The COVID-19 pandemic has woven a lot of changes into the fabric of society. Many of these changes have pertained to technology and the ways in which technological tools have been harnessed. Machine learning is a prime example of this. On fact, there is much to be gained from reviewing the interactions of machine learning analytics and COVID-19.
Since the earliest days of the COVID-19 pandemic, one of the biggest challenges for health systems has been to gain an understanding of the community spread of this virus and to determine how likely is it that a person walking through the doors of a facility is at a higher risk of being COVID-19 positive.
In response, machine learning was used to modify an existing epidemiological model to predict the spread of disease. Researchers introduced a neural network trained to deal with real data associated with the novel coronavirus. This new model was based on the number of individuals who fit into a variety of categories, such as susceptible, contagious, or recovered.
Over time it has learned to identify patterns in the data, as it relates to cases of infected and recovered people. From this data, it can figure out the number of infected individuals who happen not to be spreading the virus to other people. The resulting value shows how successful any given region is in quarantining a person who is infected. Over time, the model will be able to process data in order to see how the quarantine strength of a region evolves.
The model was applied to different countries and different states in the United States. Real data was used, and the results showed that in every single stage, there was a noticeable decline in quarantine strength soon after businesses started to reopen. The steepness of the decline and the subsequent rise and number of infections was strongly related to whether the state reopened earlier or later.
Overall, these findings have demonstrated just how effective following guidelines in pandemic measures can be in slowing the spread of COVID-19.
Medical Outcome Predictions
Other machine learning informed models have been created that can predict outcomes in infected people when they go into the hospital. There was a model that was capable of predicting whether or not a patient would be intubated and/or die within 30 days of checking into the hospital, and the model turned out to have a sensitivity of approximately 82 percent.
This algorithm can help healthcare providers predict outcomes in COVID-19 patients when they are in the hospital or emergency room. It even helps when it comes to patients who have mild symptoms. This type of model and algorithm could be very useful in the future, because it could aid healthcare providers in the anticipation of the worsening in condition of certain patients, so they are prepared to handle the situation.
It also demonstrated laboratory tests and initial imaging provided sufficiently accurate information to be able to reliably predict these outcomes in patients who are suffering with COVID-19.
Overall, machine learning analytics have been very useful in the fight against COVID-19. These are but two of a number of ways the technology has informed the actions of health care professionals. They have also provided a great deal of information as far as what people should be doing to avoid infection and what they can potentially expect if they are infected and need to be hospitalized.
The catastrophic outbreak has brought a worldwide threat to the living society. In response, the whole world is exerting an incredible effort to arrest the spread of this deadly disease in terms of infrastructure, finance, data sources, protective gears and life-risk treatments and several These are just two of the many ways machine learning has helped researchers and health care workers get their arms around this unprecedented calamity.