The Role of AI in Controlling the Spread of the COVID-19 Pandemic
Well, the worst of the COVID-19 pandemic has passed many countries. And with the successful roll-out strategy of vaccines and the contributions of AI, the best is yet to come. So, read this article to know more about how AI can curb the spread of COVID-19, and how it can improve medical planning during future epidemics.
COVID-19 has been the worst pandemic in 100 years since the Spanish Flu. And Artificial Intelligence has shown us a gamut of applications in the last decade, making us hopeful of a mask-free future.
The scientific community has gone into overdrive mode too. Innovations in AI keep coming up, thus improving the recovery rate. And medical planning is undergoing drastic changes. Plus, vaccines are getting delivered through reliable incentivization programs. So, what crucial elements are we missing?
How are we developing vaccines?
Scientists are trying to find out how to battle the coronavirus through improvement in testing and intensive vaccine testing. But, this alone needs time for widespread acceptance. Not to mention, there is still the issue of vaccine hesitancy in some developed countries. For instance, the US has a large population still waiting to get vaccinated against the oncoming delta variant.
How AI can help consolidate efforts for a future wave?
Traditional know-how is not enough to beat this pandemic as medical equipment often is scarce during critical moments. For instance, the medical infrastructure collapsed during the oxygen crisis. And the necessary actions are slowed down by partisan politics.
Here is where machine learning and its subset, deep-learning, can help us make conclusions, and make executable decisions. AI can help cut through the noise and help us cope with any human error.
The Role of Machine Learning
Predictive analysis can figure out the number of patients or deaths in the future, with some months of data parsing. And by learning from it, we can precisely forecast the number of deaths in men above 60.
However, once we have this data and make conclusions based on what we see in the data trends sourced from Big Data, we require machines to work autonomously to develop intelligent solutions. Here are some use-cases:
- Most countries didn’t anticipate a pandemic, so people didn’t know how to react, although AI alerted authorities about the first case on December 31st, 2019 (in Wuhan, China.)
- There weren’t enough testing supplies and medical equipment in some countries, so governments have to collaborate with medical officials to plan with AI inputs.
- Lastly, there wasn’t enough time to wait for a vaccine, so we needed real-time inputs from AI that could help people learn to control the spread. We have to be open to these inputs and build solutions around them.
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The Role of Deep-Learning
Deep-learning uses algorithms and stacks them in a way that allows machines to make intelligent decisions autonomously. It can bring solutions to real-world issues. For instance, in China, AI-powered glasses detected people with a fever to maintain social distancing norms.
Conclusion
Our dependency on AI was always coming. Elon Musk says that AI will be “vastly smarter” than any human by 2025. But, the pandemic is the real test for AI. What’s more, in South Korea, X-ray screening for COVID-19 risk assessment is much more accurate with AI, and they follow strict social distancing too. That is why AI works best with better COVID-19 awareness and vaccine acceptance. These are the crucial elements missing in our path to success.
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