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|---|
| rdfs:label
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| dbkwik:resource/57jW2McNGlE9pqjtgT9qcQ==
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| dbkwik:resource/QnNqLb9ZSWSpQX201CKLLA==
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| dbkwik:resource/QseLpQRy423bxxL2KmhCog==
| - [In this] system authors compared results of low alert states to known influenza epidemics and to data containing emergency room visits, pharmacy purchases and absenteeism. Although the peak incidence of the simulated outbreak is larger than the peak incidence seen in the population data, the simulation results are temporally similar to those seen in the population data. They hoped that this simulation framework will allow them to ask ‘what-if’ questions regarding appropriate response and detection strategies for both natural and man-made epidemics. This is a city scale multi-agent model of weaponized bioterrorist attacks for intelligence and training. At present the model is running with 100,000 agents . All agents have real social networks and the model contains real city data -hospitals, schools etc. Agents are as realistic as possible and contain a cognitive model.
* DYNET—Dynamic Networks. The team is building a model of how networks adapt, evolve and change in response to various types of attacks e.g. infowar or assassination.
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| dbkwik:resource/cOQ63AVwiZYf3UUTa8fbjA==
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| dbkwik:resource/eDhPLpw7o8B5wk3WJ-2kTA==
| |
| dbkwik:resource/p9TOJUtbeeejfnk5AAQHUg==
| - [page 105]
We have compared results of low alert states to known influenza epidemics and to data containing emergency room visits, pharmacy purchases and absenteeism. Although the peak incidence of the simulated outbreak is larger than the peak incidence seen in the population data, the simulation results are temporally similar to those seen in the population data. [...]. It is hoped that this simulation framework will allow us to ask ‘what-if’ questions regarding appropriate response and detection strategies for both natural and man-made epidemics.
[page 18]
this is a cityscale multi-agent model of weaponized bioterrorist attacks for intelligence and training. At present the model is running with 100,000 agents . All agents have real social networks and the model contains real city data - hospitals, schools etc. Agents are as realistic as possible and contain a cognitive model.
[...]
DYNET – Dynamic Networks. The team is building a model of how networks adapt, evolve and change in response to various types of attacks e.g. infowar or assassination
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| Quelle
| |
| dbkwik:resource/XhOMK2reAaqeAKQ0pQhkhA==
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![[RDF Data]](/fct/images/sw-rdf-blue.png)