Networks are ubiquitous in natural, technological and social systems. They offer a fertile framework for understanding and controlling the diffusion of ideas, rumors, and infectious diseases of plants, animals, and humans. Despite recent advances, many challenging scientific questions remain about the correct tools and their practical role in epidemics dynamics and effective strategies supporting public health decision making. The goal of this special issue is to offer a platform to the interdisciplinary community of scientists working on the diffusion process on networks and its plethora of applications. We hope for a broad range of topics to be covered, across theory, methodology, and application to empirical data with a special emphasis on epidemic spreading.
Expression of interest and abstract submission: July 10, 2020
Abstract feedback notification: July 13, 2020
Paper submission deadline: September 21, 2020
Target publication: November 01, 2020
Daniel B Larremore, Bryan Wilder, Evan Lester, Soraya Shehata, James M Burke, James A Hay, Milind Tambe, Michael J Mina, Roy Parker
The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with pre-symptomatic, symptomatic, and asymptomatic infections, the re-opening of societies and the control of virus spread will be facilitated by robust surveillance, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are usually too low to detect, followed by an exponential growth of virus, leading to a peak viral load and infectiousness, and ending with declining viral levels and clearance. Given the pattern of viral load kinetics, we model surveillance effectiveness considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective surveillance, including time to first detection and outbreak control, depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity. We therefore conclude that surveillance should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.
One hundred years after it was proposed, the Ising model is used to understand everything from magnets to brains.
The Observatory on Social Media (OSoMe, pronounced ‘awe•some’) at Indiana University is looking for a postdoctoral fellow to work at the intersection of computing, network, data, and media sciences with a focus on (mis/dis)information diffusion and the detection and countering of online manipulation.