scholarly journals Infectious or Recovered? Optimizing the Infectious Disease Detection Process for Epidemic Control and Prevention Based on Social Media

2020 ◽  
Vol 17 (18) ◽  
pp. 6853
Author(s):  
Siqing Shan ◽  
Qi Yan ◽  
Yigang Wei
Keyword(s):  
Social Media ◽  
Disease Surveillance ◽  
Infectious Period ◽  
Disease Detection ◽  
Epidemic Control ◽  
Detection Process ◽  
Word Level ◽  
Building Information ◽  
Control And Prevention ◽  
Building Information Management

Detecting the period of a disease is of great importance to building information management capacity in disease control and prevention. This paper aims to optimize the disease surveillance process by further identifying the infectious or recovered period of flu cases through social media. Specifically, this paper explores the potential of using public sentiment to detect flu periods at word level. At text level, we constructed a deep learning method to classify the flu period and improve the classification result with sentiment polarity. Three important findings are revealed. Firstly, bloggers in different periods express significantly different sentiments. Blogger sentiments in the recovered period are more positive than in the infectious period when measured by the interclass distance. Secondly, the optimized disease detection process can substantially improve the classification accuracy of flu periods from 0.876 to 0.926. Thirdly, our experimental results confirm that sentiment classification plays a crucial role in accuracy improvement. Precise identification of disease periods enhances the channels for the disease surveillance processes. Therefore, a disease outbreak can be predicted credibly when a larger population is monitored. The research method proposed in our work also provides decision making reference for proactive and effective epidemic control and prevention in real time.

scholarly journals Suicide rates in Zhejiang Province, China, from 2006 to 2016: a population-based study

2019 ◽  
Vol 73 (8) ◽  
pp. 745-749 ◽  
Author(s):  
Fangrong Fei ◽  
Huixin Liu ◽  
Sequoia I Leuba ◽  
Yichong Li ◽  
Ruying Hu ◽  
...  
Keyword(s):  
Suicide Rate ◽  
Disease Surveillance ◽  
Temporal Trends ◽  
Population Based ◽  
Suicide Rates ◽  
Urban Males ◽  
Control And Prevention ◽  
And Gender ◽  
Zhejiang Provincial ◽  
Gender Specific

BackgroundWe investigated the current temporal trends of suicide in Zhejiang, China, from 2006 to 2016 to determine possible health disparities in order to establish priorities for intervention.MethodsWe collected mortality surveillance data from 2006 to 2016 from the Zhejiang Chronic Disease Surveillance Information and Management System from the Zhejiang Provincial Centre for Disease Control and Prevention. We estimated region-specific and gender-specific suicide rates using joinpoint regression analyses to determine the average annual percentage change (AAPC) and its 95% CI.ResultsThe crude suicide rate declined from 9.64 per 100 000 people in 2006 to 4.86 per 100 000 in 2016, and the age-adjusted suicide rate decreased from 9.74 per 100 000 in 2006 to 4.14 per 100 000 in 2016. During 2006–2013, rural males had the highest suicide rate, followed by rural females, urban males, and urban females, while after 2013, urban males suicide rates surpassed rural female suicide rates, and became the second highest suicide rate subgroup. The rate of suicide declined in all region-specific and/or gender-specific subgroups except among urban males between 20 and 34 years of age. Their age-adjusted suicide rate AAPC greatly increased to 28.39 starting in 2013 compared with an AAPC of −13.47 from 2006 to 2013.ConclusionsThe suicide rate among young urban males has been alarmingly increasing since 2013, and thus, researchers must develop targeted effective strategies to mitigate this escalating loss of life.

Africa Centres for Disease Control and Prevention Is Closing Gaps in Disease Detection

2020 ◽  
Vol 18 (6) ◽  
pp. 483-488
Author(s):  
Jay Varma ◽  
Justin Maeda ◽  
Mgaywa G.M.D. Magafu ◽  
Philip C. Onyebujoh
Keyword(s):  
Disease Control ◽  
Disease Detection ◽  
Control And Prevention

Implementation of Power Law Network Models of Epidemic Surveillance Data for Better Evaluation of Outbreak Detection Alarms

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Razvan G. Romanescu ◽  
Rob Deardon
Keyword(s):  
Power Law ◽  
Disease Surveillance ◽  
Incidence Rates ◽  
Rate Of Change ◽  
Outbreak Detection ◽  
Disease Spread ◽  
Infectious Period ◽  
Contact Network ◽  
Human Populations ◽  
Susceptible Population

Abstract Properties of statistical alarms have been well studied for simple disease surveillance models, such as normally distributed incidence rates with a sudden or gradual shift in mean at the start of an outbreak. It is known, however, that outbreak dynamics in human populations depend significantly on the heterogeneity of the underlying contact network. The rate of change in incidence for a disease such as influenza peaks early on during the outbreak, when the most highly connected individuals get infected, and declines as the average number of connections in the remaining susceptible population drops. Alarm systems currently in use for detecting the start of influenza seasons generally ignore this mechanism of disease spread, and, as a result, will miss out on some early warning signals. We investigate the performance of various alarms on epidemics simulated from an undirected network model with a power law degree distribution for a pathogen with a relatively short infectious period. We propose simple custom alarms for the disease system considered, and show that they can detect a change in the process sooner than some traditional alarms. Finally, we test our methods on observed rates of influenza-like illness from two sentinel providers (one French, one Spanish) to illustrate their use in the early detection of the flu season.

Governance Model for Cloud Computing in Building Information Management

2015 ◽  
Vol 8 (2) ◽  
pp. 314-327 ◽  
Author(s):  
Thomas Beach ◽  
Omer Rana ◽  
Yacine Rezgui ◽  
Manish Parashar
Keyword(s):  
Cloud Computing ◽  
Information Management ◽  
Governance Model ◽  
Building Information ◽  
Building Information Management

scholarly journals The role of managing knowledge and information in BIM implementation processes in the Czech Republic

2018 ◽  
Vol 146 ◽  
pp. 01003 ◽  
Author(s):  
Vladimír Nývlt
Keyword(s):  
Czech Republic ◽  
Construction Industry ◽  
Success Factors ◽  
The Czech Republic ◽  
Implementation Processes ◽  
Critical Success ◽  
3D Data ◽  
Building Information ◽  
Building Information Management

Czech construction sector is at the initial stages of implementing and assessing Building Information Management (BIM) on pilot projects. Object modeling developed over the last 20 years is seemed as a stable ground for many professionals. 3D data models are basis for further concepts associated with BIM, helping to support Co-ordinated Project Information (CPI) and Integrated Project Delivery (IPD). This paper presents the role of managing knowledge, information and critical success factors (CSF) associated with BIM implementation within the construction industry in the Czech Republic. Determining the CSF in the context of BIM maturity levels should support BIM implementation processes within the construction industry and also within the associated bodies (education, government, technology).

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