Analysis of Death Risk of COVID-19 under Incomplete Information

Abstract

It is easy to write a story about the Coronavirus Disease 2019 (COVID-19) when everything about COVID-19 is known. It is difficult to analyze the death risks of COVID-19 with a few evidences collected before and at the beginning of the outbreak. In this paper, we suggest a hybrid model to analyze the death risk under incomplete information. The hybrid model would be supported by the internet of intelligences, being a platform interacting with infectious disease specialists and local doctors who fuse the evidences with the experience of the known infectious diseases and provide a series of judgments related to the death risk of a human population in a given period to COVID-19. The hybrid model consists of two models of set-valued statistics and a formula. The set-valued statistics integrate the judgments for constructing (1) a probability distribution of the percent of patients, as the exposure of the population, and (2) a mortality curve with respect to the percent, as the vulnerability of the population. The suggested formula calculates the expected value of death toll. We give a virtual case to show how to use the hybrid model.