Understanding evolution of COVID-19 driven mortality rate
Abstract
Objective
COVID-19 has resulted in the death of almost 4 million people till date 1 . However, the mortality rate across countries seems to be vastly different irrespective of their respective socio-economic backgrounds. It is well known now that COVID-19 is an acute inflammatory infectious disease that gets complicated by type-I interferon response 2,3 . However, the precise reason for variations in COVID-19 related mortality rates is unknown. A detailed understanding behind the evolution of mortality rate around the globe is needed.
Methods
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 the publicly available data of all the countries in the world with regard to the mortality rate and the relevant biological and socio-economical causes. The data was analyzed using a novel FFT driven machine learning algorithm.
Results
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 lifestyle aka food habits (meat consumption per capita, alcohol consumption per capita, dairy product consumption per capita and sugar consumption per capita). Interestingly, we found that individually these parameters show no visible trend that can be generalized with mortality.
Conclusions
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 and endemics/ pandemics by taking a holistic view.
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