After being diagnosed with pancreatic cancer in 2011, Steve Jobs had an epiphany. “I think that we will witness the most important developments in the coming century at the nexus of biology and technology,” says the scientist.
The present pandemic, which has been going on for at least 2 years, is on par with the Spanish Flu outbreak of 1917 in terms of speed and potential for morbidity and mortality. The state of science now, in contrast to 1917, is more knowledgeable about biology, genetics, and the dynamics of illness.
Within a few years, we had built a strong computational capacity that allowed us to identify patterns and insights quicker than the human brain could handle them. The question is, how can machine learning assist us in dealing with the first worldwide pandemic since the invention of artificial intelligence (AI)?
In a search of Google Scholar, it was discovered that artificial intelligence and machine learning, as well as COVID-19, would be the topic of at least 19,700 papers in preprints or peer-reviewed journals by the year 2020. Elsevier recently published a meta-review of the research on artificial intelligence and machine learning applications for COVID-19.
It found that these applications could play a variety of roles, such as making medicine and screening it for side effects (Lalmuanawma, 2020). The majority of the dissemination among the general public and scientists is likely to have happened via Twitter, which is owned by PLOS, the Journal of Medical Internet Research (JMIR), and MedRxiv a publication of BMC, Yale, and Cold Spring Harbor Laboratory among other platforms.
Role of Data Science in managing Covid-19 pandemic
COVID-19 infectees who are asymptomatic or resistant to the adverse symptoms that cause illness and death may infect others, according to the study’s findings. “Silent carriers,” as they’ve been dubbed, remain a mystery. Immune people could go to work and school and shop without putting themselves in danger, for example, if they could be categorized or differentiated from the 20% of people who would fall ill.
While 20% of the population is in danger, they can be protected. We may learn more about the pathophysiology of illness and develop novel treatments for patients who cannot get vaccines if we can identify distinguishing biochemical, genetic, or molecular biomarkers that indicate healthy individuals from sick ones. A third benefit is that mass immunizations may be prioritized based on 20 percent of the population’s most significant risk.
As a consequence of their investigation, the researchers developed two methods for classifying and identifying persons at high risk of contracting COVID-19 infection using machine learning. A study that included 37 asymptomatic and 37 symptomatic COVID-19 patients discovered that 100 percent of the asymptomatic patients had an immune protein called stem cell growth factor-beta (SCGF-B) with a value of more than one 127,637 in a study that included 37 asymptomatic and 37 symptomatic patients.
A greater level of interleukin-16 (IL-16) and macrophage colony-stimulating factor (M-CSF) was detected in 94.8 percent of COVID-19 immunological patients, compared to lower levels of these two proteins. How and where the study results were disseminated is also essential at the macro level of how data science is being used to assist in the COVID-19 pandemic response effort. On August 16, 2020, MedRxiv made the pre-print available, verified for quality but not peer-reviewed before publication. The abstract has been seen 1,553 times; however, 328 individuals have downloaded the whole text today.
It was tweeted about by a total of 108 people, resulting in an Altmetric impact score of 58 for the tweet. Since its publication on October 19, 2020, the peer-reviewed paper has been shared 191 times on social media and currently has an Altmetric impact score of 10. It has also received seven tweets from five different users and has been picked up, televised, and read by a single news source, according to Altmetric.
How did Data Science help combat the Covid-19 pandemic?
According to anecdotal evidence, science is making discoveries at a faster rate than humanity’s ability to learn about and use them. Even with the usage of social media tools such as real-time posting of results online, Twitter, LinkedIn, and other similar platforms, there is still a barrier to using the vast amount of scientific data. According to experts, those technologies that employ a meritocracy to pick and accelerate the implementation of the most critical research findings will be among the most impactful in the following years.
The IoT Academy serves as the right destination for you to learn more about data science. As data remains the new gold in recent times you must capitalize on your efforts in learning more about it and more future opportunities.
