methods. The lack of accuracy and reliability of these methods can create problems when assigning acceptable tasks to an injured worker. In this study, various deep learning models were applied to data from eight inertial measurement units (IMUs) to predict 15 occupational physical activities (OPA). Overall, a 95% accuracy was reached by convolutional neural network (CNN) for predicting occupational physical activities when performed in isolation. More work is needed to estimate the accuracy of the model when OPA elements are combined into a more complex task.

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The 2022 report of synergetic roadmap on carbon neutrality and clean air for China:Accelerating transition in key sectors

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Environmental Science and Ecotechnology 2024年第3期19卷 4-18页

作者： Yu Lei Zhicong Yin Xi Lu Qiang Zhang Jicheng Gong Bofeng Cai Cilan Cai Qimin Chai Huopo Chen Renjie Chen Shi Chen Wenhui Chen Jing Cheng Xiyuan Chi Hancheng Dai Xiangzhao Feng Guannan Geng Jianlin Hu Shan Hu Cunrui Huang Tiantian Li Wei Li Xiaomei Li Jun Liu Xin Liu Zhu Liu Jinghui Ma Yue Qin Dan Tong Xuhui Wang Xuying Wang Rui Wu Qingyang Xiao Yang Xie Xiaolong Xu Tao Xue Haipeng Yu Da Zhang Ning Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Xin Zhang Zengkai Zhang Bo Zheng Yixuan Zheng Jian Zhou Tong Zhu Jinnan Wang Kebin He Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100041China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100041China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China National Center for Climate Change Strategy and International CooperationBeijing100035China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of HealthFudan UniversityShanghai200032China School of Economics and Management Beijing University of Chemical TechnologyBeijing100029China Department of Earth System Science University of CaliforniaIrvineCA92697USA National Meteorological Center China Meteorological AdministrationBeijing100081China College of Environmental Sciences and Engineering Peking UniversityBeijing100871China Policy Research Center for Environment and Economy Ministry of Ecology and Environment of the People's Republic of ChinaBeijing100029China Jiangsu Key Laboratory of Atmospheric Environment Monitoring

China is now confronting the intertwined challenges of air pollution and climate *** the high synergies between air pollution abatement and climate change mitigation,the Chinese government is actively promoting synergetic control of these two *** Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key *** Synergetic Roadmap 2022 report is the first annual update,featuring 20 indicators across five aspects:synergetic governance system and practices,progress in structural transition,air pollution and associated weather-climate interactions,sources,sinks,and mitigation pathway of atmospheric composition,and health impacts and benefits of coordinated *** to the comprehensive review presented in the 2021 report,the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant *** milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time,a decline in the production of crude steel and cement after years of growth,and the surging penetration of electric ***,in 2022,China issued the first national policy that synergizes abatements of pollution and carbon emissions,marking a new era for China's pollution-carbon *** changes highlight China's efforts to reshape its energy,economic,and transportation structures to meet the demand for synergetic control and sustainable ***,the country has witnessed a slowdown in carbon emission growth,improved air quality,and increased health benefits in recent years.

关键词： Synergetic roadmap Carbon neutrality Clean air

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The 2023 report of the synergetic roadmap on carbon neutrality and clean air for China: Carbon reduction, pollution mitigation, greening, and growth

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Environmental Science and Ecotechnology 2025年第1期23卷 1-18页

作者： Jicheng Gong Zhicong Yin Yu Lei Xi Lu Qiang Zhang Cilan Cai Qimin Chai Huopo Chen Renjie Chen Wenhui Chen Jing Cheng Xiyuan Chi Hancheng Dai Zhanfeng Dong Guannan Geng Jianlin Hu Shan Hu Cunrui Huang Tiantian Li Wei Li Xiaomei Li Yongsheng Lin Jun Liu Jinghui Ma Yue Qin Weiqi Tang Dan Tong Jiaxing Wang Lijuan Wang Qian Wang Xuhui Wang Xuying Wang Libo Wu Rui Wu Qingyang Xiao Yang Xie Xiaolong Xu Tao Xue Haipeng Yu Da Zhang Li Zhang Ning Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Zengkai Zhang Hongyan Zhao Bo Zheng Yixuan Zheng Tong Zhu Huijun Wang Jinnan Wang Kebin He State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100041China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100041China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China National Center for Climate Change Strategy and International CooperationBeijing100035China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of HealthFudan UniversityShanghai200032China School of Economics and Management Beijing University of Chemical TechnologyBeijing100029China Department of Earth System Science University of CaliforniaIrvineIrvineCA92697USA National Meteorological Center China Meteorological AdministrationBeijing100081China College of Environmental Sciences and Engineering Peking UniversityBeijing100871China Institute of Eco-Environmental Management and Policy Chinese Academy of Environmental PlanningBeijing100041China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Contr

