Introduction
To slow down the transmission of Coronavirus, most countries all over the world have resorted to quarantine strategies and social distancing. But this will only inhibit the spread of the virus, and cannot be the ultimate solution. Scientists are using the power of artificial intelligence, especially machine learning to understand the impact of quarantine measures in the fight against COVID-19.
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Let us learn more about the subject.
Machine Learning quarantine prediction: Developments at MIT
Researchers and scientists have been analyzing huge sets of data based on previous outbreaks such as SARS and MERS. Recently, a team at MIT has developed a machine learning model that uses the COVID-19 data along with a neural network. The combination of these two technologies will predict the spread of this virus and understand the effectiveness of quarantine.
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The tools that were used earlier for understanding disease transmission were based on the SEIR model. This model segmented people as susceptible, exposed, infected, and recovered. This model was improved by MIT researchers. They trained the neural network to gather data about infected individuals under quarantine who are not transmitting the virus.
Powered by machine learning and epidemiology, the model immediately showed results depicting the efficiency of quarantine in different countries. The analysis showed that in countries such as South Korea, strict quarantine measures slowed down the transmission rate of COVID-19. This was because the government was quick to intervene and implement quarantine.
Read: 24 Things To Do in Quarantine For Professional Growth
The machine learning model also depicted that in countries such as Italy and the USA, the virus spread rapidly and exponentially. This was due to the fact that government agencies were slow to react and implement quarantine strategies. According to the model, quarantine restrictions have successfully brought down the rate of effective reproduction of COVID-19 from greater than 1 to smaller than 1.
Using the data from different countries, the SEIR model was used along with the neural network to predict the effect of quarantine on the COVID-19 transmission rate. The neural network was trained to learn something called quarantine control strength function. The model further predicts that the infection rate will go up to almost 6,00,000 in the USA before starts to dropdown.
According to the researchers, the model shows that relaxation in quarantine measures can spike up the transmission rate again.
Research and developments at Rensselaer Polytechnic Institute (RPI)
Researchers at RPI are also analyzing Machine Learning quarantine prediction. They are using machine learning to evaluate the effectiveness of social distancing. The machine learning models developed here use medical data from New York’s health department. Data from counties Albany, Schenectady, Saratoga, and Rensselaer until April 10 were collected.
This machine learning model predicted that the total number of infections will be around 58,000 by June 8, with 50% people quarantined at home.
Machine learning models used in smaller cities posed a problem as the medical data was not frequently updated. So, to solve this issue the researcher at RPI, Magdon-Ismail used machine learning to create a simple model that suits the data best. This is helping him to analyze the importance of quarantine and social distancing measures in these smaller cities.
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Another team of researchers used human mobility data gathered by Baidu to understand COVID-19 transmission in China. Machine learning was used to analyze the data. It was found that, yet again, quarantine measures reduced the number of infections and dropped the rate of transmission in China.
Read: How Data Science Can Help Preventing Future Pandemics
Conclusion
All the reports discussed above suggest that machine learning quarantine prediction is playing a significant role in mitigating the COVID-19 pandemic. It is proved that quarantine and social distancing rules are the best ways to keep the transmission rates in check until an absolute solution is found.
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