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1.
Int J Equity Health ; 20(1): 3, 2021 01 04.
Artículo en Inglés | MEDLINE | ID: mdl-33397390

RESUMEN

Despite being the wealthiest and one of the most technologically advanced countries in the world, the United States has the greatest number of Covid-19 cases and deaths. What accounts for this failure? The dismantling of the country's public health infrastructure has crippled contact tracing and exacerbated inequality as a disproportionate number of poor people and people of color have fallen ill with Covid-19. Inadequate regulation of the private for-profit sector has adversely affected the efficiency and quality of testing for the virus, and the prescription of costly drugs whose benefit and safety in treating infected patients have not been established. More stringent regulation of the commercial sector has led to the development of efficacious vaccines in a remarkably short time. Still, questions remain about the vaccines' effectiveness in the real world, and their safety.


Asunto(s)
/epidemiología , Pandemias , Trazado de Contacto , Predicción , Disparidades en el Estado de Salud , Humanos , Pandemias/prevención & control , Salud Pública/legislación & jurisprudencia , Administración en Salud Pública , Estados Unidos/epidemiología
2.
Nat Commun ; 12(1): 222, 2021 01 11.
Artículo en Inglés | MEDLINE | ID: mdl-33431854

RESUMEN

Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.


Asunto(s)
Brotes de Enfermedades/estadística & datos numéricos , Vigilancia de la Población/métodos , Incertidumbre , Infecciones por Coronavirus/epidemiología , Predicción , Humanos , Gripe Humana/epidemiología , Modelos Estadísticos
3.
PLoS One ; 16(1): e0244173, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33411744

RESUMEN

The novel coronavirus (COVID-19) is an emergent disease that initially had no historical data to guide scientists on predicting/ forecasting its global or national impact over time. The ability to predict the progress of this pandemic has been crucial for decision making aimed at fighting this pandemic and controlling its spread. In this work we considered four different statistical/time series models that are readily available from the 'forecast' package in R. We performed novel applications with these models, forecasting the number of infected cases (confirmed cases and similarly the number of deaths and recovery) along with the corresponding 90% prediction interval to estimate uncertainty around pointwise forecasts. Since the future may not repeat the past for this pandemic, no prediction model is certain. However, any prediction tool with acceptable prediction performance (or prediction error) could still be very useful for public-health planning to handle spread of the pandemic, and could policy decision-making and facilitate transition to normality. These four models were applied to publicly available data of the COVID-19 pandemic for both the USA and Italy. We observed that all models reasonably predicted the future numbers of confirmed cases, deaths, and recoveries of COVID-19. However, for the majority of the analyses, the time series model with autoregressive integrated moving average (ARIMA) and cubic smoothing spline models both had smaller prediction errors and narrower prediction intervals, compared to the Holt and Trigonometric Exponential smoothing state space model with Box-Cox transformation (TBATS) models. Therefore, the former two models were preferable to the latter models. Given similarities in performance of the models in the USA and Italy, the corresponding prediction tools can be applied to other countries grappling with the COVID-19 pandemic, and to any pandemics that can occur in future.


Asunto(s)
/epidemiología , Predicción/métodos , Modelos Biológicos , /mortalidad , Control de Enfermedades Transmisibles , Simulación por Computador , Toma de Decisiones , Humanos , Italia/epidemiología , Estados Unidos/epidemiología
4.
Ann Fam Med ; 19(1): 75-78, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33431398

RESUMEN

As I begin my 4th year of medical school amidst the coronavirus disease 2019 (COVID-19) pandemic, telehealth has allowed me to connect with many patients who previously struggled to access consistent primary care. In this essay, I describe 2 of my most formative experiences with telehealth: participating in my medical school's new "tele-hotspotting" elective, and providing virtual gender-affirming care through our student-run free clinic. These experiences demonstrate not only telehealth's utility during a viral pandemic, but also its potential as a powerful tool for expanding access to care and promoting health equity over the coming years. With this said, telehealth is not without limitations. Patients and clinicians alike have expressed concerns regarding the challenge of performing a physical exam and maintaining emotional connection across physical distance. A sustained expansion of telehealth is further challenged by inconsistent availability of broadband Internet, as well as a lack of standardized reimbursement procedures for telehealth visits. Strategies are available to help meet these challenges while maximizing health equity.


