scholarly journals Estimating spatiotemporal patterns of deaths by COVID-19 outbreak on a global scale

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e047002
Author(s):  
Fernanda Valente ◽  
Marcio Poletti Laurini
Keyword(s):  
Measurement Error ◽  
Study Design ◽  
Spatial Dynamics ◽  
Secondary Data ◽  
Global Scale ◽  
Dependency Structure ◽  
Proposed Model ◽  
Spatial And Temporal Heterogeneity ◽  
Measures Of Dispersion ◽  
Spatiotemporal Models

ObjectiveOur main objective is to estimate the trend of deaths by COVID-19 on a global scale, considering the six continents.Study designThe study design was a retrospective observational study conducted using the secondary data provided by the Our World in Data project on a public domain.SettingThis study was conducted based on worldwide deaths by COVID-19 recorded for the Our World in Data project from 29 February 2020 to 17 February 2021.MethodsEstimating the trend in COVID-19 deaths is not a trivial task due to the problems associated with the COVID-19 data, such as the spatial and temporal heterogeneity, observed seasonality and the delay between the onset of symptoms and diagnosis, indicating a relevant measurement error problem and changing the series’ dependency structure. To bypass the aforementioned problems, we propose a method to estimate the components of trend, seasonality and cycle in COVID-19 data, controlling for the presence of measurement error and considering the spatial heterogeneity. We used the proposed model to estimate the trend component of deaths by COVID-19 on a global scale.ResultsThe model was able to capture the patterns in the occurrence of deaths related to COVID-19, overcoming the problems observed in COVID-19 data. We found compelling evidence that spatiotemporal models are more accurate than univariate models to estimate the patterns of the occurrence of deaths. Based on the measures of dispersion of the models’ prediction in relation to observed deaths, it is possible to note that the models with spatial component are significantly superior to the univariate model.ConclusionThe findings suggested that the spatial dynamics have an important role in the COVID-19 epidemic process since the results provided evidence that spatiotemporal models are more accurate to estimate the general patterns of the occurrence of deaths related to COVID-19.

Spatiotemporal clustering, social inequities and the risk of leptospirosis in an endemic area of Brazil: a retrospective spatial modelling

2020 ◽  
Author(s):  
Katyucia O C de Souza ◽  
José Augusto P Góes ◽  
Matheus S Melo ◽  
Paula M G Leite ◽  
Lucas A Andrade ◽  
...  
Keyword(s):  
Urban Areas ◽  
Spatial Regression ◽  
Temporal Trends ◽  
Spatial Dynamics ◽  
Secondary Data ◽  
The State ◽  
Scan Statistics ◽  
Northeastern Brazil ◽  
Metropolitan Region ◽  
Social Inequities

Abstract Background Leptospirosis is an endemic disease in Brazil of social and economic relevance related to behavioural and socioenvironmental factors. This study aimed to analyse the spatiotemporal distribution of the incidence of leptospirosis and its association with social determinants in health in a state of northeastern Brazil. Methods An ecological study of temporal series with techniques of spatial analysis using secondary data of the cases of leptospirosis notified in the Information System of Notifiable Diseases of the state of Sergipe (2008–2017) was conducted. The analysis of temporal trends was performed using Poisson regression. Spatial analyses were performed using the Moran index, the local empirical Bayesian model, scan statistics and spatial regression. Results The incidence rate decreased from 3.66 to 1.44 cases per 100 000 inhabitants in 2008 and 2017, respectively. Leptospirosis was associated with social inequities, mostly affecting males aged 20–49 y living in urban areas. The space-time scan indicated the formation of a risk cluster in municipalities in the metropolitan region of the state. Conclusions The data indicated the persistence of leptospirosis transmission, maintaining a pattern of high endemicity in some municipalities associated with social inequities. The study showed the temporal and spatial dynamics of the disease to better target specific actions for prevention and control.

