tetano
Editor, Senior Moderator
Risk Anal
. 2022 Dec 4.
doi: 10.1111/risa.14033. Online ahead of print.
Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques
Mehrab Tanhaeean[SUP] 1 [/SUP], Negin Nazari[SUP] 1 2 [/SUP], Seyed Hosein Iranmanesh[SUP] 1 [/SUP], Majid Abdollahzade[SUP] 3 [/SUP]
Affiliations
Abstract
Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best-Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best-Worst Method-local linear neuro-fuzzy-adaptive neuro-fuzzy inference system approach, the relationship between COVID-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.
Keywords: Best-Worst Method; COVID-19; adaptive-network-based fuzzy inference; artificial intelligence; local linear neuro-fuzzy model.
. 2022 Dec 4.
doi: 10.1111/risa.14033. Online ahead of print.
Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques
Mehrab Tanhaeean[SUP] 1 [/SUP], Negin Nazari[SUP] 1 2 [/SUP], Seyed Hosein Iranmanesh[SUP] 1 [/SUP], Majid Abdollahzade[SUP] 3 [/SUP]
Affiliations
- PMID: 36464484
- DOI: 10.1111/risa.14033
Abstract
Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best-Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best-Worst Method-local linear neuro-fuzzy-adaptive neuro-fuzzy inference system approach, the relationship between COVID-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.
Keywords: Best-Worst Method; COVID-19; adaptive-network-based fuzzy inference; artificial intelligence; local linear neuro-fuzzy model.