Continuous Intelligent Pandemic Monitoring (CIPM)

Abstract

This proposal applies measurement science (accounting), assurance science (auditing), and machine learning predictive analytics to epidemic research It utilizes accounting frameworks, such as Continuous Monitoring, to establish a system that can assess the realistic parameters and continuously monitor the evolution of COVID-19 by using exogenous variables Continuous Intelligent Pandemic Monitoring (CIPM) can generate alerts following risk assessments from the time series, machine learning models, and cross-sectional analytics CIPM provides policy guidance based on epidemic simulations The goal is to validate the epidemic related numbers and to provide guidance to policymakers so that sufficient resources can be allocated to the upcoming high risk areas in order to control the spread and lower the impact of the disease Through this study, we hope to provide different knowledge and perspectives to COVID-19 analysis and a different pandemic measurement and data validation approach