https://socictopen.socict.org/files/original/acdf6eac6e8389c3ab10e90e8a929152.pdf e152e3314682dd081732425ceb7316ab 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 COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population Creator An entity primarily responsible for making the resource Sotiris Kotsiantis, Vasilis Papastefanopoulos, Pantelis Linardatos Description An account of the resource The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource machine learning, statistics, time series, coronavirus, Pandemic, COVID-19 Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/app10113880 Source A related resource from which the described resource is derived Applied Sciences 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 Biology (General), Technology, Physics, Chemistry, Engineering (General). Civil engineering (General)