Situation awareness in crowdsensing for disease surveillance in crisis situations

@article{Haddawy2015SituationAI,
  title={Situation awareness in crowdsensing for disease surveillance in crisis situations},
  author={Peter Haddawy and Lutz Frommberger and Tomi Kauppinen and Giorgio De Felice and Prae Charkratpahu and Sirawaratt Saengpao and Phanumas Kanchanakitsakul},
  journal={Proceedings of the Seventh International Conference on Information and Communication Technologies and Development},
  year={2015}
}
Crowdsensing can provide real time and detailed information about rapidly evolving crisis situations to facilitate rapid response and effective resource allocation. But while challenges such as heterogeneity of data content and quality, asynchronicity, and volume call for robust data integration and interpretation capabilities, situation awareness in crowdsensing for crisis management remains a largely unexplored area of research. In this paper we extend the mobile4D smartphone-based disaster… 

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References

SHOWING 1-10 OF 19 REFERENCES
Crowd-Sensing Meets Situation Awareness: A Research Roadmap for Crisis Management
TLDR
A systematic catalog of evaluation criteria is derived and used for an in-depth comparison of nine existing SAW systems, thereby highlighting current capabilities and directions for further research.
Mobile4D: crowdsourced disaster alerting and reporting
TLDR
This work presents Mobile4D, an integrated mobile crowdsourcing-based disaster alerting and reporting system tested in Lao PDR, which is possible to gather information from affected people, to establish direct communication channels between affected people and administrative units, and to rapidly distribute information to regions and people struck by disasters.
An integrated approach for fusion of environmental and human health data for disease surveillance
TLDR
The problem of public health monitoring for waterborne disease outbreaks using disparate evidence from health surveillance data streams and environmental sensors is described and a solution framework using Bayesian Networks (BN) is presented.
Methods Paper: Bayesian Information Fusion Networks for Biosurveillance Applications
TLDR
New information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities are introduced and results showed increased specificity compared with the alerts generated by temporal anomaly detection algorithms currently deployed by NCR health departments.
Situational Awareness from Social Media
TLDR
VIStology’s HADRian system for semantically integrating disparate information sources into a common operational picture (COP) for humanitarian assistance/disaster relief (HADR) operations is applied to the task of determining where unexploded or additional bombs were being reported via Twitter in the hours immediately after the Boston Marathon bombing.
Bayesian Biosurveillance of Disease Outbreaks
TLDR
An investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people is reported.
Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance
TLDR
Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore, and represent valuable complement to assist with traditional d Dengue surveillance.
Predicting Disease Transmission from Geo-Tagged Micro-Blog Data
TLDR
A probabilistic model is constructed that can predict if and when an individual will fall ill with high precision and good recall on the basis of his social ties and co-locations with other people, as revealed by their Twitter posts.
Using Social Media to Enhance Emergency Situation Awareness
The described system uses natural language processing and data mining techniques to extract situation awareness information from Twitter messages generated during various disasters and crises.
Cholera outbreak--southern Sudan, 2007.
TLDR
This report summarizes the results of that investigation, which found that 3,157 persons were diagnosed with suspected cholera during January--June 2007, with 74 deaths resulting from the disease, and an environmental investigation revealed suboptimal hygiene practices and a lack of water and sanitation infrastructure in Juba.
...
1
2
...