The response to climate change and air pollution control demonstrates strong synergy across scientific mechanisms,targets,strategies,and governance *** report,based on a monitoring indicator system for coordinated governance of air pollution and climate change,employs an interdisciplinary approach combining natural and social *** establishes 20 indicators across five key areas:air pollution and climate change,governance systems and practices,structural transformation and technologies,atmospheric components and emission reduction pathways,and health impacts and *** report tries to provide actionable insights into the interconnectedness of air pollution and climate *** highlights key policy gaps,presents updated indicators,and offers a refined monitoring framework to track progress toward China's dual goals of reducing emissions and improving air *** to previous editions,this year's report has updated four key indicators:meteorological impacts on air quality,climate change and its effects,governance policies,and low-carbon building energy *** aim is to further refine the monitoring framework,track progress,and establish a comprehensive theory for collaborative governance while identifying challenges and proposing solutions for China's pathway to carbon neutrality and clean *** report comprises six *** executive summary chapter is followed by analyzing air pollution and climate change *** systems and practices are discussed in the third chapter,focusing on policy implementation and local *** fourth chapter addresses structural transformations and emission reduction technologies,including energy and industrial shifts,transportation,low-carbon buildings,carbon capture and storage,and power *** fifth chapter outlines atmospheric component dynamics and emission pathways,presenting insights into emission drivers and future *** sixth chapter assesses he

关键词： Synergetic roadmap Carbon neutrality Clean air

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Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective

arXiv

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arXiv 2024年

作者： Restrepo, David Wu, Chenwei Vásquez-Venegas, Constanza Matos, João Gallifant, Jack Celi, Leo Anthony Bitterman, Danielle S. Nakayama, Luis Filipe Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telematica Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Scientific Image Analysis Lab Faculty of Medicine Universidad de Chile RM Santiago Chile Department of Computer Science Faculty of Engineering Universidad de Concepción Biobio Concepción Chile Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation. © 2024, CC BY.

关键词： Decision making

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Relationship among state reopening policies, health outcomes and economic recovery through first wave of the COVID-19 pandemic in the U.S

arXiv

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arXiv 2021年

作者： Ligo, Alexandre K. Mahoney, Emerson Cegan, Jeffrey Trump, Benjamin D. Jin, Andrew S. Kitsak, Maksim Keenan, Jesse Linkov, Igor US Army Corps of Engineers Engineer Research and Development Center ConcordMA United States University of Virginia Department of Engineering Systems and Environment CharlottesvilleVA United States Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Carnegie Mellon University Department of Engineering and Public Policy PittsburghPA United States Tulane University School of Architecture New OrleansLA United States

State governments in the U.S. have been facing difficult decisions involving tradeoffs between economic and health-related outcomes during the COVID-19 pandemic. Despite evidence of the effectiveness of government-mandated restrictions mitigating the spread of contagion, these orders are stigmatized due to undesirable economic consequences. This tradeoff resulted in state governments employing mandates at widely different ways. We compare the different policies states implemented during periods of restriction ("lockdown") and reopening with indicators of COVID-19 spread and consumer card spending at each state during the first "wave" of the pandemic in the U.S. between March and August 2020. We find that while some states enacted reopening decisions when the incidence rate of COVID-19 was minimal or sustained in its relative decline, other states relaxed socioeconomic restrictions near their highest incidence and prevalence rates experienced so far. Nevertheless, all states experienced similar trends in consumer card spending recovery, which was strongly correlated with reopening policies following the lockdowns and relatively independent from COVID-19 incidence rates at the time. Our findings suggest that consumer card spending patterns can be attributed to government mandates rather than COVID-19 incidence in the states. We estimate the recovery in states that reopened in late April was more than the recovery in states that did not reopen in the same period – 15% for consumer card spending and 18% for spending by high income households. This result highlights the important role of state policies in minimizing health impacts while promoting economic recovery and helps planning effective interventions in subsequent waves and immunization efforts. © 2021, CC BY.

关键词： SARS

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