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Wang H, Ye H, Liu L. Constructing big data prevention and control model for public health emergencies in China: A grounded theory study. Front Public Health 2023; 11:1112547. [PMID: 37006539 PMCID: PMC10060899 DOI: 10.3389/fpubh.2023.1112547] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Big data technology plays an important role in the prevention and control of public health emergencies such as the COVID-19 pandemic. Current studies on model construction, such as SIR infectious disease model, 4R crisis management model, etc., have put forward decision-making suggestions from different perspectives, which also provide a reference basis for the research in this paper. This paper conducts an exploratory study on the construction of a big data prevention and control model for public health emergencies by using the grounded theory, a qualitative research method, with literature, policies, and regulations as research samples, and makes a grounded analysis through three-level coding and saturation test. Main results are as follows: (1) The three elements of data layer, subject layer, and application layer play a prominent role in the digital prevention and control practice of epidemic in China and constitute the basic framework of the “DSA” model. (2) The “DSA” model integrates cross-industry, cross-region, and cross-domain epidemic data into one system framework, effectively solving the disadvantages of fragmentation caused by “information island”. (3) The “DSA” model analyzes the differences in information needs of different subjects during an outbreak and summarizes several collaborative approaches to promote resource sharing and cooperative governance. (4) The “DSA” model analyzes the specific application scenarios of big data technology in different stages of epidemic development, effectively responding to the disconnection between current technological development and realistic needs.
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Affiliation(s)
- Huiquan Wang
- School of Politics and Public Administration, China University of Political Science and Law, Beijing, China
| | - Hong Ye
- School of Foreign Studies, China University of Political Science and Law, Beijing, China
- *Correspondence: Hong Ye
| | - Lu Liu
- School of Engineering and Technology, China University of Geosciences, Beijing, China
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Patil S, Pandya S. Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models. Front Public Health 2021; 9:798034. [PMID: 34900929 PMCID: PMC8661059 DOI: 10.3389/fpubh.2021.798034] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R 2) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.
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Affiliation(s)
- Seema Patil
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
| | - Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
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Hridoy AEE, Mohiman MA, Tusher SMSH, Nowraj SZA, Rahman MA. Impact of meteorological parameters on COVID-19 transmission in Bangladesh: a spatiotemporal approach. THEORETICAL AND APPLIED CLIMATOLOGY 2021; 144:273-285. [PMID: 33551528 PMCID: PMC7854875 DOI: 10.1007/s00704-021-03535-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 01/11/2021] [Indexed: 05/03/2023]
Abstract
It has been more than 10 months since the first COVID-19 case was reported in Wuhan, China, still menacing the world with a possible second wave. This study aimed to analyze how meteorological variables can affect the spread of local COVID-19 transmission in Bangladesh. Nine spatial units were considered from a meteorological standpoint to characterize COVID-19 transmission in Bangladesh. The daily COVID-19 incidence and meteorological variable (e.g., mean temperature, relative humidity, precipitation, and wind speed) data from April 5 to September 20, 2020, were collected. The Spearman rank correlation, heat maps, and multivariate quasi-Poisson regression were employed to understand their association. The effect of meteorological variables on COVID-19 transmission was modeled using a lag period of 10 days. Results showed that mean temperature, relative humidity, and wind speed are substantially associated with an increased risk of COVID-19. On the other hand, daily precipitation is significantly associated with a decreased risk of COVID-19 incidence. The relative risks (RR) of mean temperature for daily COVID-19 incidences were 1.222 (95% confidence interval [CI], 1.214-1.232). For wind speed, the RR was 1.087 (95% CI, 1.083-1.090). For relative humidity, the RR was 1.027 (95% CI, 1.025-1.029). Overall, this study found the profound effect of meteorological parameters on COVID-19 incidence across selected nine areas in Bangladesh. This study is probably the first study to explore the impact of region-specific meteorological conditions on COVID-19 incidence in Bangladesh. Moreover, adjustments on the areal-aggregated and regional levels were made for three confounding factors, including lockdown, population density, and potential seasonal effects. The study's findings suggest that SARS-CoV-2 can be transmitted in high temperatures and humidity conditions, which contradicts many other countries' prior studies. The research outcomes will provide implications for future control and prevention measures in Bangladesh and other countries with similar climate conditions and population density.
