Analysis of Death Risk of COVID-19 under Incomplete Information1

DOI

10.2991/jracr.k.200709.002How to use a DOI?

Keywords

Coronavirus; death risk; evidence; experience; internet of intelligences; set-valued statistics

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.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Chongfu Huang
PY  - 2020
DA  - 2020/07/15
TI  - Analysis of Death Risk of COVID-19 under Incomplete Information1
JO  - Journal of Risk Analysis and Crisis Response
SP  - 43
EP  - 53
VL  - 10
IS  - 2
SN  - 2210-8505
UR  - https://doi.org/10.2991/jracr.k.200709.002
DO  - 10.2991/jracr.k.200709.002
ID  - Huang2020
ER  -