Volume 12, Number 4

Analyzing the Effects of Different Policies and Strictness Levels on Monthly Corona Virus Case Increase Rates using Machine Learning and Big Data Analysis

  Authors

Charline Chen¹ and Yu Sun², ¹USA, ²California State Polytechnic University, USA

  Abstract

The corona virus is one of the most unprecedented events of recent decades. Countries struggled to identify appropriate COVID-19 policies to prevent virus spread effectively. Although much research has been done, little focused on policy effectiveness and their enforcement levels. As corona virus cases and death numbers fluctuated among countries, questions of which policies are most effective in preventing corona virus spread and how strict they should be implemented have yet to be answered. Countries are prone to making policy and implementation errors that could cost lives. This research identified the most effective policies and their most effective enforcement levels through data analysis of 12 common coronavirus policies. A monthly case increase rate prediction model was developed to enable decision makers to evaluate the effectiveness of COVID-19 policies and their enforcement levels so that they can implement policies efficiently to save lives, time, and money.

  Keywords

COVID-19 Policy Effectiveness, COVID-19 Policy Enforcement Level, COVID-19 Policy Enforcement Level and Effectiveness Consistency, Machine Learning Enabled Monthly COVID-19 Case Increase Rate Prediction.