TIME public health

This Technology Tracks Antibiotic Resistance In Food

healthiest foods, health food, diet, nutrition, time.com stock, spinach, greens, vegetables, salad
Danny Kim for TIME

Federal officials have created a new public database that tracks superbugs

On Wednesday, the U.S. Centers for Disease Control and Prevention (CDC) rolled out a new interactive tool that allows users to follow the spread of antibiotic resistant bugs nationwide, called NARMS (National Antimicrobial Resistance Monitoring System) Now: Human Data. According to the CDC, every year there are two million reported illnesses and 23,000 deaths associated with antibiotic resistant bacteria. Bacteria in our food accounts for 440,000 of those illnesses.

The CDC has long tracked the travel routes of four of the common types of bacteria transmitted through food: Campylobacter, E. coli O157, Salmonella, and Shigella. The data has already helped researchers investigate the distribution of multi drug resistant strains of salmonella and track down trends in resistance. For instance, the FDA withdrew approval for Enrofloxacin (a fluoroquinolone) in chickens after NARMS data revealed growing fluoroquinolone-resistant bacterial infections among Americans. Now the interactive database is free to the public to examine how these bugs have changed through the past 18 years.

“This is an educational tool for people who want to learn more about foodborne pathogens,” says Regan Rickert-Hartman, senior epidemiologist and program coordinator for NARMS. “This is [also] a good tool for health departments that are looking to compare their data to other states.”

Interactive maps, some of the most consumer-friendly aspects of the database, allow users to watch the spread and growth of antibiotic resistant bacteria like Salmonella and Campylobacter over time through the United States.

The database was launched partly in response to calls from academics, Congress and consumer groups for more transparency and better access to data on antibiotic resistance, the CDC says. Rickert-Hartman says the database is part of the agency’s response to President Obama’s Open Government Initiative to establish more participation and open collaboration.

Though the current data only goes through 2013, Rickert-Hartman says the CDC hopes to add 2014 and 2015 data by the end of the year.

TIME Natural Disasters

This Technology Could Help Predict Where Wildfires Strike Next

wildfire helicopter
Getty Images

Scientists say the technology will be in the hands of firefighters next year

The King Fire, one the most devastating forest fires of 2014, began when an arsonist bent on inflicting damage lit a small a swathe of land ablaze. But, as with all forest fires, what transformed the blaze into a disaster of record-breaking proportions was mother nature, not the human who lit the spark. In one afternoon, the fire unexpectedly spread more than 10 miles to the surprise of those fighting it.

Firefighters are familiar with the nearly unpredictable nature of forest fires. But researchers now say they could be better prepared. After years of development, Janice Coen and her colleagues at the National Center for Atmospheric Research (NCAR) say they’re preparing to launch technology that integrates data on weather, topography and other factors to predict how fires spread in a way previously unimaginable.

“With the King Fire, the operational tools didn’t really capture when the fire spread rapidly,” said Janice Coen, project scientist at NCAR. “The fire was creating its own winds and those were what was driving fire growth. Those are the types of things we can capture with this new model.”

Wildfires in the United States cost an estimated $3.5 billion dollars each year and have destroyed countless homes and communities over the past decade. This year alone a wildfire in Alaska has burned more than 5 million acres. But despite millions in funding, accurately predicting wildfire growth has remained difficult.

Read More: How Climate Change Is Making Wildfires Worse

Coen, who has an academic background in atmospheric science, said that early in her research she noticed fire patterns that resembled severe storms. Those patterns had largely escaped the attention of engineers and forest researchers, said Coen. Currently, weather forecasters pass data on anticipated weather to land agencies and scientists observing a fire. Those agencies then try to interpret how predicted weather patterns will affect a fire. The new model integrates those two steps — the weather, and the fire’s own internal patterns — and accounts for interactions between the weather patterns, the land, and the blaze itself.

The NCAR research team has received funding from NASA and has partnered with Colorado firefighters to launch the technology in 2016. Most of the tough science questions have been answered, Coen says. Now, they’re focusing on making the data accessible for firefighters on the ground.

Researchers hope that when wildfire season hits next summer, Colorado firefighters will be able to look at tablets with automatically updating data on fire growth. Once the data is in the hands of fire responders, they will be able to determine how and where a fire will grow or even predict where to allocate resources in advance of a fire.

“This is a really disruptive technology,” said Coen. “People will say it’s a perfect storm of events, it never could have been predicted: the fuel, the terrain, the weather. What I found over 20 years of working on this is that the vast majority of the distinguishing characteristics of each event can be captured with the model.”

