Virus Spread in Networks

ViSioN presents my Network Science view on virus spread in networks, in which the duality between the virus transmission process and the contact graph is key.The devastating Corona crisis reveals two major shortcomings in traditional epidemiology.

First, it ignores the human contact graph and implicitly assumes a homogeneous population without specific graph structure.

Second, most models for the virus spreading process relate to a Markovian setting, with exponential infection and curing times, leading to an exponential decay of the epidemic. Measurements, however, point to significantly different infection and curing time distributions.

In addition, digital technology can help in constructing the contact graph and combined with medical testing, all infected can be detected, thus avoiding a second wave.Building on my pioneering work on Markovian epidemics in networks, I will complement the recipe book of epidemic model ingredients with corresponding algorithms/software for next pandemic outbreaks.

I will develop the theory of non-Markovian epidemic process on networks, a surprisingly missing element today, because non-Markovian theory is needed to tell, based on the characteristic infection and curing times of the virus, how long a pandemic will last and when the peak occurs.

Next, I will combine all available measurement technologies to construct the best possible contact graph via temporal networking or adaptive networking.

Finally, I will explore how accurately infections can be predicted under partial information of process and contact graph.ViSioN’s outcomes will allow to predict, manage and control any epidemic in the best possible way.

Moreover, as epidemics are part of the larger class of “local rule–global emergence” systems, my outcomes will be directly beneficial for the other members of this broad and abundant class, and find applications ranging from computer malware spread to human brain surgery.