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We investigate the spreading of information through Twitter messaging relatedto the spread of Ebola in western Africa using epidemic based dynamic models.Diffusive spreading leads to NLPDE models and fixed point analysis yieldssystems of NLODE models. When tweets are mapped as connected nodes in a graphand are treated as a time sequenced Markov chain, TSMC, then by the Kurtztheorem these specific paths can be identified as being near solutions tosystems of ordinary differential equations that in the large N limit retainmany of the features of the original Tweet dynamics. Constraints on the modelrelated to Tweet and re-Tweet rates lead to different versions of the system ofequations. We use Ebola Twitter meme based data to investigate a modified fourparameter model and apply the resulting fit to an accuracy metric for a set ofEbola memes. In principle the temporal and spatial evolution equationsdescribing the propagation of the Twitter based memes can help ascertain andinform decision makers on the nature of the spreading and containment of anepidemic of this type.

Sample NLPDE and NLODE Social-Media Modeling of Information Transmission for Infectious Diseases:Case Study Ebola