Predict recovery risk rate of Covid-19

Abstract

The outbreak of the 2019 novel coronavirus (2019-nCoV) in Wuhan, China has become expanded globally, the current novel coronavirus (2019-nCoV) or also called COVID-19 pandemic is unprecedented, but the global response draws on the lessons learned from other disease outbreaks over the past several decades. WHO (World Health Organisation) said that Person-to-person transmission may occur through droplet or contact transmission and according to early estimates by China’s National Commission (NHC) people with high risk for severe disease and death people is an old age and people with pre-existing health conditios (comorbid) as hyertention, diabetes, cardiovasculer disease, chronic respiratory diseas (asthma, lung etc), heart disease, kidney disease, liver disease, immunodeficiency, cancer and other. The fatality cases about 80% were over the age of 60 and 75% of them had pre-existing health conditios (comorbid). The government prevent further spread of novel coronavirus (2019-nCoV) disease with advice for public on how to keep healthy. In this article we consider a predict recovery risk rate of COVID-19 using fuzzy tsukamoto inference system, representation linguistic variable using linear membership function up and down, use two input, age and comorbid with “IF-THEN” rules, fuzzy logic conjunction connectives. The defuzzification or output mortality rate using centroid triangular fuzzy number.