Fight with us
Naming ourselves THE NOSOI FIGHTERS we were inspired by the stories of classical
mythology. We were looking for the name who personified killing disease. And we
found... THE NOSOI (also known as Nosi, Nosos). These are the spirits, or rather
daimones of illness, plague and disease. Hesiod describes the nosoi escaping from
Pandora's jar, and like Elpis (Hope), they were probably personified to a certain
degree. Thus, already in this name we wanted to emphasize the connection between
the group and the aim of our project.
Future begins now
We created an innovative software solution,
CARE,
that utilizes pioneering sociological theories. It has a very practical purpose
of growing importance and demand: to counter infectious diseases like AIDS, malaria,
pneumonia etc. We understand and demonstrate how an epidemic spreads in a population
and how we can nail it before it nails us.
About Project
The project focuses special attention on research of Complex Networks. Complex Networks
have Scale Free and Small Word features, what make them accurate model of many networks
such as social networks. These features, which appear to be very efficient for communication
networks, favor at the same time the spreading of many diseases.
Based on defined centrality measures, we show how to discover the critical elements
of any network. The Degree Centrality measure gives the highest score of influence
to the vertex with the largest number of first-neighbours. This agrees with the
intuitive way to estimate someone’s influence from the size of his immediate environment.
If we need to find influential nodes in an area modeled by network it is quite natural
to use the Radius Centrality measures. This measure chooses the vertex with the
smallest value of shortest longest path starting in each vertex. Closeness Centrality
focuses on the idea of communications between different vertices. The vertex which
is ‘closer’ to all vertices gets the highest score. In effect this measures indicates
which one of two vertices needs fewer steps in order to communicate with some other
vertex. Betweenness Centrality (or Load Centrality) refines the concept of communications,
introduced in Closeness Centrality. Informally, Load Centrality of a vertex can
be defined as the percent of shortest paths connecting any two vertices that pass
through that vertex. This definition of centrality explores the ability of a vertex
to be ‘irreplaceable’ in the communication of two random vertices. It is of particular
interest in the study of network attack, because at any given time the removal of
the maximum betwenness vertex seems to cause maximum damage in terms of connectivity
and mean distance in the network. Where degree centrality gives a simple count of
the number of connection a vertex has, eigenvector centrality acknowledges that
not all connections are equal. In general, connections to people who are themselves
influential will lend a person more influence than connections to less influence
people.
The identification and then vaccination of the critical elements of a given network
should be the first concern in order to reduce the consequence of epidemics. We
define dynamic model for the spreading of infections on networks and build application
to simulate and analyse many epidemic scenarios for various diseases. Based on available
data of some social networks, we show how and why epidemics are spreading in real
networks and how could be halted.
Presenting idea is a new attempt at integrating theories and practices from many
area, in particular: social networks, graph and network theory, decision theory,
data mining and security. It utilizes that theoretical basis for very practical
purpose of growing importance and demand: widely understood countering high contagious
diseases like HIV/AIDS, SARS and others.
We are very grateful to: