https://www.socictopen.socict.org/files/original/84e7f3a01200d274c1f3238921657f25.pdf a8e0475bf3f8d0be914418484b09fb8a 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 Effectiveness of the Early Response to COVID-19: Data Analysis and Modelling Creator An entity primarily responsible for making the resource Edoardo Bertone, Oz Sahin, Khoi Nguyen, Martin Jason Luna Juncal, Rafaela Keiko Prado Umeno, Douglas Alves Peixoto Description An account of the resource Governments around the world have introduced a number of stringent policies to try to contain COVID-19 outbreaks, but the relative importance of such measures, in comparison to the community response to these restrictions, the amount of testing conducted, and the interconnections between them, is not well understood yet. In this study, data were collected from numerous online sources, pre-processed and analysed, and a number of Bayesian Network models were developed, in an attempt to unpack such complexity. Results show that early, high-volume testing was the most crucial factor in successfully monitoring and controlling the outbreaks; when testing was low, early government and community responses were found to be both critical in predicting how rapidly cases and deaths grew in the first weeks of the outbreak. Results also highlight that in countries with low early test numbers, the undiagnosed cases could have been up to five times higher than the officially diagnosed cases. The conducted analysis and developed models can be refined in the future with more data and variables, to understand/model potential second waves of contagions. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Pandemic, bayesian networks, System Thinking, COVID-19 Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/systems8020021 Source A related resource from which the described resource is derived Systems Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Technology (General), Systems engineering