Establishment and comparison of a spatial dynamics model for virtual track train with different steering modes

2021 ◽  
pp. 146441932110240
Author(s):  
Zhonghui Yin ◽  
Jiye Zhang ◽  
Haiying Lu
Keyword(s):  
Pid Controller ◽  
Dynamic Performance ◽  
Spatial Dynamics ◽  
Steering System ◽  
Front Wheel ◽  
Proportional Integral Derivative ◽  
Dynamics Model ◽  
Proposed Model ◽  
Suspension Control ◽  
Curved Section

To solve the urbanization and the economic challenges, a virtual track train (VTT) transportation system has been proposed in China. To evaluate the dynamic behavior of the VTT, a spatial dynamics model has been developed that considers the suspension system and the steering system. Additionally, the model takes into account road irregularity to make simulations more realistic. Based on the newly proposed dynamic model and a designed proportional–integral–derivative (PID) controller, simulation frames of the vehicle and of the VTT are established with the path-tracking performance. The results show that the vehicle and the VTT can run along a desired lane with allowable errors, verifying the proposed model. The vehicle and VTT with the four-wheel steering system show a better dynamic performance than the models with the front-wheel steering system in the curved section. Moreover, the simulation frame can be further applied to dynamics-related assessments, parameter optimization and active suspension control strategy.

scholarly journals Measurements and Computations of Internal Temperatures of the IGBT and the Diode Situated in the Common Case

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 210
Author(s):  
Paweł Górecki ◽  
Krzysztof Górecki
Keyword(s):  
Thermal Properties ◽  
Measurement Error ◽  
Thermal Model ◽  
Bipolar Transistor ◽  
Internal Temperature ◽  
Self Heating ◽  
Factors Influencing ◽  
Proposed Model ◽  
The Common ◽  
Transient Thermal

This article proposes effective methods of measurements and computations of internal temperature of the dies of the Insulted Gate Bipolar Transistor (IGBT) and the diode mounted in the common case. The nonlinear compact thermal model of the considered device is proposed. This model takes into account both self-heating phenomena in both dies and mutual thermal couplings between them. In the proposed model, the influence of the device internal temperature on self and transfer thermal resistances is taken into account. Methods of measurements of each self and transfer transient thermal impedances occurring in this model are described and factors influencing the measurement error of these methods are analysed. Some results illustrating thermal properties of the investigated devices including the IGBT and the antiparallel diode in the common case are shown and discussed. Computations illustrating the usefulness of the proposed compact thermal model are presented and compared to the results of measurements. It is proved that differences between internal temperature of both dies included in the TO-247 case can exceed even 15 K.

scholarly journals PERAN SOSIAL EKONOMI KELUARGA DALAM MENANGGULANGI KESEHATAN ANAK DI INDONESIA

2020 ◽  
Vol 6 (1) ◽  
pp. 59-71
Author(s):  
Syifa Pramudita Faddila ◽  
Laras Ratu Khalida ◽  
Uus Mohammad Darul Fadli ◽  
Aji Tuhagana
Keyword(s):  
Study Design ◽  
Secondary Data ◽  
Children's Health ◽  
Children’S Health ◽  
Eating Patterns ◽  
Family Role ◽  
High Fat ◽  
Obesity Rate

Abstrak Peningkatan sosial ekonomi keluarga, maka kecenderungan pola makan pun akan mengalami perubahan. Sosial ekonomi keluarga juga memiliki korelasi dengan frekuensi membeli makanan diluar rumah yang cenderung mengandung lemak yang tinggi. Secara global, sebanyak 42 juta anak mengalami overweight pada tahun 2015 dan angka kegemukan di Indonesia sekitar 10,8% pada tahun 2013. Tujuan penelitian ini adalah untuk mengetahui peran sosial ekonomi keluarga dalam menanggulangi kesehatan anak di Indonesia. Penelitian ini menggunakan data sekunder Riskesdas 2013 dengan desain studi deskriptif verifikatif dimana sampel penelitian sebanyak 49.620 anak. Hasil penelitian menunjukkan 14,5% anak mengalami overweight dengan sosial ekonomi keluarga menengah atas (kuintil 4) sebesar 23,9%. Artinya hampir ¼ anak usia 10-12 tahun di Indonesia berada pada keluarga dengan sosial ekonomi yang lebih dari cukup. Dibutuhkan peran keluarga yang solid untuk mengontrol pendapatan yang tepat guna untuk meningkatkan kesehatan anak. Kata kunci: Overweight, Sosial Ekonomi Keluarga, Anak     Abstract Increasing the family's socioeconomic, then the tendency for eating patterns will change. The socioeconomic family also has a correlation with the frequency of buying food outside the home which tends to contain high fat. Globally, as many as 42 million children were overweight in 2015 and the obesity rate in Indonesia was around 10.8% in 2013. The purpose of this study was to determine the socioeconomic role of families in tackling children's health in Indonesia. This research uses Riskesdas 2013 secondary data with a descriptive verification study design in which the research sample is 49,620 children. The results showed that 14.5% of children were overweight with upper middle family socioeconomic (quintile 4) of 23.9%. This means that almost ¼ children aged 10-12 years in Indonesia are in families with more than enough socioeconomic. A solid family role is needed to control appropriate income to improve children's health.   Keywords: Overweight, Family Socio-Economic, Children