Asunto(s)
Predicción , Accesibilidad a los Servicios de Salud/tendencias , Atención Primaria de Salud/tendencias , Telemedicina/tendencias , Humanos , Atención Primaria de Salud/métodos
5.
Curr Sports Med Rep ; 20(1): 57-61, 2021 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-33395131

RESUMEN

ABSTRACT: Sports ultrasound (US) is a rapidly advancing and expanding field, where "hands-on" education and real-time instructor feedback are paramount in developing this skill. In light of a global pandemic and limited access to instructors and educational conferences, sports US education must adapt to continue to teach future ultrasonographers. Virtual US education, conducted using various virtual meeting platforms not only allows for continued didactic education but also can virtually recreate the "hands-on" training sessions with live, immediate instructor feedback that is necessary for acquiring competence. Additionally, using these methods, sports US conferences can continue in a virtual manner, sports US education can expand remote areas, and collaboration among distant experts may increase, all without the cost of travel and extended time away from work. While immediately relevant because of the COVID-19 pandemic, virtual US methods may continue to be beneficial as sports US education and collaboration continue to expand.


Asunto(s)
Traumatismos en Atletas/diagnóstico por imagen , Colaboración Intersectorial , Ultrasonografía/tendencias , Realidad Virtual , Predicción , Humanos
6.
Praxis (Bern 1994) ; 110(1): 48-53, 2021 Jan.
Artículo en Alemán | MEDLINE | ID: mdl-33406927

RESUMEN

Artificial Intelligence in Radiology - Definition, Potential and Challenges Abstract. Artificial Intelligence (AI) is omnipresent. It has neatly permeated our daily life, even if we are not always fully aware of its ubiquitous presence. The healthcare sector in particular is experiencing a revolution which will change our daily routine considerably in the near future. Due to its advanced digitization and its historical technical affinity radiology is especially prone to these developments. But what exactly is AI and what makes AI so potent that established medical disciplines such as radiology worry about their future job perspectives? What are the assets of AI in radiology today - and what are the major challenges? This review article tries to give some answers to these questions.


Asunto(s)
Inteligencia Artificial , Radiología , Predicción , Humanos , Aprendizaje Automático
8.
Environ Sci Pollut Res Int ; 28(3): 2948-2958, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-32897471

RESUMEN

The pandemic shock puts the world on quarantine and paused economic operations that affected energy consumption and economic output. This study analyzed the impact of the COVID-19 shock on GDP, energy consumption, and climate change then forecasted the situation until 2032 using the system dynamic modeling approach. The outcomes reveal that the pandemic shock will decrease the growth by 1.3% in 2020 and 1.32% in 2021. The current shortfall, low energy consumption, and delay in completion of energy-related projects can reduce the GDP by 5.2% in 2020. The effect will penetrate the system and will cause further losses in the upcoming years. The energy consumption and quarantine situation will improve the climate situation and drop the average temperature by 0.049 and 0.021 oC in 2020 and 2021. The aggregate demand and supply side measures such as national spending, lowering the lending rate, and cuts in income taxes can help in diffusing the situation. The government should start operations on ongoing energy projects, give relaxation to SME's with tight SOPs to secure jobs, and prevent possible GDP losses. The decline in oil prices provides an opportunity to cut fossil fuel subsidies and implement a carbon pricing mechanism.


Asunto(s)
Clima , Economía , Dióxido de Carbono/análisis , Predicción , Combustibles Fósiles , Humanos
9.
Soc Sci Med ; 268: 113473, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-33130402

RESUMEN

OBJECTIVE: We define prediction bias as the systematic error arising from an incorrect prediction of the number of positive COVID cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our objective is to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. To that end, our goal is to document EGPB in the comprehension of disease data, study how it evolves as the epidemic progresses, and connect it with compliance of personal safety guidelines such as the use of face coverings and social distancing. We also investigate whether a behavioral nudge, cost less to implement, can significantly reduce EGPB. RATIONALE: The scientific basis for our inquiry is the received wisdom that infectious disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. If people suffer from EGPB, they will likely make incorrect judgments about their infection risk, which in turn, may lead to reduced compliance of safety protocols. METHOD: To collect data on prediction bias, we ran an incentivized, experiment on a global, online platform with participation from people in forty-three countries, each at different stages of progression of COVID-19. We also constructed several indices of compliance by surveying participants about their frequency of hand-washing and use of sanitizers and masks; their willingness to pay for masks; their view about the social appropriateness of others' behavior; and their like/dislike of government responses. The prediction data was used to construct several measures of EGPB. Our experimental design permits us to identify the root of under-prediction as EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold. RESULTS: Respondents make predictions about the path of the disease using a model that is substantially less convex than the actual data generating process. This creates significant EGPB, which, in turn, is significantly and negatively associated with non-compliance with safety measures. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. A simple behavioral nudge that shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB. CONCLUSION: Behavioral biases concerning the comprehension of disease data are quantitatively important, and act as severe impediments to effective policy action against the spread of COVID-19. Clear communication of future infection risk via raw numbers could increase the accuracy of risk perception, in turn, facilitating compliance with suggested protective behaviors.


