In September 2014 the rapid evolving situation of the Ebola Outbreak in West Africa raised the need of continuous updates of the models as more and more data became available from the affected region.The access to this data allowed us to recalibrate our model, improving the projections and analysis. After we published the first article, we created a website (http://www.mobs-lab.org/ebola.html) where we have been continuously updating our initial results.
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The accurate characterization of the structure of social contacts in mathematical and computational models of infectious disease transmission is a key element in the assessment of the impact of epidemic outbreaks and in the evaluation of effective control measures. For instance, the transmissibility potential of a disease and the final epidemic size strongly depend on mixing patterns between individuals of the population, which in turn depend on socio-demographic parameters (e.g. household size, fraction of workers and students in the population). Empirical data collection on a large scale is however extremely difficult and although several models tackling both new emerging epidemics and endemic diseases have introduced a significant amount of information on contact patterns, it is clear that the increasing use of data-driven models in the support of public health decisions is calling for novel approaches to the estimation of mixing patterns in human populations.
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