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Affiliation(s)
- Al-Ekram Elahee Hridoy
- Department of Geography and Environmental Studies, University of Chittagong, Chittagong, 4331 Bangladesh
| | - Md. Abdul Mohiman
- Department of Geography and Environmental Studies, University of Chittagong, Chittagong, 4331 Bangladesh
| | | | - Sayed Ziaul Amin Nowraj
- Department of Geography and Environmental Studies, University of Chittagong, Chittagong, 4331 Bangladesh
| | - Mohammad Atiqur Rahman
- Department of Geography and Environmental Studies, University of Chittagong, Chittagong, 4331 Bangladesh
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Jahanbakhsh M, Ashrafi-Rizi H, Jangi M, Sattari M. Big data in COVID-19 surveillance system: A commentary. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2020; 9:329. [PMID: 33426133 PMCID: PMC7774614 DOI: 10.4103/jehp.jehp_303_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/09/2020] [Indexed: 06/12/2023]
Affiliation(s)
- Maryam Jahanbakhsh
- Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hasan Ashrafi-Rizi
- Medical Library and Information Science, Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Majid Jangi
- Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Sattari
- Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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Wei JT, Liu YX, Zhu YC, Qian J, Ye RZ, Li CY, Ji XK, Li HK, Qi C, Wang Y, Yang F, Zhou YH, Yan R, Cui XM, Liu YL, Jia N, Li SX, Li XJ, Xue FZ, Zhao L, Cao WC. Impacts of transportation and meteorological factors on the transmission of COVID-19. Int J Hyg Environ Health 2020; 230:113610. [PMID: 32896785 PMCID: PMC7448770 DOI: 10.1016/j.ijheh.2020.113610] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/03/2020] [Accepted: 08/21/2020] [Indexed: 12/20/2022]
Abstract
The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease.
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Affiliation(s)
- Jia-Te Wei
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Yun-Xia Liu
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Yu-Chen Zhu
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Jie Qian
- School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Run-Ze Ye
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Chun-Yu Li
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Xiao-Kang Ji
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Hong-Kai Li
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Chang Qi
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Ying Wang
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Fan Yang
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Yu-Hao Zhou
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, PR China
| | - Ran Yan
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Xiao-Ming Cui
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, PR China
| | - Yuan-Li Liu
- School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Na Jia
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, PR China
| | - Shi-Xue Li
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China; Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Xiu-Jun Li
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China; Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China
| | - Fu-Zhong Xue
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China; Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China.
| | - Lin Zhao
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China.
| | - Wu-Chun Cao
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, PR China; State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, PR China.
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Zhang Y, Ye C, Yu J, Zhu W, Wang Y, Li Z, Xu Z, Cheng J, Wang N, Hao L, Hu W. The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 701:134607. [PMID: 31710904 PMCID: PMC7112088 DOI: 10.1016/j.scitotenv.2019.134607] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 09/12/2019] [Accepted: 09/21/2019] [Indexed: 05/04/2023]
Abstract
Most previous studies focused on the association between climate variables and seasonal influenza activity in tropical or temperate zones, little is known about the associations in different influenza types in subtropical China. The study aimed to explore the associations of multiple climate variables with influenza A (Flu-A) and B virus (Flu-B) transmissions in Shanghai, China. Weekly influenza virus and climate data (mean temperature (MeanT), diurnal temperature range (DTR), relative humidity (RH) and wind velocity (Wv)) were collected between June 2012 and December 2018. Generalized linear models (GLMs), distributed lag non-linear models (DLNMs) and regression tree models were developed to assess such associations. MeanT exerted the peaking risk of Flu-A at 1.4 °C (2-weeks' cumulative relative risk (RR): 14.88, 95% confidence interval (CI): 8.67-23.31) and 25.8 °C (RR: 12.21, 95%CI: 6.64-19.83), Flu-B had the peak at 1.4 °C (RR: 26.44, 95%CI: 11.52-51.86). The highest RR of Flu-A was 23.05 (95%CI: 5.12-88.45) at DTR of 15.8 °C, that of Flu-B was 38.25 (95%CI: 15.82-87.61) at 3.2 °C. RH of 51.5% had the highest RR of Flu-A (9.98, 95%CI: 4.03-26.28) and Flu-B (4.63, 95%CI: 1.95-11.27). Wv of 3.5 m/s exerted the peaking RR of Flu-A (7.48, 95%CI: 2.73-30.04) and Flu-B (7.87, 95%CI: 5.53-11.91). DTR ≥ 12 °C and MeanT <22 °C were the key drivers for Flu-A and Flu-B, separately. The study found complex non-linear relationships between climate variability and different influenza types in Shanghai. We suggest the careful use of meteorological variables in influenza prediction in subtropical regions, considering such complex associations, which may facilitate government and health authorities to better minimize the impacts of seasonal influenza.
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Affiliation(s)
- Yuzhou Zhang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Chuchu Ye
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China
| | - Jianxing Yu
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Weiping Zhu
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China
| | - Yuanping Wang
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China
| | - Zhongjie Li
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhiwei Xu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Jian Cheng
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Ning Wang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Lipeng Hao
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China.
