Author
Tingting Ye
Other affiliations: Binzhou University, Zhejiang University
Bio: Tingting Ye is an academic researcher from Monash University. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 7, co-authored 22 publications receiving 290 citations. Previous affiliations of Tingting Ye include Binzhou University & Zhejiang University.
Topics: Medicine, Population, Demography, Confidence interval, Cancer
Papers
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Monash University1, Shandong University2, University of London3, Academy of Sciences of the Czech Republic4, Czech University of Life Sciences Prague5, Hakim Sabzevari University6, University of Bern7, Harvard University8, National and Kapodistrian University of Athens9, Brunel University London10, Nagasaki University11, Instituto Nacional de Saúde Dr. Ricardo Jorge12, Universidade Nova de Lisboa13, Umeå University14, National Institutes of Health15, University of Valencia16, Ho Chi Minh City Medicine and Pharmacy University17, University of Santiago de Compostela18, University of Tartu19, Health Canada20, University of Ottawa21, University of Turin22, Norwegian Institute of Public Health23, University of Florence24, Cayetano Heredia University25, University of California, San Diego26, Fudan University27, Seoul National University28, Babeș-Bolyai University29, University of Porto30, University of Oulu31, Finnish Meteorological Institute32, King's College London33, Swiss Tropical and Public Health Institute34, University of Basel35, University of Tokyo36, University of São Paulo37, University of Los Andes38, Emory University39, University of Buenos Aires40, University of the Republic41, Potsdam Institute for Climate Impact Research42, Pablo de Olavide University43, Yale University44, University of Tsukuba45, National Taiwan University46
TL;DR: In this paper, the global, regional, and national mortality burden associated with non-optimal ambient temperatures was evaluated using time-series data collected from 750 locations in 43 countries and five meta-predictors.
189 citations
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TL;DR: A typical type of geospatial big data, points-of-interest (POIs), was combined with multi-source remote sensing data in a random forests model to disaggregate the 2010 county-level census population data to 100 × 100 m grids and showed higher accuracy.
145 citations
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TL;DR: The notion that smaller size fractions of PM have a more toxic mortality impacts is supported, which suggests to develop strategies to prevent and control PM1 in China, such as to foster strict regulations for automobile and industrial emissions.
94 citations
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TL;DR: Rural residents are more sensitive to both cold and hot temperatures than urban residents in Zhejiang Province, China, particularly the elderly, suggesting past studies using exposure–response functions derived from urban areas may underestimate the mortality burden for the population as a whole.
Abstract: Background: Temperature-related mortality risks have mostly been studied in urban areas, with limited evidence for urban–rural differences in the temperature impacts on health outcomes. Objectives:...
79 citations
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Sun Yat-sen University1, Monash University2, Nanjing University of Information Science and Technology3, University of London4, Yale University5, Harvard University6, University of Oulu7, Health Canada8, University of Ottawa9, University of São Paulo10, Fudan University11, University of Santiago de Compostela12, Academy of Sciences of the Czech Republic13, Czech University of Life Sciences Prague14, Hakim Sabzevari University15, University of Bern16, Brunel University London17, Spanish National Research Council18, Nagasaki University19, Instituto Nacional de Saúde Dr. Ricardo Jorge20, Umeå University21, National Institutes of Health22, University of Valencia23, Ho Chi Minh City Medicine and Pharmacy University24, National and Kapodistrian University of Athens25, University of Florence26, Cayetano Heredia University27, Chinese Academy of Sciences28, University of Tartu29, Seoul National University30, University of Porto31, Swiss Tropical and Public Health Institute32, University of Tokyo33, University of Los Andes34, Emory University35, University of Pretoria36, Council for Scientific and Industrial Research37, North-West University38, University of Buenos Aires39, Norwegian Institute of Public Health40, University of Turin41, University of the Republic42, Potsdam Institute for Climate Impact Research43, Pablo de Olavide University44, University of Tsukuba45, National Taiwan University46, Colorado School of Public Health47
TL;DR: In this article, the association between wildfire-related PM2·5 exposure and mortality was examined using a quasi-Poisson time series model in each city considering both the current-day and lag effects, and the effect estimates were then pooled using a random-effects meta-analysis.
60 citations
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
13,246 citations
01 Jan 2020
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
4,408 citations
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TL;DR: In this paper, the authors investigated the association between hospital admission for cardiovascular disease (CVD) and respiratory disease and the chemical components of PM2.5 in the United States.
Abstract: Background Population-based studies have estimated health risks of short-term exposure to fine particles using mass of PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) as the indicator. Evidence regarding the toxicity of the chemical components of the PM2.5 mixture is limited. Objective In this study we investigated the association between hospital admission for cardiovascular disease (CVD) and respiratory disease and the chemical components of PM2.5 in the United States. Methods We used a national database comprising daily data for 2000–2006 on emergency hospital admissions for cardiovascular and respiratory outcomes, ambient levels of major PM2.5 chemical components [sulfate, nitrate, silicon, elemental carbon (EC), organic carbon matter (OCM), and sodium and ammonium ions], and weather. Using Bayesian hierarchical statistical models, we estimated the associations between daily levels of PM2.5 components and risk of hospital admissions in 119 U.S. urban communities for 12 million Medicare enrollees (≥ 65 years of age). Results In multiple-pollutant models that adjust for the levels of other pollutants, an interquartile range (IQR) increase in EC was associated with a 0.80% [95% posterior interval (PI), 0.34–1.27%] increase in risk of same-day cardiovascular admissions, and an IQR increase in OCM was associated with a 1.01% (95% PI, 0.04–1.98%) increase in risk of respiratory admissions on the same day. Other components were not associated with cardiovascular or respiratory hospital admissions in multiple-pollutant models. Conclusions Ambient levels of EC and OCM, which are generated primarily from vehicle emissions, diesel, and wood burning, were associated with the largest risks of emergency hospitalization across the major chemical constituents of PM2.5.
394 citations
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TL;DR: Wang et al. as discussed by the authors used multi-source datasets, including Luojia1-01 nighttime light imagery, Landsat-8, Sentinel-2 and building vector data, to analyze the thermal characteristics of different local climate zones (LCZs).
145 citations