IoT cloud-based framework using of smart integration to control the spread of COVID-19

• Main Authors: Andreas, Andreou, Mavromoustakis, Constandinos X., Mastorakis, George, Mongay Batalla, Jordi, Sahalos, John N., Pallis, Evangelos, Markakis, Evangelos
• Format: Conference Proceeding
• Language: English
• Subjects: cloud computing; Conferences; coronavirus; Coronaviruses; COVID-19; epidemic; Geology; Government; Internet; internet of things; machine learning; pandemic; Vaccines
• ISSN: 1938-1883
• DOI: 10.1109/ICC42927.2021.9500528

Coronavirus disease 2019 (COVID-19) is currently the most crucial emerging virus in the world. The absence of licensed medication or vaccination leads to alternative strategies. A fundamental response plan implemented by all countries was the detection and isolation of infected cases. Contact tracing of infected citizens and testing every suspected case is a prerequisite to avoid new quarantine measures. Infected cases called 'Orphan cases' with no epidemiological connection are more worrying. The initial method to identify them should be knowing the probability for a citizen to be infected, given that presents specific symptoms, to be tested as a suspected case and not as random. This article proposes a cloud-based identification system that studies suspected cases to increase the likelihood that a positive result is correct. Also, it introduces an innovative solution to prevent and control the further spread of Corona-virus disease based on smartphones through the deployment of cutting-edge computing systems in the framework of a Naive Bayesian Network (NBN). Furthermore, the integration of Google Maps could provide geolocation risk assessment and early inferences to government health authorities to raise the test rates in risk- prone areas.