COVID-19 and Data Science
The rapid spread of the coronavirus has turned the lives of many people upside down in this past year. COVID-19 followed by the lockdown had shattered our lives and has made collateral damage to society in economical, social, education as well as psychological and physical health.
Around the globe, millions of people have become the victim of this disease. To date, approximately 106 million people have tested positive for COVID-19 worldwide and above 2 million have died from this disease. To keep the spread of the virus under control, every individual has contributed hugely in so many ways such as maintaining social distance, medical treatment, and help in need. Alongside other approaches that have been emerged from Data Science and AI communities has helped to decline the graph of active COVID-19 cases.
Approach to understand the COVID-19: Transmission, Incubation, and Environmental Stability
The AI Company Black Swan and the White Swan have collaboratively worked on a search engine for academic coronavirus papers to develop an NLP (Natural Language Processing) tool to search 44,000 research papers related to coronavirus. The aim is that any clinician or researcher can search for answers without having to read through thousands of papers.
Here I have mentioned some approaches on how data science and AI has contributed to control the spread of COVID-19 with Data Science
Acknowledgment of the movement of people along with how effectively precaution has been implemented is one of the important factors that helps to undermine the chain of infection.
1. Mitigating the spread of COVID-19 by restringing the individual mobility
COVID-19 Mobility Monitoring Project is one of them in Italy, which uses data collected from anonymized users who have opted-in to provide access to their location data anonymously, through a GDPR-compliant framework. In this project, data is collected through smartphones using multiple sensors like GPS, Beacons, Wifi, and network.
2. COVID-19 Open Research Dataset Challenge (CORD-19)
CORD-19 is the challenge of a large set of scientific papers that have been released by the white house and a group of researchers where the freely available dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against COVID 19.
Similarly, many models are used to project the spread of COVID-19, one of them used is SIR, SIR( Susceptible, Infected, Recovered) model is a predictive model for infectious diseases that are transmitted from human to human. And here S, I, & R are the variables representing the number of susceptible, infectious, and recovered individuals.
SIR model provides us with the insights and prediction of the spread of the virus and helps to access the impact of the disease by offering valuable predictions. Likewise, SEIR (Susceptible Exposed Infectious Removed) epidemic model is a derivative of the simple SIR model which predicts infectious disease dynamics by compartmentalizing the population into four possible states: Susceptible[S], Exposed or latent[E], Infectious[I], and Removed[R].
Conclusions
Other many approaches have emerged from Data Science, Predictive models, and algorithms, and their successful implementation has immensely helped to mitigate and control the spread of COVID-19 viruses. And finally, after continuous research, study, and effort, scientists have introduced a vaccine in less than a year and that has been invigorating to know that our field has been able to fight and control the accelerating global crisis.
If you know other approaches, please feel free to share them in the response. Happy Reading!
References:
https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge/tasks?taskId=882
https://www.nature.com/articles/s41597-020-00575-2
https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
