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The new computer model predicts where Ebola might strike the next one



The prediction of where Ebola might strike next may be easier thanks to a new computer model. The model tracks how changes in the environment and in human societies could affect the spread of the deadly virus. He predicts that the Ebola epidemic could become 60 percent more likely by 2070 if the world continues on its way to a warmer climate and a cooling economy.

Ebola kills half of all people who contract the virus on average. In previous epidemics, mortality has risen to 90 percent. Thus, the ability to predict where Ebola may lead to a lesion further could save thousands of lives, ensuring that people are more able to spot the disease, care and take steps to stop the spread of the virus .

"The future is inherently uncertain. But politicians and decision makers want to understand the range of future opportunities, "says Christie Abby, professor of global health at the University of Washington, The Verge . "You need information on what can happen so you can be better prepared."

The model can eventually be used to figure out where to vaccinate people before an epidemic has a chance get hold of, or may allow, the government to take action along borders where sick travelers can spread the disease, David Reding, lead author of the study published today in Nature Communications tells The Verge . The model can also be modified to deal with other diseases. Redding hopes the new model will make people think about all the factors that can cause the spread of a disease like Ebola ̵

1; from changes in society to animal behavior and a changing environment. "This is something we need to do if we are to understand and deal with this kind of complex problem," says Reading.

People can catch Ebola by coming in close contact with the blood or body fluids of the infected person or animal. Scientists suspect that a fruit bat may have been behind the fires in West Africa in 2014, killing 11,325 people. The effects of climate change can change where bats and humans live, putting them in closer contact with each other. Poverty – which other studies suggest may also grow in a warming world – could also cause people to turn to more risky sources of food, including wild animals that carry Ebola. And in places where poverty can put people at greater risk, there are often no hospitals and clinics with resources to stop the spread of the disease.

As climate change becomes confused with ecosystems, scientists and doctors are worried about the epidemic of zoonotic diseases. (those that can spread from animals to humans) can become more difficult to predict. Mites and mosquitoes are already on the move, thanks to warmer temperatures, bringing with them diseases such as Lyme, Dengue and Zika. "Contactability and frequency are critical to the spread of infectious diseases," wrote Konstans Wells, an ecologist at Swansea University, at The Verge in an email.

To understand the prospects for Ebola in 2070, researchers developing the new mathematical model looked at different scenarios for how the world could work together to reduce inequality, slow population growth, and reduce greenhouse gas emissions. They saw the likelihood of new outbreaks jump unless people took action to combat each of these factors.

Their method illustrates how complicated it can be to clarify all the problems that can lead to an epidemic. To understand the existing risk of epidemics, researchers used the model to analyze climate change data, land use, population growth and poverty. He was able to identify precisely where epidemics had already erupted, such as the Democratic Republic of the Congo and Gabon. But he also mentioned places, especially in Nigeria, that have not yet seen the epidemic. This may be because the health infrastructures in these places are better prepared to meet the risk.

Whether a community has a strong health system and ways to detect and track a disease is what can differentiate a single case of Ebola and a widespread disaster, according to Daniel Bausch, a member of the Executive Committee of the American Society of Tropical Medicine . The model can just help solve this problem too. In places where there are not many resources to track where the disease is occurring, predictive models such as this could help employees and humanitarian organizations working to stop the epidemic find where to focus their attention. But first, he warns, they must have confidence in the model.

"Do I suppose that this will be something with the Eureka, which from now on we can predict all the areas in which Ebola will occur? I'm still skeptical of that, "Bausch says. "Only time will tell."


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