Critically hybrid epidemics – from Internet worm Conficker to HIV in vivo infection
- Speaker: Dr Shi Zhou, UCL Media Future Group
- Date: Wednesday, 16 November 2016 from 17:00 to 18:00
- Location: Room 151
Many real epidemics in nature and society are hybrid epidemics, where more than one spreading mechanisms are used simultaneously.
Recently we studied the critically hybrid epidemics , where each of the spreading mechanisms is ineffective but the combination of them generates a significant outbreak.
We studied two distinct examples: the outbreak of computer worm Conficker on the Internet in 2008  and the infection of Human Immunodeficiency Virus (HIV) in human body .
Using the Internet measurement data, we revealed how Conficker was able to infect millions of computers in the first few days, why it is still active today, and why the worm could have been more infectious if it had mixed its three spreading mechanisms with an optimal ratio.
HIV can infect CD4+ T cells in two channels: the cell-free spreading, where the virus is released from infected cells into blood and then randomly infects other T cells; and the recently discovered cell-to-cell spreading, where the virus is transmitted directly between T cells when they form a synapse. We proposed the first mathematical model that can accurately recapitulate the entire HIV infection course as observed in clinical trials.
Our work highlights the possibility to produce a highly infectious epidemic by combining simple, ineffective spreading mechanisms. It explains the difficulty in eliminating critically hybrid epidemics such as Conficker and HIV, and calls for new strategies to fight against such epidemics.
Zhang, C., Zhou, S., Miller, J.C., Cox, I.J., Chain, B.M. (2015). Optimizing Hybrid Spreading in Metapopulations. Scientific Reports, 5 (9924). doi:10.1038/srep09924
Zhang, C., Zhou, S., Chain, B.M. (2015). Hybrid epidemics - A case study on computer worm Conficker. PLoS ONE, 10 (5). doi:10.1371/journal.pone.0127478
Zhang, C., Zhou, S., et al (2015). Hybrid Spreading Mechanisms and T Cell Activation Shape the Dynamics of HIV-1 Infection. PLoS Computational Biology, 11 (4). doi:10.1371/journal.pcbi.1004179