7 Ways Machine Learning helping us fight the Viral Pandemic

As we are finally having the glimpse of silver lining in the form of effective vaccines, the Covid-19 viral pandemic is eventually expected to slow down its wrath. Still, there’s a long time to witness its ultimate end, and AI, along with ML, has turned out as promising tools for combating the spread of the CORONAVirus.

According to ML Development Stats, healthcare AI’s total investment is expected to cross 6.6 billion USD by 2021. Machine Learning is clearly emerging as a useful tool for combating the pandemic. However, the research is still in progress, and exact results may take some time to appear.

Let’s take a look at the top 7 ways in which Machine Learning is aiding to combat the Viral Pandemic

• Identifying the most vulnerable among us
• Diagnosing the infected
• Rapid drug/vaccine development
• Identifying the most useful existing drug
• Predicting the virus proliferation
• Better research of the deadly virus
• Finding the most likely hosts of the virus

Before we proceed to read each of them in detail, keep a note that in most cases, what is represented has mainly been evident in initial experiments, similar research, conclusions, etc., from the general/dedicated application of Machine Learning.

1. Identifying individuals with the highest vulnerability of infection

Machine Learning has always been effective in finding risks, and thus identifying the most vulnerable individuals or groups can be conveniently achieved by employing it. The risk involving identification can be done in the following three ways:

• Finding the risk of infection: Essential parameters for successful application of ML would require data index having age, social habits, usual hygiene conditions, social interactions frequency, etc.
• Analyzing the risk of developing severe complications that require professional aid: We all are well aware of the mysterious nature of the CORONAVirus with zero affect to some of us and putting others on the death bed. The facilities of developed nations have failed in offering advanced medical facilities to every citizen simultaneously. Thus, it is essential to separate the individuals more likely to develop severe complications.
• Diagnosing the death risk for a patient due to the drugs’ ineffectiveness: Researchers have already utilized ML to find outcomes of immunotherapy for Cancer patients. Similar models can be developed for the viral pandemic.

2. Diagnosing the patients

A significant issue of any new viral infections is related to conducting affordable tests. While researchers continue finding the best option, ML can be used as a first defense line for diagnosing the patients.

For example, ML face scans have been used to detect patients with fever. Similarly, AI based chatbots are useful in diagnosing based on the communication with the patient.

3. Rapid Vaccine/Drug Development

The usual methods of identifying the most suitable vaccine or drug involve multiple trial and error sessions. Speeding them up leads to quick identification of the most potent cure. ML-based rapid drug development has already been practiced in the case of EBOLA and H7N9 cure.

4. Identifying the most useful existing drug

A significant inconvenience in developing new drugs is the possibility of severe side effects. A pandemic demands effective medications in no time, which is hard to achieve. However, already tried and tested drugs are repurposed, and ML can effectively find the ideal ones.

5. Predicting the virus proliferation

While handling a pandemic, data plays an essential role in controlling the spread of the virus. For developing a pragmatic plan, the management authorities must be well aware of the live scenario and situation. A machine learning model effectively evaluates the valuable communication on social media platforms to produce valuable insights about the scenario.

6. Better research of the deadly virus

Biologically viruses are mainly made up of proteins, and their effect on the human body is primarily an outcome of the protein-protein interaction. The solution lies in interpreting these interactions to find out the possible cure for the body. However, analyzing them is not as simple as ABC, and the entire process is termed as the virus-host interactome.

Machine Learning models have been successfully used to predict the possible virus-host interactome for other deadly viruses like H1N1, Human Immuno Virus (HIV), etc.

7. Finding the most likely hosts of the virus

Finding the possible hosts from which the virus has erupted plays a crucial factor in its scientific study. Tons of viruses survive in diverse animal hosts and remain unnoticed from human research unless they start posing a threat.

Machine Learning models, by interacting with the genome sequence data, effectively pinpoint the possible hosts of a particular virus.

Final Words

That was all about how Machine learning models have become a persuasive technology tool for combating the viral pandemic. Healthcare ML development companies in India successfully deliver the project at a discounted price.

Choosing an Indian firm also allows you to closely monitor your project at your convenience via daily/weekly/monthly reports.