ABSTRACT
A novel method for assessing the effectiveness of enrichment evaluations PROMETHEE combining pentagonal intuitionistic fuzzy numbers (PIFNs) and preference rank-ing organization is presented in the present paper. PIFN suggests a new technique for multi -criteria group decision making (MCGDM) in which two characteristic values of membership and non-membership functions are involved. The key practicality of incorporating PIFN in decision -making is its effective capability of managing the vagueness and uncertainties of linguistic terms used during discussions. The designed algorithm is then applied to get an appropriate, cost-effective, and publicly accepted awareness campaign to be used to forewarn populaces about any virulent disease, which has not been studied before. Importantly, it is the only way to protect any huge population of a country from any fatal disease, i.e. to be timely aware of the disease's transmissibility, severity, and precautionary measures through any effectively ap-proachable source. Here, we consider alternative sources of campaigns, such as commercial advertisement on television, on social media, on bills /other government circulars, billboards, and door-to-door volunteering for guidance. These alternative campaigns are based on five generalized criteria, where the weight of each criterion is evaluated via the fuzzy analytical hier-archy process (F-AHP). After using the F-AHP for complex decisions based on acceptance and effectiveness, the F-PROMETHEE algorithm is applied to achieve the closest ideal alternative.
ABSTRACT
Background: The outbreak of coronavirus 2019 (COVID-19) which emerged in December 2019 spread rapidly and created a public health emergency. Geospatial records of case data are needed in real time to monitor and anticipate the spread of infection. Methods: This study aimed to identify the emerging hotspots of COVID-19 using a geographic information system (GIS)-based approach. Data of laboratory-confirmed COVID-19 patients from March 15 to June 12, 2020, who visited the emergency department of a tertiary specialized academic hospital in Dubai were evaluated using ArcGIS Pro 2.5. Spatiotemporal analysis, including optimized hotspot analysis, was performed at the community level. Results: The cases were spatially concentrated mostly over the inner city of Dubai. Moreover, the optimized hotspot analysis showed statistically significant hotspots (p < 0.01) in the north of Dubai. Waxing and waning hotspots were also observed in the southern and central regions of Dubai. Finally, there were nonsustaining hotspots in communities with a very low population density. Conclusion: This study identified hotspots of COVID-19 using geospatial analysis. It is simple and can be easily reproduced to identify disease outbreaks. In the future, more attention is needed in creating a wider geodatabase and identifying hotspots with more intense transmission intensity.