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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7593| Understanding Evolution of COVID-19 Driven Mortality Rate | |
| Ishika Bhaumik Suman Sinha Ray ANSHUL CHAUDHARY Abhishek Srivastava Prashant Kodgire | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2022.01.16.22269210 | |
| https://www.medrxiv.org/content/10.1101/2022.01.16.22269210v1 | |
| The precise reason for variations in COVID-19 related mortality rates is unknown. In this article, we show that a biological science guided machine learning-based approach can predict the evolution of mortality rates across countries. We collected publicly available data of all the countries in the world with regard to the mortality rate and the relevant biological and socio-economical causes and analyzed using a novel FFT driven machine learning algorithm. Our results demonstrate how COVID-19 related mortality rate is closely dependent on a multitude of socio-economic factors (population density, GDP per capita, global health index and population above 65 years of age), environmental (PM2.5 air pollution) and food habits (meat consumption per capita, alcohol consumption per capita, dairy product consumption per capita and sugar consumption per capita). We anticipate that our work will initiate conversations between health officials, policymakers and world leaders towards providing preventative measures against COVID-19 and future coronavirus-based diseases. | |
| medRxiv and bioRxiv | |
| 18-01-2022 | |
| Preimpreso | |
| www.medrxiv.org | |
| Inglés | |
| Epidemia COVID-19 | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Understanding evolution of COVID 19 driven mortality rate.pdf | 1.07 MB | Adobe PDF | Visualizar/Abrir |