The current health crisis of the world is getting very critical because it is not only killing patients but also the medicals staffs who are treating the patients. In China, more than 3,000 doctors were infected, nearly half of them in Wuhan, where the pandemic began, according to Chinese government statistics. Li Wenliang, the Chinese doctor who first tried to raise the alarm about Covid-19, eventually died of it. In Italy, the number of infected heath care workers is now twice the Chinese total, and the National Federation of Orders of Surgeons and Dentists has compiled a list of 50 who have died. Nearly 14 percent of Spain’s confirmed Coronavirus cases are medical professionals. Even though the data of how many medical personals have been infected is not reported to the public, it is not hard to imagine it is very large in number because of the level of infection in that city. This data is very worrying and if we see the world infection data for patient VS the medical staff, you can see it is matter of time that we no longer have sufficient medical personals to control the put break.
This project is building algorithms that calculate the chance or probability of infection occurrence on medical personal based on their activities and rotates with other medical staff to even the risk. This is very important because, no medical staff should hold the burden of getting high probability of getting infected with Coronavirus by himself or herself. This project will show that we have appreciation for our medical staff and this will be a good way of showing it besides clapping our hand in our window being in protected our home which does not really provide much help in protection of their health besides being a moral strength. The working mechanism of the algorithm bases on the data we gather from other infected medical personals like the data that shows how many patients they treated, how many risky medical operations or treatments they had, how many hours of work they spent in the treatment center, their physical conditions and other factors before they got infected with the Coronavirus. This calculation will help us to estimate mathematically, which medical staff is in risk of getting infection and switch her/ him to lower load faculty and bring other personals sharing the risk equally.
The algorithm can be built in matter of days with right team in place and there need to be complete transparency of the data between the developer and the hospital centers in supplying the required input listed above. In order for the algorithm to function as intended we need to register and record every activities of the medical staffs which is the basic procedure of standard medicals centers, so the algorithms most likely be very practical.