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Strategy to Stop a Pandemic

Sunshine123

Well-known member
http://www.sciencenews.org/view/generic/id/33877/title/Strategy_to_stop_a_pandemic
Strategy to stop a pandemic
By Davide Castelvecchi
July 4th, 2008
Web edition


New approach could effectively use a scarce supply of vaccine
access
Stopping the spread.

Illustrated are three different strategies for preventing the spread of a disease across a simple social network by targeting vaccine shots to a limited number of people (nodes marked red). Picking people at random (top) doesn?t help the others ? the disease can still spread freely. Vaccinating the people who are most connected (center) leaves a large network where the disease could still spread, while some nodes are, unnecessarily, completely isolated. A targeted approach that partitions the network into roughly equal parts (bottom) gives a better chance at protecting the most people.
Yiping Chen, Boston University

When deadly bird flu strikes, six degrees of separation could be the distance from here to hell. Even if a vaccine is found to be effective, it may be impossible to produce enough shots for everybody quickly enough, so authorities would have to decide how to use the doses they have in the most effective way. Researchers are now proposing a new strategy for targeting shots that could, at least in theory, stop a pandemic from spreading along the network of social interactions.

Vaccinating selected people is essentially equivalent to cutting out nodes of the social network. As far as the pandemic is concerned, it?s as if those people no longer exist. The team?s idea is to single out people so that immunizing them breaks up the network into smaller parts of roughly equal sizes. Computer simulations show that this strategy could block a pandemic using 5 to 50 percent fewer doses than existing strategies, the researchers write in an upcoming Physical Review Letters.

?The strategy is to disintegrate the network,? says study coauthor Shlomo Havlin of Bar-Ilan University in Ramat-Gan, Israel. Havlin and his collaborators say their method could also offer a cost-effective way of blocking the spread of computer viruses on the Internet, or breaking up a terrorist network.

?The idea of splitting a network into equal subnetworks is very simple, yet quite successful,? comments network-theory expert Dirk Brockmann of Northwestern University in Evanston, Ill. ?It?s a surprise to me that it seems to work so well.?

The hard part could be getting enough information about the structure of social networks to know which nodes to target, says Alessandro Vespignani, a physicist at Indiana University in Bloomington who also studies mathematical models of pandemics. ?I see this method as more promising in the context of computer viruses,? Vespignani says, because the Internet?s structure is easier to understand. In the case of pandemics, the strategy might still be effective for restricting travel by shutting down nodes in the network of global airline traffic.

Network-theory researchers have often assumed that one of the most efficient ways of blocking a pandemic is to immunize people who have the largest number of social connections. However, most people are separated from most other people by the proverbial six degrees of separation, and removing only the highly connected nodes might leave, say, 10 degrees of separation ? but the people are still connected. The pandemic could still spread over large swaths of society, albeit at a slower pace. Meanwhile, Havlin and his team say, many doses of vaccine could be used in parts of the networks that have already been isolated and shrunk down.

?You?ve just got to use the doses you?ve got in a more clever way,? says coauthor H. Eugene Stanley of Boston University.

To test their idea, the researchers designed a computer program that singles out the nodes that, when removed, will break up the network into parts of equal sizes. They tested it on models of several different types of networks. Their method was faster at stopping a simulated pandemic than was removing the highly connected nodes, they report.

In one of the most dramatic illustrations of their technique, the researchers simulated the spread of a pandemic using data from a Swedish study of social connections, in which more than 310,000 people are represented and connected based on whether they live in the same household or they work in the same place. With the new method, the epidemic spread to about 4 percent of the population, compared to nearly 40 percent for more standard strategies, the team reports.
 
Re: Stratagy to Stop a Pandemic

Re: Stratagy to Stop a Pandemic

only 5-50% fewer doses ? They still need 50%-95% of full doses.
When you can get vaccine for 50% you can probably also get it for 100%.