Asunto(s)
/epidemiología , Adhesión a Directriz/estadística & datos numéricos , Guías como Asunto , Salud Pública , Adulto , Sesgo , Femenino , Predicción , Desinfección de las Manos , Desinfectantes para las Manos/administración & dosificación , Humanos , Masculino , Máscaras/estadística & datos numéricos , Encuestas y Cuestionarios
10.
Lancet Infect Dis ; 21(1): 137-147, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-32702302

RESUMEN

BACKGROUND: The long-term benefits of pneumococcal conjugate vaccines (PCVs) remain unknown because of serotype replacement. We aimed to estimate the effect of PCV implementation on invasive pneumococcal disease incidence in France. METHODS: We did a quasi-experimental interrupted time-series analysis using data from a French national prospective surveillance system. We included all invasive pneumococcal disease cases in children and adults from more than 250 participating hospitals between Jan 1, 2001, and Dec 31, 2017. The primary outcome was incidence of invasive pneumococcal disease (meningitis and non-meningitis) over time, analysed by segmented regression with autoregressive error. Isolates were serotyped by latex agglutination with antiserum samples. FINDINGS: We included 75 903 patients with invasive pneumococcal disease, including 4302 (5·7%) children younger than 2 years and 37 534 (49·4%) adults aged 65 years or older. Before PCV7 implementation, the estimated monthly incidence of invasive pneumococcal disease was 0·78 cases per 100 000 inhabitants, which did not change significantly up to May, 2010. PCV13 implementation in 2010 was followed by a significant decrease in the incidence of invasive pneumococcal disease (-1·5% per month, 95% CI -2·2 to -0·8), reaching an estimated monthly incidence of 0·52 cases per 100 000 inhabitants in December, 2014. From January, 2015, the incidence rebounded (1·8% per month, 95% CI 1·0 to 2·6), reaching an estimated monthly incidence of 0·73 cases per 100 000 inhabitants in December, 2017. The estimated monthly incidence increased from 0·93 cases per 100 000 in December, 2014, to 1·73 cases per 100 000 in December, 2017, for children younger than 2 years, and from 1·54 cases per 100 000 in December, 2014, to 2·08 cases per 100 000 in December, 2017, for adults aged 65 years or older. The main non-PCV13 serotypes involved in the increase were 24F in young children and 12F, 22F, 9N, and 8 in adults aged 65 years or older. INTERPRETATION: PCV13 implementation led to a major reduction in the incidence of invasive pneumococcal disease. However, a rebound in cases among children and adults since 2015, driven by several emerging non-PCV13 serotypes, jeopardises the long-term PCV benefits. These findings, if confirmed in the coming years, should be considered in the development of next-generation PCVs and might guide policy makers in the selection of future pneumococcal vaccines. FUNDING: Foundation for Medical Research; Pfizer, BioMérieux, Sanofi for the Regional Observatory of Pneumococci.


Asunto(s)
Programas de Inmunización/estadística & datos numéricos , Programas de Inmunización/tendencias , Infecciones Neumocócicas/epidemiología , Infecciones Neumocócicas/prevención & control , Vacunas Neumococicas/administración & dosificación , Vacunas Conjugadas/administración & dosificación , Adolescente , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Niño , Preescolar , Femenino , Predicción , Francia , Humanos , Incidencia , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Estudios Retrospectivos , Vigilancia de Guardia , Factores de Tiempo , Adulto Joven
11.
Lancet Infect Dis ; 21(1): 127-136, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-32702303

