Disease + travel = spread

Modern travel has no doubt made it easier for diseases to spread - and spread farther.

This is nothing new in that diseases have always traveled with us, even back in the day when our best modes of transport were our feet, or a horse and cart.

Over the ages, various forms of the plague have spread with us over whole continents.

It just happens a lot faster these days.

With planes flying from one side of the world to the other in a mere 24 hours, germs are having a field day.

For now, the Ebola virus seems to be contained in western Africa.

But it has spread from Guinea - where some of the first cases drew international attention in March - to neighboring countries. In fact, since December 2013, the virus has spread to Sierra Leone, Liberia, Nigeria and Senegal.

Plotting a virus

Tools like Google Maps have enabled almost anyone to map cases of Ebola and plot the epidemic's chronology, showing the spreading of the virus in real-time.

The technology is also used by the professionals. Take HealthMap for example.

Developed by a team of researchers, epidemiologists and software developers at Boston Children's Hospital, the healthmap on Ebola provides a clickable timeline that allows you to follow the development of the epidemic.

It's interesting up to a point. But looking at the past doesn't necessary let you see the future.

Germs travel by plane

To understand how a virus spreads to the rest of the world, it is crucial to look at airports and at the flights going from one country to another, researchers say.

Flight connections can indicate how fast a germ will travel. For a virus starting in London, New York may be closer and easier to reach than a remote village in Scotland - simply because of direct air travel.

Dirk Brockmann of Humboldt University in Berlin, and Dirk Helbing of the Federal Institute of Technology in Zurich, have created a computer model that calculates how a germ will spread geographically, and which city it will probably reach first.

In the past, they have simulated the spread of the H1N1 swine flu, E. coli (EHEC), and Severe acute respiratory syndrome (SARS).

They have now adjusted their model for the Ebola outbreak in West Africa.

Brockmann, who also works for the World Health Organization, says our travel behavior is the key to determining the pattern by which a virus will spread. It's more important than the specifics of the virus.

And, says Brockmann, "when the number of cases is high, the disease can spread faster."

Ebola may reach Paris and London first

The researchers' simulation shows a network of airports, and gives the relative probability with which Ebola would reach a city in comparison to others.

Starting from an airport at Conakry in Guinea, Paris has the highest risk of becoming an entry point for Ebola: its "relative import probability" is about 8 percent.

But from Freetown in Sierra Leone, London's Gatwick and Heathrow airports face a greater risk at a combined 8.5 percent, followed by Brussels.

"In this network, we show historical factors," says Brockmann. "You could name them 'colonial fingerprints.' Several European countries still have much air traffic to their former colonies - France to Guinea, for example."

Germany, with its main international airport at Frankfurt, has a lower risk than England and France.

The probability of an Ebola patient reach another African country, such as Ghana, is higher.

But Brockmann stresses these are only relative risks.

The model can estimate that the relative risk of importing Ebola is higher for France than for Germany, but "at the moment, we cannot calculate how high the absolute probability is that there will be an Ebola case, for example, in England in the next three weeks," he says, adding that the team is working on including factors such as these into the model.

Googling symptoms

Other tools have attempted to calculate the outbreak of an epidemic, some with unconventional means.

The Internet firm Google has long said it could predict the location of flu outbreaks.

Its "Flu Trends" tool analyzes search terms and links them to IP addresses, which gives them a location. The idea being that influenza starts to spread in a city, people are more likely to search for terms such as "flu symptoms."

Google Flu Trends allows you to check the risk of flu in your area, virtually in real-time.

The company has launched a similar tool for dengue fever.

But according to a paper in the journal "Science", Google's big data technology fails to make realistic prognoses.

For instance, it missed a flu epidemic in 2009, and despite a software update, it is said to have exaggerated the risk.

As the researchers write in "Science," the tool "was predicting more than double the proportion of doctor visits for influenza-like illness than the (US) Centers for Disease Control and Prevention."

Some see this as sufficient proof that Google's approach to big data have yet to mature.

Future challenges

"Our computer simulations are already quite good," Brockmann says, "but there is one thing missing: feedback."

In addition, we often change or adapt our behavior when we perceive a threat to our lives. We might change our travel plans and stay at home based on media reports about an epidemic.

"We would like to include this feedback into our models, too," says Brockmann.

But that will take time.