Covid-19 Spread Detection and Controlling with Fog-based Infection Probability Evaluation Model

TL;DR

This paper proposes a fog-based system utilizing Hidden Markov Models and Bluetooth contact tracing to detect and control COVID-19 spread efficiently, aiming for timely awareness and precautions without long delays.

Contribution

It introduces a novel fog server system that combines HMM and Bluetooth contact tracing for real-time COVID-19 spread detection and control, with polynomial computational complexity.

Findings

Effective detection of COVID-19 spread using real-world data

System operates with polynomial time complexity

Enhanced awareness and precaution measures enabled

Abstract

COVID-19 has created a pandemic around the world, paused the path of building the future, and still ongoing without having any long-term solution shortly. The time taken in vaccine distribution is too slow compared to the spread of COVID-19. Hence, it is important to aware and takes precautions on time without delaying and waiting for long-duration after getting infected with the virus. Currently used technology is more advanced than ever before. Almost everyone has access to at least one mobile device with an Internet connection. Therefore, we propose a Fog Server (FS) based system that can be used to create awareness about the spread of COVID-19 within the surroundings of individuals utilizing the concept of Hidden Markov Models (HMM) and Bluetooth contact tracing, in polynomial computational time complexity. Moreover, we evaluate the effectiveness of the proposed model through real-world data analysis on different simulation parameter settings.