RESUMEN

BACKGROUND: Ten-valent and 13-valent pneumococcal conjugate vaccines (PCVs) have shown important benefits by decreasing invasive pneumococcal disease caused by vaccine serotypes. Belgium had an uncommon situation with sequential use of PCV7, PCV13, and PCV10 in the childhood vaccination programmes between 2007 and 2018. We aimed to analyse the changes in incidence of invasive pneumococcal disease and serotype distribution in children throughout this period. METHODS: Streptococcus pneumoniae isolates were obtained from patients with invasive pneumococcal disease in Belgium between 2007 and 2018 by the national laboratory-based surveillance. Paediatric invasive pneumococcal disease incidence, serotype distribution, and antimicrobial susceptibility were analysed in periods during which PCV7 (2009-10), PCV13 (2013-14), both PCV13 and PCV10 (2015-16), and PCV10 (2017-18) were used. Incidence rates and trends were compared. Vaccination status was collected. For a subset of serotype 19A isolates, multilocus sequence type was identified. FINDINGS: After a decrease in PCV7 serotype invasive pneumococcal disease was observed during the PCV7 period, total paediatric invasive pneumococcal disease incidence significantly declined during the PCV13 period (-2·6% monthly, p<0·0001). During the PCV13-PCV10 period (2015-16), the lowest mean in paediatric invasive pneumococcal disease incidence was achieved, but the incidence increased again during the PCV10 period (2017-18), especially in children younger than 2 years (+1·7% monthly; p=0·028). This increase was mainly due to a significant rise in serotype 19A invasive pneumococcal disease incidence in the PCV10 period compared with the PCV13 period (p<0·0001), making serotype 19A the predominant serotype in paediatric invasive pneumococcal disease in the PCV10 period. Genetic diversity within the 2017-18 serotype 19A collection was seen, with two predominant clones, ST416 and ST994, that were infrequently observed before PCV10 introduction. In 2018, among children younger than 5 years with invasive pneumococcal disease who were correctly vaccinated, 37% (37 of 100) had PCV13 serotype invasive pneumococcal disease, all caused by serotype 19A and serotype 3. INTERPRETATION: After a significant decrease during the PCV13 period, paediatric invasive pneumococcal disease incidence increased again during the PCV10 period. This observation mainly resulted from a significant increase of serotype 19A cases. During the PCV10 period, dominant serotype 19A clones differed from those detected during previous vaccine periods. Whether changes in epidemiology resulted from the vaccine switch or also from natural evolution remains to be further elucidated. FUNDING: The Belgian National Reference is funded by the Belgian National Institute for Health and Disability Insurance and the whole genome sequencing by an investigator-initiated research grant from Pfizer.


Asunto(s)
Programas de Inmunización/estadística & datos numéricos , Programas de Inmunización/tendencias , Infecciones Neumocócicas/epidemiología , Infecciones Neumocócicas/prevención & control , Vacunas Neumococicas/administración & dosificación , Vacunas Conjugadas/administración & dosificación , Adolescente , Factores de Edad , Bélgica/epidemiología , Niño , Preescolar , Femenino , Predicción , Humanos , Incidencia , Lactante , Recién Nacido , Masculino , Estudios Retrospectivos , Vigilancia de Guardia
12.
Lancet Infect Dis ; 21(1): 107-115, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-32717205

RESUMEN

BACKGROUND: The WHO Access, Watch, and Reserve (AWaRe) antibiotic classification framework aims to balance appropriate access to antibiotics and stewardship. We aimed to identify how patterns of antibiotic consumption in each of the AWaRe categories changed across countries over 15 years. METHODS: Antibiotic consumption was classified into Access, Watch, and Reserve categories for 76 countries between 2000, and 2015, using quarterly national sample survey data obtained from IQVIA. We measured the proportion of antibiotic use in each category, and calculated the ratio of Access antibiotics to Watch antibiotics (access-to-watch index), for each country. FINDINGS: Between 2000, and 2015, global per-capita consumption of Watch antibiotics increased by 90·9% (from 3·3 to 6·3 defined daily doses per 1000 inhabitants per day [DIDs]) compared with an increase of 26·2% (from 8·4 to 10·6 DIDs) in Access antibiotics. The increase in Watch antibiotic consumption was greater in low-income and middle-income countries (LMICs; 165·0%; from 2·0 to 5·3 DIDs) than in high-income countries (HICs; 27·9%; from 6·1 to 7·8 DIDs). The access-to-watch index decreased by 38·5% over the study period globally (from 2·6 to 1·6); 46·7% decrease in LMICs (from 3·0 to 1·6) and 16·7% decrease in HICs (from 1·8 to 1·5), and 37 (90%) of 41 LMICs had a decrease in their relative access-to-watch consumption. The proportion of countries in which Access antibiotics represented at least 60% of their total antibiotic consumption (the WHO national-level target) decreased from 50 (76%) of 66 countries in 2000, to 42 (55%) of 76 countries in 2015. INTERPRETATION: Rapid increases in Watch antibiotic consumption, particularly in LMICs, reflect challenges in antibiotic stewardship. Without policy changes, the WHO national-level target of at least 60% of total antibiotic consumption being in the Access category by 2023, will be difficult to achieve. The AWaRe framework is an important measure of the effort to combat antimicrobial resistance and to ensure equal access to effective antibiotics between countries. FUNDING: US Centers for Disease Control and Prevention.


