Akademik Veri Yönetim Sistemi
Türkiye Klinikleri Biyoistatistik Dergisi, cilt.12, sa.2, ss.127-139, 2020 (Hakemli Dergi)
Objective:As we have been living through COVID-19 pandemic for more than 5 months with its all detrimental im-pacts on our economic, social, and individual lives, developing models that will accurately inform us about the possible ending date of the pandemic locallyor globally becomes ever more critical. In this study, we provide a data-driven model projecting the end-date of a given pandemic, specifically COVID-19.Material and Meth-ods:To predict the end date of a given pandemic for early-phase and mature pandemicprofiles, we propose a logistic-mixture mod-elling framework utilizing only the dates and number of infections (i.e., cases), where the level of mixing is determined in a data-driven way with one, two, three or four peaks. We assess the pro-jection accuracythrough model convergence and goodness of fit measures for countries that have controlled the pandemic. Results:We have shown that our logistic-mixture modelling approach has very favourable convergence and goodness of fit properties, espe-cially when thenumber of local and global peaks and their timings are provided to the model carefully. Based on the projections of our model, using the available data as of June 01, 2020, the COVID-19 pandemic is ending in early September in Turkey, in early October in the United States of America, and not before December 2020 for the entire world. Conclusion:A mixture-logistic modelling frame-work is a flexible modelling strategy to capture multiple pandemic peaks and, therefore, a reasonable projection approach.