Even if anyone is vaccinated, they will hardly reduce the spread from 40% to 4%
according to other models.
I remember one model which suggested to split the scarce vaccine
into smaller doses so to decrease the cases by a small amount.


In order to increase your chances to get the vaccine, make them believe
you are a big spreader. Spreading is rewarded with vaccine.
 
Re: Stratagy to Stop a Pandemic

Re: Stratagy to Stop a Pandemic




Illustrated are three different strategies for preventing the spread of a disease across a simple social network by targeting vaccine shots to a limited number of people (nodes marked red).

Picking people at random (top) doesn?t help the others ? the disease can still spread freely. Vaccinating the people who are most connected (center) leaves a large network where the disease could still spread, while some nodes are, unnecessarily, completely isolated.

A targeted approach that partitions the network into roughly equal parts (bottom) gives a better chance at protecting the most people.
-
http://www.sciencenews.org/view/access/id/33878/name/vaccination-compare.gif
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Re: Stratagy to Stop a Pandemic

Re: Stratagy to Stop a Pandemic

Source: http://arstechnica.com/journals/science.ars/2008/07/10/amathematical-optimization-of-immunization

A mathematical optimization of immunization

By Matt Ford | Published: July 10, 2008 - 06:57AM CT

In a paper set to be published in a forthcoming issue of Physical Review Letters a team of physicists consider how to immunize a population with a minimal number of doses. This real world problem has a clear mathematical analog: namely given a graph (network of people or computers), how can you break it up while removing (immunizing) the fewest nodes (individual person or computer) possible? Since there is a simple mathematical representation, mathematicians and physicists can study and develop strategies to efficiently solve this problem. That mathematical representation can also be applied to problems beyond stopping a fast-spreading disease, as it applies to anything spreading on a node-based system, including computer viruses.

Most of the current approaches are based on what is termed a "targeted" strategy. In this type of solution, you label the nodes that have the most importance, specifically the ones that can infect the most neighbors, and immunize (remove) them first. You then continue with the remaining nodes until the network is completely disconnected or everyone is immunized. Work carried out by researchers in Boston, Israel, and Sweden has identified a new type of strategy that requires anywhere between five and 50 percent fewer immunizations.

In their upcoming letter, the authors propose what they term a "equal graph partitioning" (EGP) strategy. The idea behind this is to break graphs (meaning networks of people or computers) into many sub-networks of equal sizes. Targeted immunization strategies result in a broad distribution of sizes, including a large number of small clusters that up having immunization doses wasted on them as they are isolated. According to the authors, this is not needed in their scheme?their EGP strategy is closer to a global optimization of the problem, as opposed to local decisions that are made repetitively in the targeted strategies.

The researchers tested EGP against a variety of targeted strategies using two types of mathematical models for networks. However, to find out how well the EGP strategy could work in the real world, they used data taken from workplaces in Stockholm, a computer network, and the high energy physics citation network on arXiv. The workplace network contained 310,136 households, with 906,260 direct links among them. The EGP strategy resulted in a 30 percent advantage over non-adaptive targeted strategies, and a 15 percent advantage over the adaptive targeted strategy. Using the inter-domain level map of the Internet as a test network, which contains 20,556 nodes and 62,920 links, the EGP strategy showed a 50 percent improvement over other methods. Finally using the HEP citation network, the EGP strategy showed a 23 and 46 percent advantage over the adaptive and non-adaptive strategies, respectively.

To show how well the new EGP strategy could work if combating an infection spreading through the population, the team modeled a disease spreading through a network. The model, known as the susceptible-infectious-recovery (SIR) epidemic spreading model, steps through time and has assigned probabilities that a person/computer/node will get infected if a neighbor is infected, and a separate rate of recovery. In all the model networks examined, the fraction of nodes infected was five to 10 times lower when the EGP strategy was used when compared to the non-adaptive targeted strategy. By changing the strategy used to break up a given network by immunizing individuals, the research shows that huge reductions can be made in the number of immunization doses used.

Physical Review Letters, 2008. To be published.
 
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