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
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Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals. J Med Syst 2018; 42:228. [DOI: 10.1007/s10916-018-1093-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 10/02/2018] [Indexed: 10/28/2022]
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da Costa ACC, Codeço CT, Krainski ET, Gomes MFDC, Nobre AA. Spatiotemporal diffusion of influenza A (H1N1): Starting point and risk factors. PLoS One 2018; 13:e0202832. [PMID: 30180215 PMCID: PMC6122785 DOI: 10.1371/journal.pone.0202832] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 08/09/2018] [Indexed: 01/27/2023] Open
Abstract
Influenza constitutes a major challenge to world health authorities due to high transmissibility and the capacity to generate large epidemics. This study aimed to characterize the diffusion process of influenza A (H1N1) by identifying the starting point of the epidemic as well as climatic and sociodemographic factors associated with the occurrence and intensity of transmission of the disease. The study was carried out in the Brazilian state of Paraná, where H1N1 caused the largest impact. The units of spatial and temporal analysis were the municipality of residence of the cases and the epidemiological weeks of the year 2009, respectively. Under the Bayesian paradigm, parametric inference was performed through a two-part spatiotemporal model and the integrated nested Laplace approximation (INLA) algorithm. We identified the most likely starting points through the effective distance measure based on mobility networks. The proposed estimation methodology allowed for rapid and efficient implementation of the spatiotemporal model, and provided evidence of different patterns for chance of occurrence and risk of influenza throughout the epidemiological weeks. The results indicate the capital city of Curitiba as the probable starting point, and showed that the interventions that focus on municipalities with greater migration and density of people, especially those with higher Human Development Indexes (HDIs) and the presence of municipal air and road transport, could play an important role in mitigation of effects of future influenza pandemics on public health. These results provide important information on the process of introduction and spread of influenza, and could contribute to the identification of priority areas for surveillance as well as establishment of strategic measures for disease prevention and control. The proposed model also allows identification of epidemiological weeks with high chance of influenza occurrence, which can be used as a reference criterion for creating an immunization campaign schedule.
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Affiliation(s)
- Ana Carolina Carioca da Costa
- National Institute of Women, Children and Adolescents Health Fernandes Figueira, Department of Clinical Research, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
- * E-mail:
| | | | - Elias Teixeira Krainski
- Federal University of Paraná, Paraná, Brazil
- Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Aline Araújo Nobre
- Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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Thota C, Sundarasekar R, Manogaran G, R V, M. K. P. Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System. EXPLORING THE CONVERGENCE OF BIG DATA AND THE INTERNET OF THINGS 2018. [DOI: 10.4018/978-1-5225-2947-7.ch011] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients' medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.
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Manogaran G, Thota C, Lopez D. Human-Computer Interaction With Big Data Analytics. ADVANCES IN HUMAN AND SOCIAL ASPECTS OF TECHNOLOGY 2018. [DOI: 10.4018/978-1-5225-2863-0.ch001] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.
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Abstract
Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.
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12
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Rao H, Shi X, Zhang X. Using the Kulldorff's scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009-2016. BMC Infect Dis 2017; 17:578. [PMID: 28826399 PMCID: PMC5563899 DOI: 10.1186/s12879-017-2643-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 07/26/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Although the incidence of tuberculosis (TB) in most parts of China are well under control now, in less developed areas such as Qinghai, TB still remains a major public health problem. This study aims to reveal the spatio-temporal patterns of TB in the Qinghai province, which could be helpful in the planning and implementing key preventative measures. METHODS We extracted data of reported TB cases in the Qinghai province from the China Information System for Disease Control and Prevention (CISDCP) during January 2009 to December 2016. The Kulldorff's retrospective space-time scan statistics, calculated by using the discrete Poisson probability model, was used to identify the temporal, spatial, and spatio-temporal clusters of TB at the county level in Qinghai. RESULTS A total of 48,274 TB cases were reported from 2009 to 2016 in Qinghai. Results of the Kulldorff's scan revealed that the TB cases in Qinghai were significantly clustered in spatial, temporal, and spatio-temporal distribution. The most likely spatio-temporal cluster (LLR = 2547.64, RR = 4.21, P < 0.001) was mainly concentrated in the southwest of Qinghai, covering seven counties and clustered in the time frame from September 2014 to December 2016. CONCLUSION This study identified eight significant space-time clusters of TB in Qinghai from 2009 to 2016, which could be helpful in prioritizing resource assignment in high-risk areas for TB control and elimination in the future.
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Affiliation(s)
- Huaxiang Rao
- Institute for Communicable Disease Control and Prevention, Qinghai Center for Disease Control and Prevention, No.55 Bayi middle Road, Xining, Qinghai, 810007, China.
| | - Xinyu Shi
- Operational Department, The Second Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, 030001, China
| | - Xi Zhang
- Clinical Research Center, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
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Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. MULTIMEDIA TOOLS AND APPLICATIONS 2017. [DOI: 10.1007/s11042-017-4768-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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14
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Manogaran G, Lopez D. Disease Surveillance System for Big Climate Data Processing and Dengue Transmission. ACTA ACUST UNITED AC 2017. [DOI: 10.4018/ijaci.2017040106] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.
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Affiliation(s)
| | - Daphne Lopez
- School of Information Technology and Engineering, VIT University, Vellore, India
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Big Data Security Intelligence for Healthcare Industry 4.0. SPRINGER SERIES IN ADVANCED MANUFACTURING 2017. [DOI: 10.1007/978-3-319-50660-9_5] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Thota C, Manogaran G, Lopez D, Vijayakumar V.. Big Data Security Framework for Distributed Cloud Data Centers. CYBERSECURITY BREACHES AND ISSUES SURROUNDING ONLINE THREAT PROTECTION 2017. [DOI: 10.4018/978-1-5225-1941-6.ch012] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.
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Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2015. [DOI: 10.1007/978-3-319-27212-2_16] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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