GLEAM – An Epidemic Forecaster
As the world becomes more connected, and travel becomes even easier, it is especially important to be able to understand and prevent the spread of dangerous and highly contagious diseases. Without an understanding of networks and disease epidemics, the world’s population could quickly become infected with a strain of virus. In an article done by the online magazine “Wired”, journalist Marcus Woo explores the science of viral outbreaks, and the prediction model that modern epidemiologists use to inform policy makers of possible steps toward prevention.
This prediction model is known as GLEAM, and is an epidemic forecaster that has been in the works for almost 10 years. It uses parameters such as population, travel habits and patterns, characteristics of the disease and travel restrictions, in order to output a hypothetical roadmap to how the disease will spread around the world. Although the prediction is never perfect, it provides a good idea of how the disease could spread, and gives an idea of how deadly the contagion could be. These hypothetical predictions provide policy makers with the insight to make more informed decisions when it comes to deciding how to deal with the contagion.
Similarly, these predictions and resulting network graphs can help epidemiologists, as well as health care workers in suggesting preventative measures to the virus spreading. By doing this, the probability of an infected person passing on the virus to a healthy individual will decrease. This allows the R0 of the disease to decrease, making an epidemic less likely.
Unfortunately, Woo continues by stating that these prediction models are not enough to fully prevent possible pandemics. In many cases, although scientists know how the virus will spread, their input is limited by outside constraints. For example, if a vaccine hasn’t been created yet, there is no way to administer it to healthy individuals to prevent further contagion. Similarly, the adoption of preventative policies might vary country by country, with some being more difficult to fully implement quickly. These outside factors also determine whether a disease will continue to grow or die out.
In the end, it is important to understand that although there are many external factors that affect disease spread, the knowledge of how to model predictions for epidemics vastly improves our chances of such diseases dying out.
Source: See Diseases Spread Mesmerizing Graphics