Early detection of COVID-19 is crucial in preventing the spread of this disease. In some regions, it is possible to detect this disease with the use of wearable devices. However, this method is not always effective. It is not always possible to monitor an entire population, as some people may not report symptoms of infection. Moreover, it does not always guarantee early detection, which is why early detection has to take the dynamic nature of the disease into account.
The goal of the current study is to build an algorithm to detect COVID-19 infection earlier and recommend treatment. While participants will not experience any direct medical benefit, they will be able to help scientists develop algorithms to detect COVID-19 early in other high-risk workers. In order to participate, participants must be over 18 years of age and undergo self-quarantine for suspected COVID-19 infection.
The mHealth application used for early detection of CVD risk can be accessed using smartphones. The data collected from the mHealth application is collected and stored on a server. Health workers can then use it to improve the program. If the tool works well, it can be used to educate and encourage people to take care of their heart health.
Early detection of COVID-19 is critical to containing the spread of the disease. In the early stages of the pandemic, COVID-19 transmission is often asymptomatic and unnoticeable, with no symptoms. In many countries, traveler-based viral genomic surveillance is used to detect novel variants and gather comprehensive epidemiological data.