Open AccessADAPTIVE NEURO FUZZY ESTIMATION OF THE OPTIMAL COVID-19 PREDICTORS FOR GLOBAL TOURISM
Author(s) -
Boris Kuzman,
Biljana Petković
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.52370/tisc2194bk
Subject(s) - adaptive neuro fuzzy inference system , pandemic , population , covid-19 , tourism , infection rate , mortality rate , statistics , demography , computer science , index (typography) , geography , medicine , fuzzy logic , artificial intelligence , mathematics , fuzzy control system , environmental health , infectious disease (medical specialty) , disease , surgery , archaeology , sociology , world wide web
COVID-19 is a pandemic that has emerged as a result of 2019-novel coronavirus droplet infection (2019-nCoV). Recognition of its risk and prognostic factor is critical due to its rapid dissemination and high casefatality rate. Tourism industry as one of the greatest industries has suffered a lot in the pandemic situation. The main aim of the study was to present travelers’ reaction during the pandemic by data mining methodology. The effect of eleven predictors for COVID-19 was also analyzed. The used predictors are: population density, urban population percentage, number of hospital beds, female and male lung size, median age, crime index, population number, smoking index and percentage of females. As the output factors, infection rate, death rate and recovery rate were used. The analyzing procedure was performed by adaptive neuro fuzzy inference system (ANFIS). The results revealed that the frequency of the used words in the pandemic show the highest impact on the travelers’ reactions. Number of hospital beds and population number is the optimal combination for the best prediction of infection rate of COVID-19.