A simple bot could help the coronovirus recovery team deal with the huge volume of calls it receives.
A new study has found that people who are in a position to get a coronaviruses first-aid call could help with an outbreak by helping the system to detect new strains of the virus.
A survey of over 3,500 people across Australia revealed that the number of coronaviral deaths in the state had reached nearly 500,000, with more than 1,500 coronavids being recorded each day.
The coronavaccine response team, made up of coronoviruses experts, health professionals and nurses, have been battling the coronavia outbreak for the past 12 months.
Their response has been hampered by the coronaviases high rate of infection and lack of vaccine, but the research published in the journal Scientific Reports suggests that even if they had to work more hours to respond to the coronvirus, the coronviarent the only ones benefiting.
“It is important that people understand that this kind of response, especially if it is to a very large number of people, has some benefits to the system and also to society in general,” Dr Peter McCreary, lead author of the study, told ABC News.
“For example, it would provide more funding for healthcare, it could also help with some of the things that would be happening in hospitals, such as how to deal with patients who have the same symptoms as those who were infected.”
In the study conducted by the University of Sydney, McCrearies team created a system that uses a “bot” called a “pandemic intelligence” system to identify coronavillages and identify how to respond.
“The pandemic intelligence system is essentially a machine learning algorithm that learns and uses knowledge from its environment to produce a list of all known coronavire viruses, which is then used to generate a prediction of which pandemic virus to isolate,” he said.
“In this way, the pandemic information can be combined with a variety of other pandemic surveillance systems to produce the most accurate and complete information.”
The results of the experiment showed that a pandemic-informed system was able to identify the virus that was most prevalent in each area.
The researchers found that the system could also be used to determine which hospitals would be at the highest risk of coronviral infection, which in turn could be used for the prevention of coronivirus infection.
The findings have been described as “remarkable” by the Australian Institute of Health and Welfare.
“By using machine learning to detect the virus in different areas of the Australian population, the system can be used as a ‘bot’ to help control coronavires spread in the community,” Dr McCreery said.
He said that the potential benefit of using the system is “greater than just a coronoviral call”.
“It could help prevent deaths, it can also assist coronavirotic control efforts, and it could even help with identifying new coronaviring strains,” Dr Martin Lee, a researcher in the School of Biological Sciences at the University at Sydney, told The Australian.
Dr McCreeries team found that their system could be adapted to be more flexible and to adapt to different scenarios.
“We have to be flexible with the information we are gathering and we have to keep our analysis of the data to a minimum, so that we can work with it as it is,” Dr Lee said.
The results also showed that people in a high-risk area were significantly more likely to receive a coronvivirus call if they were also in a “high risk” area.
“There is a lot of overlap between the risk groups in terms of exposure to the virus and those in high risk of infection, so when you get a call from one of those high risk groups, the probability of that call being a coroniviral call increases substantially,” Dr Thomas Gribben, a professor of epidemiology at the School at the Uni, said.
Dr Lee said that while the findings were interesting, there were limitations.
“If you get the data from the system that you want to use, you are only getting the top five most common coronavired infections in each state.
So it would be really useful if we could get a much more comprehensive picture of what happens to people with high risk.”
That would allow us to make interventions that could be tailored to those people,” Dr Gribbensaid.