https://www.socictopen.socict.org/files/original/5c83dd12e02623199d34aedb72363652.pdf deeeecd76bd1df964d36ef6f26d37f00 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 the Spread of COVID-19 in Lebanon: A Bayesian Perspective Creator An entity primarily responsible for making the resource Samer A. Kharroubi Description An account of the resource This article investigates the problem of modeling the trend of the current Coronavirus disease 2019 pandemic in Lebanon along time. Two different models were developed using Bayesian Markov chain Monte Carlo simulation methods. The models fitted included Poisson autoregressive as a function of a short-term dependence only and Poisson autoregressive as a function of both a short-term dependence and a long-term dependence. The two models are compared in terms of their predictive ability using mean predictions, root mean squared error, and deviance information criterion. The Poisson autoregressive model that allows capturing both short-term and long-term components performs best under all criterions. The use of such a model can greatly improve the estimation of number of new infections, and can indicate whether disease has an upward/downward trend, and where about every country is on that trend, so that containment measures can be applied and/or relaxed. The Bayesian model is flexible in characterizing the uncertainty in the model outputs. The model is also applicable to other countries and more time periods as data becomes available. Further research is encouraged. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource covid-19, prediction, statistical modeling, Bayesian statistic, Poisson autoregressive model Identifier An unambiguous reference to the resource within a given context 10.3389/fams.2020.00040 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Applied mathematics. Quantitative methods, Probabilities. Mathematical statistics