Contact Tracing of Infectious Diseases Using Wi-Fi Signals and Machine Learning Classification
| Autor: | Zahriddin Muminov, Mavlutdin Narzullaev, Anvar Narzullaev |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
2019-20 coronavirus outbreak Coronavirus disease 2019 (COVID-19) business.industry Computer science 02 engineering and technology Machine learning computer.software_genre Disease control World health Statistical classification 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Contact tracing |
| Zdroj: | IICAIET 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) |
| Popis: | There is just a handful of interventions proven to curb the spread of infectious diseases. One of them is contact tracing that involves reaching infected people to investigate where they might have been infected and whom they might have exposed to the virus. Contact tracing has been identified as a core disease control measure by the World Health Organization and has been exercised by state health agencies for decades. In this research, we proposed a new contact tracing method based on machine learning classification algorithms, for infectious diseases, such as COVID-19. The proposed method uses the Wi-Fi signals data from a possible contact and a confirmed patient's smartphones to detect whether the two shared the same physical space. Simulation results show up to 95% tracing accuracy depending on area size. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|