https://www.socictopen.socict.org/files/original/ff617d32758891f8a0da4796df30ef8f.pdf c86abdb7b4c5332b1611ee5aa8fb4e8e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Modeling Social Groups, Policies and Cognitive Behavior in COVID-19 Epidemic Phases. Basic Scenarios Creator An entity primarily responsible for making the resource Franco Bagnoli, Pietro Liò, Daniele Lorini Description An account of the resource The Covid-19 pandemic is distinct from Spanish flu of 1918 from many aspects among which the contrast between the overabundance of worldwide exchange of information (infomedia) and the actual scarce knowledge of the pathogen and the infection mechanism. Another important distinction is that the epidemics threaten society components, social groups, communities and jobs in very different ways and different death tolls. With this in mind, we start with simple models of pandemics and we drive the reader to more complex models that take into accounts social compartments and communities. The discrete-state models are built by adding elements, first in a mean-field approximation, then adding age classes and differential contact rates, and finally inserting the social group dimension. The novel element we insert is the effect of restrictions in contacts and travels, filtered by the risk perception, according with the growth of the number of infected or recovered people. Assimilating risk perception with cognitive behavior, we obtain several coarse-grain scenarios, that can be used for instance to calibrate the level of restrictions so not to exceed the capacity of the health system, and to speed the post-emergency recovery. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Risk perception, infection dynamics, agent-based models, Epidemic modelling Identifier An unambiguous reference to the resource within a given context DOI: 10.13128/Substantia-914 Source A related resource from which the described resource is derived Substantia Publisher An entity responsible for making the resource available Firenze University Press Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chemistry, History (General) and history of Europe