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