Using Dynamic Models to Showcase Pandemic Prevention Empirical Covid-19

Abstract

Coronavirus disease (COVID-19) is a viral contagious disease caused by a newly discovered coronavirus. The COVID-19 virus primarily spreads from an infected person through droplets of saliva or nasal discharge when the person coughs or sneezes, and most people who have been infected with the virus usually experience mild to severe respiratory illness, and they recover with minimal or no treatment. COVID-19 causes mild illness in the majority of patients although it can be fatal in rare cases. Our project focuses on using an SPO2 level monitor and thermal scanning to monitor patient health and take precautions to avoid constant transmission, as well as providing support to patients by assisting them with basic needs with the help of food delivery agencies and non-governmental organizations (NGOs) and assisting with prevention. We use an enhanced version of the SIR epidemic model, which is further explained in this work as an IoT-based system which is being used for automated health monitoring and surveillance, this work aims to reveal certain facts about the current situation that are not presented by data, as well as predict and forecast future situations. AI-assisted sensors can be of major help to foresee whether or not someone is tested positive for the virus supported on indicators like body temperature, coughing patterns, and blood oxygen levels. The ability to track people's locations is another helpful function. All these problems collectively checked will make an efficient model to curb the virus.