Asunto(s)
Antibacterianos/economía , Comercio/economía , Comercio/tendencias , Países en Desarrollo/estadística & datos numéricos , Utilización de Medicamentos/economía , Utilización de Medicamentos/tendencias , Preparaciones Farmacéuticas/economía , Programas de Optimización del Uso de los Antimicrobianos , Comercio/estadística & datos numéricos , Utilización de Medicamentos/estadística & datos numéricos , Predicción , Humanos , Organización Mundial de la Salud
13.
Sci Total Environ ; 755(Pt 1): 142561, 2021 Feb 10.
Artículo en Inglés | MEDLINE | ID: mdl-33039891

RESUMEN

Recent years have seen a rise of techniques based on artificial intelligence (AI). With that have also come initiatives for guidance on how to develop "responsible AI" aligned with human and ethical values. Compared to sectors like energy, healthcare, or transportation, the use of AI-based techniques in the water domain is relatively modest. This paper presents a review of current AI applications in the water domain and develops some tentative insights as to what "responsible AI" could mean there. Building on the reviewed literature, four categories of application are identified: modeling, prediction and forecasting, decision support and operational management, and optimization. We also identify three insights pertaining to the water sector in particular: the use of AI techniques in general, and many-objective optimization in particular, that allow for a pluralism of values and changing values; the use of theory-guided data science, which can avoid some of the pitfalls of strictly data-driven models; and the ability to build on experiences with participatory decision-making in the water sector. These insights suggest that the development and application of responsible AI techniques for the water sector should not be left to data scientists alone, but requires concerted effort by water professionals and data scientists working together, complemented with expertise from the social sciences and humanities.


Asunto(s)
Inteligencia Artificial , Agua , Predicción , Humanos
14.
Waste Manag ; 119: 275-284, 2021 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-33099072

RESUMEN

Critical high-tech minerals (CHTMs) are raw materials that are essential for a future clean-energy transition and the manufacture of high-end products. Cellphones, one of the fastest growing electronic products, contain various CHTMs. Since 2019, India has surpassed the United States to become the second largest smartphone market in the world. An increasing and alarming number of excessive waste cellphones will be generated in India in the near future. In this study, the dynamic material flow analysis approach and the Weibull distribution are adopted to analyze the volumes of accumulated waste cellphones and the contained CHTMs based on the differentiation between smartphones and feature phones in India. Moreover, a market supply model is adopted to predict the future trends of CHTMs in waste cellphones. The results show a general upward tendency of waste cellphone volume in India, which indicates that various CHTMs contained in cellphone waste can be properly reused or recycled. Future implications based on the analysis results are provided for efficient cellphone management in India.


Asunto(s)
Teléfono Celular , Administración de Residuos , Predicción , India , Minerales , Reciclaje
15.
J Theor Biol ; 509: 110501, 2021 01 21.
Artículo en Inglés | MEDLINE | ID: mdl-32980371

RESUMEN

We model the COVID-19 coronavirus epidemics in China, South Korea, Italy, France, Germany and the United Kingdom. We identify the early phase of the epidemics, when the number of cases grows exponentially, before government implementation of major control measures. We identify the next phase of the epidemics, when these social measures result in a time-dependent exponentially decreasing number of cases. We use reported case data, both asymptomatic and symptomatic, to model the transmission dynamics. We also incorporate into the transmission dynamics unreported cases. We construct our models with comprehensive consideration of the identification of model parameters. A key feature of our model is the evaluation of the timing and magnitude of implementation of major public policies restricting social movement. We project forward in time the development of the epidemics in these countries based on our model analysis.