scholarly journals Impact of Facebook and Newspaper Advertising on Sales: A Comparative Study of Online and Print Media

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yushan Lin ◽  
Zubair Ahmad ◽  
Wasswa Shafik ◽  
Saima K. Khosa ◽  
Zahra Almaspoor ◽  
...  
Keyword(s):  
Quantitative Research ◽  
Statistical Tests ◽  
Secondary Data ◽  
Research Approach ◽  
Statistical Tool ◽  
Family Approach ◽  
Advertising Budget ◽  
Proposed Model ◽  
Heavy Tailed ◽  
Linear Regression Modeling

Marketing means the strategies and tactics an organization undertakes for attracting consumers to promote the buying or selling of a product or service. Active marketing is about receiving messages from potential buyers to create ways to influence their purchasing decisions. Advertising is one of the most prominent marketing strategies to promote products to consumers. It is well known that advertisement has a significant impact on the sale of certain goods or services. In this paper, we consider two mediums of advertisement, such as Facebook (which is an online medium) and Newspaper (which is a printed medium). We consider a dataset representing the advertising budget (in hundreds of US dollars) of an electronic company and the sales of that company. We apply the quantitative research approach, and the data which are used in this research are secondary data. For analysis purposes, we consider a statistical tool called simple linear regression modeling. To check the significance of the advertising on sale, definite statistical tests are applied. Based on the findings of this research, it is observed that advertising has a significant impact on sales. It is also showed that spending money on advertising through Facebook has better sales than newspapers. The finding of this research shows that the use of computer-based technologies and online mediums has a brighter future for advertising. Furthermore, a new statistical model is introduced using the Z family approach. The proposed model is very interesting and possesses heavy-tailed properties. Finally, the applicability of the proposed model is illustrated by considering the financial dataset.

scholarly journals Mathematical modeling approach to predict COVID-19 infected people in Sri Lanka

2021 ◽  
Vol 7 (3) ◽  
pp. 4672-4699
Author(s):  
I. H. K. Premarathna ◽  
◽  
H. M. Srivastava ◽  
Z. A. M. S. Juman ◽  
Ali AlArjani ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Secondary Data ◽  
Logistic Growth ◽  
Solution Technique ◽  
The Novel ◽  
Infected People ◽  
Proposed Model ◽  
New Finding ◽  
Stochastic Forecasting ◽  
The Government

<abstract> <p>The novel corona virus (COVID-19) has badly affected many countries (more than 180 countries including China) in the world. More than 90% of the global COVID-19 cases are currently outside China. The large, unanticipated number of COVID-19 cases has interrupted the healthcare system in many countries and created shortages for bed space in hospitals. Consequently, better estimation of COVID-19 infected people in Sri Lanka is vital for government to take suitable action. This paper investigates predictions on both the number of the first and the second waves of COVID-19 cases in Sri Lanka. First, to estimate the number of first wave of future COVID-19 cases, we develop a stochastic forecasting model and present a solution technique for the model. Then, another solution method is proposed to the two existing models (SIR model and Logistic growth model) for the prediction on the second wave of COVID-19 cases. Finally, the proposed model and solution approaches are validated by secondary data obtained from the Epidemiology Unit, Ministry of Health, Sri Lanka. A comparative assessment on actual values of COVID-19 cases shows promising performance of our developed stochastic model and proposed solution techniques. So, our new finding would definitely be benefited to practitioners, academics and decision makers, especially the government of Sri Lanka that deals with such type of decision making.</p> </abstract>

Natural Experiments in Business Research Methods

2021 ◽  
Author(s):  
Michael C. Withers ◽  
Chi Hon Li
Keyword(s):  
Study Design ◽  
Methodological Approach ◽  
Secondary Data ◽  
Control Group ◽  
Causal Relationships ◽  
Random Assignment ◽  
Natural Experiments ◽  
Theoretical Understanding ◽  
Research Questions ◽  
Viable Approach

