RESUMO
Objective: The design and implementation of an online dashboard to support data-driven decision-making and joint coordination between health institutions and government bodies in the Balkan countries faced with new COVID-19 waves in the region. Methods: Shiny R dashboard tracks COVID-19 in real-time using a comparative approach to interactively visualize national-level data from various official sources. Results: The dashboard, named COVID-19 Situation in Balkan countries can be accessed online (COVID-19 Situation in Balkan countries). The daily situation in 11 Balkan countries focuses on similarities and differences between countries on a daily basis and since the beginning of the pandemic. The web resource features the most affected countries, the number of new cases, and fatality rates reported daily. Features also include rankings of the worst affected countries, information search and filtering, and a map component interactively showing daily information for each country comparatively. Conclusions: The dashboard for the COVID-19 situation in Balkan countries simplifies meaningful real-time information for public and health agencies regarding the COVID-19 situation in the whole Balkan region. The creation allows for a deep analysis of measures taken to face COVID-19 in a regional context, allowing for health policy updates and a better basis for collaboration among Balkan governments.
RESUMO
In this paper, we first introduce a new type of rough sets called α-upward fuzzified preference rodownward fuzzy preferenceugh sets using upward fuzy preference relation. Thereafter on the basis of α-upward fuzzified preference rough sets, we propose approximate precision, rough degree, approximate quality and their mutual relationships. Furthermore, we presented the idea of new types of fuzzy upward ß-coverings, fuzzy upward ß-neighborhoods and fuzzy upward complement ß-neighborhoods and some relavent properties are discussed. Hereby, we formulate a new type of upward lower and upward upper approximations by applying an upward ß-neighborhoods. After employing the upward ß-neighborhoods based upward rough set approach to it any times, we can only get the six different sets at most. That is to say, every rough set in a universe can be approximated by only six sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Subsequently, we presented the idea to combine the fuzzy implicator and t-norm to introduce multigranulation (â,T)-fuzzy upward rough set applying fuzzy upward ß-covering and some relative properties are discussed. Finally we presented a new technique for the selection of medicine for treatment of coronavirus disease (COVID-19) using multigranulation (â,T)-fuzzy upward rough sets.