Asunto(s)
/epidemiología , Epidemias , Predicción/métodos , Modelos Estadísticos , /transmisión , China/epidemiología , Francia/epidemiología , Alemania/epidemiología , Implementación de Plan de Salud/normas , Humanos , Italia/epidemiología , Pandemias , Política Pública , Cuarentena , República de Corea/epidemiología , Aislamiento Social , Reino Unido/epidemiología
16.
Ecotoxicol Environ Saf ; 208: 111470, 2021 Jan 15.
Artículo en Inglés | MEDLINE | ID: mdl-33091772

RESUMEN

A new dimension of learning lessons from the occurrence of hazardous events involving dangerous substances is considered relying on the availability of representative data and the significant evolution of a wide range of machine learning tools. The importance of such a dimension lies in the possibility of predicting the associated nature of damages without imposing any unrealistic simplifications or restrictions. To provide the best possible modeling framework, several implementations are tested using logistic regression, decision trees, neural networks, support vector machine, naive Bayes classifier and random forests to forecast the occurrence of the human, environmental and material consequences of industrial accidents based on the EU Major Accident Reporting System's records. Many performance metrics are estimated to select the most suitable model in each treated case. The obtained results show the distinctive ability of random forests and neural networks to predict the occurrence of specific consequences of accidents in the industrial installations, with an obvious exception concerning the performance of this latter algorithm when the involved datasets are highly unbalanced.


Asunto(s)
Liberación de Peligros Químicos , Aprendizaje Automático , Accidentes , Algoritmos , Teorema de Bayes , Predicción , Humanos , Modelos Logísticos , Redes Neurales de la Computación , Máquina de Vectores de Soporte
17.
Environ Sci Pollut Res Int ; 28(1): 56-72, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-33044693

RESUMEN

PM2.5 (particulate matter with a size/diameter ≤ 2.5 µm) is an important air pollutant that affects human health, especially in urban environments. However, as time-series data of PM2.5 are non-linear and non-stationary, it is difficult to predict future PM2.5 distribution and behavior. Therefore, in this paper, we propose a hybrid short-term urban PM2.5 prediction model based on variational mode decomposition modified by the correntropy criterion, the state transition simulated annealing (STASA) algorithm, and a support vector regression model to overcome the disadvantages of traditional forecasting techniques which consider different environmental factors. Two experiments were performed with the model to assess its effectiveness and predictive ability: in experiment I, we verified the performance of STASA on benchmark functions, while in experiment II, we used PM2.5 data from different epochs and regions of Beijing to verify its forecasting performance. The experimental results showed that the proposed model is robust and can achieve satisfactory prediction results under different conditions compared with current forecasting techniques.


Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Beijing , Monitoreo del Ambiente , Predicción , Humanos , Material Particulado/análisis
19.
J Public Health Manag Pract ; 27 Suppl 1, COVID-19 and Public Health: Looking Back, Moving Forward: S101-S105, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33239571

RESUMEN

Public health laboratories have played a central role in the US response to COVID-19. Since the earliest days, myriad issues have impeded the laboratory community's ability to keep pace with the overwhelming demand for effective tests. In this article, the Association of Public Health Laboratories and a subset of its members examine the response to date and evaluate lessons learned from 4 main categories: testing surges, supplies, staffing, and regulations and policy. Within these categories, the authors offer recommendations intended both to improve the ongoing COVID-19 response and to strengthen planning for future outbreaks.


Asunto(s)
/prevención & control , Brotes de Enfermedades/prevención & control , Guías como Asunto , Ciencia del Laboratorio Clínico/tendencias , Pandemias/prevención & control , Salud Pública/normas , Salud Pública/tendencias , /epidemiología , Predicción , Humanos , Ciencia del Laboratorio Clínico/estadística & datos numéricos , Estados Unidos/epidemiología
20.
Waste Manag ; 120: 828-838, 2021 Feb 01.
Artículo en Inglés | MEDLINE | ID: mdl-33281044

RESUMEN

Waste electrical and electronic equipment (viz., WEEE or e-waste) is the fastest-growing type of hazardous solid waste in the worldwide. The accurate prediction of the amount of e-waste might help improve the efficiency of e-waste disposal. In this study, a novel decomposition-ensemble-based hybrid forecasting methodology that integrates variational mode decomposition (VMD), exponential smoothing model (ESM), and grey modeling (GM) methods (named VMD-ESM-GM) is proposed for e-waste quantity prediction. For verification purposes, sample data from Washington State, US, and UK Environment Agency are analyzed. Compared to benchmark models, the proposed VMD-ESM-GM methodology not only obtains a satisfactory prediction result for e-waste data but also predicts the future fluctuation trend of e-waste. These results indicate that the proposed VMD-ESM-GM methodology based on the decomposition-ensemble principle is a suitable model for the prediction of the e-waste quantity and could help decision-makers develop both e-waste recycling plans and circular economy plans.


Asunto(s)
Residuos Electrónicos , Eliminación de Residuos , Residuos Electrónicos/análisis , Electrónica , Predicción , Reciclaje
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