Causal identification is an important consideration for organizational researchers as they attempt to develop a theoretical understanding of the causes and effects of organizational phenomena. Without valid causal identification, insights regarding organizational phenomena are challenging given their inherent complexity. In other words, organizational research will be limited in its scientific progression. Randomized controlled experiments are often suggested to provide the ideal study design necessary to address potential confounding effects and isolate true causal relationships. Nevertheless, only a few research questions lend themselves to this study design. In particular, the full randomization of subjects in the treatment and control group may not be possible due to the empirical constraints. Within the strategic management area, for example, scholars often use secondary data to examine research questions related to competitive advantage and firm performance. Natural experiments are increasingly recognized as a viable approach to identify causal relationships without true random assignment. Natural experiments leverage external sources of variation to isolate causal effects and avoid potentially confounding influences that often arise in observational data. Natural experiments require two key assumptions—the as-if random assignment assumption and the stable unit treatment value assumption. When these assumptions are met, natural experiments can be an important methodological approach for advancing causal understanding of organizational phenomena.

Collaborations in the Open Innovation Era

2010 ◽  
pp. 61-86 ◽  
Author(s):  
Annamária Inzelt
Keyword(s):  
Open Innovation ◽  
New Technologies ◽  
New Technology ◽  
Secondary Data ◽  
Transition Process ◽  
Data Bank ◽  
Global Scale ◽  
Foreign Companies ◽  
Global Innovation ◽  
Technology Products

Although the impact of open innovation on a global scale on the collaboration between universities and foreign industry is clearly important, empirical evidence from the field is lacking. This chapter investigates the collaboration between Hungarian universities and foreign companies in research and development. The chapter attempts to provide a relevant picture of the research-related linkages of Hungarian universities and foreign companies by employing secondary data processed from various data-banks. The analysis suggests that foreign direct investment and foreign companies play major roles in the internationalisation of research during this second decade of the transition process. Assessing the research and technology products which have originated in university-industry collaboration is no easy task. According to experimental measurements and pilot data-bank, there were more joint publications involving foreign than domestic companies, and the citation value per publication was significantly higher with the former. Data-bank also show that developments in new technology in terms of patent figures rarely involved university-owned or co-owned inventions, although there is some evidence there are more patents which are university-related than owned. Domestic invention and the foreign ownership of patents represent one more sign of Hungarian involvement in global innovation in the development of new technologies.

A Soft Computing Approach to Customer Segmentation

2016 ◽  
pp. 119-146
Author(s):  
Abdulkadir Hiziroglu
Keyword(s):  
Soft Computing ◽  
Secondary Data ◽  
Customer Segmentation ◽  
Two Stage ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Unique Customer ◽  
Managerial Implications ◽  
Clustering Approach ◽  
Retail Company

There are a number of traditional models designed to segment customers, however none of them have the ability to establish non-strict customer segments. One crucial area that can meet this requirement is known as soft computing. Although there have been studies related to the usage of soft computing techniques for segmentation, they are not based on the effective two-stage methodology. The aim of this study is to propose a two-stage segmentation model based on soft computing using the purchasing behaviours of customers in a data mining framework and to make a comparison of the proposed model with a traditional two-stage segmentation model. Segmentation was performed via neuro-fuzzy two stage-clustering approach for a secondary data set, which included more than 300,000 unique customer records, from a UK retail company. The findings indicated that the model provided stronger insights and has greater managerial implications in comparison with the traditional two-stage method with respect to six segmentation effectiveness indicators.

scholarly journals Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery

2020 ◽  
Author(s):  
Christina Corbane ◽  
Vasileios Syrris ◽  
Filip Sabo ◽  
Panagiotis Politis ◽  
Michele Melchiorri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Human Settlements ◽  
Modeling Methodology ◽  
Proposed Model ◽  
Reference Layer ◽  
Sentinel 2 ◽  
Core Features

Abstract Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world. The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery. A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The core features of the proposed model are the image patch of size 5 × 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference dataset of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. The results of this study contribute to cutting-edge research in the field of automated built-up areas mapping from remote sensing data and establish a new reference layer for the analysis of the spatial distribution of human settlements across the rural–urban continuum.

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