The ongoing novel coronavirus (COVID‐19) epidemic has evolved into a full range of
challenges that the world is facing. Health and economic threats caused governments to …

In times of epidemics and humanitarian crises, it is essential to translate scientific findings
into digestible information for government policy makers who have a short time to make …

Social network information is a measure of the number of infections. Understanding the
effect of social network information on disease spread can help improve epidemic …

Governments, policy makers, and officials around the globe are working to mitigate the
effects of the COVID-19 pandemic by making decisions that strive to save the most lives and …

In this article, we develop two independent and new approaches to model epidemic spread
in a network. Contrary to the most studied models, those developed here allow for contacts …

This article proposes methods for efficiently computing the anonymity of entities in networks.
We do so by partitioning nodes into equivalence classes where a node is k-anonymous if it …

The emotion of fear related to an infectious disease not only influences an individual's
behavior but also transmits to social contacts. Therefore, modeling human behavior is a …

The COVID-19 pandemic imposes new challenges on the capability of governments in
intervening with the information dissemination and reducing the risk of infection outbreak. To …

Network robustness plays a crucial role in our understanding of complex interconnected
systems such as transportation, communication, and computer networks. While significant …

It is well known in the literature that human behavior can change as a reaction to disease
observed in others, and that such behavioral changes can be an important factor in the …