Effectiveness of Network Interdiction Strategies to Limit Contagion During a Pandemic

COVID-19 is an infectious disease that has been declared a global public health emergency by the World Health Organization. Besides claiming over 3 million lives worldwide, led to unprecedented disruption in industrial productivity, trading, and food supply, resulting loss of livelihood. Despite initial success curbing spread diseases through lockdown rapid vaccine development, human are threatened sudden outbreaks from new strains virus. This motivates conceptualization effective interdiction rules inform mobility manner damage as well economy could be minimized. In this work, we present three employ machine learning-based network inference on daily infected cases infer influence contagion between neighboring zones. The proposed leverage science concepts such coloring clustering attain time-varying partial or complete travel restrictions. Through extensive simulation experiments, show these strategies yield lower infection than greedy random migration-based tie elimination approaches balance mitigation economic gain.