TIME energy

The Renewable Energy Source That’s About to Boom Again

generator Hoover Dam hydropower electricity
Bloomberg—Getty Images Turbines spin inside hydroelectric generators at the Hoover Dam in Boulder City, Nevada on March 24, 2014.

'Whether we like it or not, over the next 20 years roughly the world will double its hydropower capacity'

Ten years ago hydropower might have been taken for dead in the United States. Environmentalists didn’t want hydropower dams because of the destruction they wreaked on nearby ecosystems. Energy companies had lost interest because hydropower wouldn’t produce enough energy to make the investment worthwhile. Indeed, in every decade since the 1970s, the U.S. has added less hydropower capacity than the decade prior.

But now energy experts say that new ways of thinking about hydropower has placed the energy source on the verge of a resurgence in the U.S. Hydropower production is anticipated to grow by more than 5% in 2016 alone, according to the U.S. Energy Information Administration.

“There has been more interest in the last few years. There are a lot of projects being considered,” said Rocío Uria-Martinez, an energy researcher at Oak Ridge National Laboratory. “Hydropower is, or it can be, a very viable complement for the other renewables.” The U.S. has some 80,000 dams, and only 2,000 are being used to harness electricity, according to Uria-Martinez. Adaptations to existing dams could drive a 15 to 20% increase in total hydropower capacity in the U.S. At the same time, adapting dams saves the cost of building new ones from the ground up.

While experts anticipate dramatic growth in hydropower in the coming years, don’t expect to see another Hoover Dam anytime soon. “Building large dams is almost out of the question in the U.S. and in Europe because of environmental constraints,” said Uria-Martinez. Energy policymakers have focused instead on developing sustainable hydropower dams, which are typically on a small scale. In some communities this means installing hydropower capabilities to existing dams that have never produced electricity.

In some areas, increasing dam efficiency has meant eliminating dams that harm the environment and replacing them with more sustainable ones. The Penobscot River in Maine, for instance, had several dams over hundreds of miles of river, many of which were operated inefficiently. Seven conservation groups teamed up and employed scientists to consider how to increase energy production and, at the same time, eliminate some dams. In the end, the group ended up dismantling two dams while achieving the same energy output with the remaining ones.

“We got the river to produce exactly the same amount of hydropower as before but with 1,000 km of connected river,” said Giulio Boccaletti, who runs the water program at the Nature Conservancy. He argues that similar results can be reached in other places around the world.

“Whether we like it or not, over the next 20 years, roughly, the world will double its hydropower capacity,” he said. “How do you intervene in a world where saying no to that development is simply not an option? I think there’s appetite for a more sustainable outcome.”

In the early stages of electricity production in the U.S., hydropower played an important role. Communities first used free-flowing water to harness electricity in the late 19th century. In need of electricity, communities across the country built dams to harness the power of free-flowing water during the first half of the 20th century. In the 1960s, heightened environmental consciousness piqued American interest in conservation, and hydropower quickly fell out of favor. The timing worked well as few good sites for hydropower dams remained.

TIME Solutions That Matter

See How Robotics Is Changing What It Means to Be Disabled

At the Human Engineering Research Laboratories (HERL) in Pittsburgh, Pa., veterans, engineers, doctors and researchers are working together to improve the lives of people with disabilities. Since 1994, Dr. Rory Cooper and his team have been solving everyday problems of people with disabilities and inventing new technologies to change the way people with disabilities interact with and experience the world around them

TIME Research

There’s a New Way to Predict West Nile Virus Outbreaks

Arizona Officials Battle West Nile Virus
Getty Images

Scientists are working on a promising new model

It’s peak mosquito season in the United States, which means the risk for the mosquito-borne West Nile is up. According to the U.S. Centers for Disease Control and Prevention (CDC), the agency sees the most cases of the disease between June and September.

As of July 21, 2015, the CDC reports that 33 states have reported West Nile in people, mosquitoes or animals and there have been 23 cases of West Nile in humans. Though many people with West Nile will not develop symptoms, the disease can cause inflammation of the brain or inflammation of the lining of the brain and spinal chord. Only about 1% of people will develop neurological illness from the virus. Unfortunately there are no drugs or vaccines for West Nile. Cases have been reported in every state except for Alaska and Hawaii.

Given the fact that there’s no cure or vaccine for West Nile, being able to predict when and where the disease could spread in the U.S. before it happens would be a boon for public health experts, and researchers are getting closer to that possibility. In May, scientists at the National Center for Atmospheric Research (NCAR) and the Centers for Disease Control and Prevention (CDC) published their recent findings that showed links between the weather and incidence of West Nile virus nationwide.

MORE: You Asked: Why Do Mosquitoes Always Bite Me?

The researchers analyzed associations between temperature and precipitation and higher prevalence of West Nile virus disease in the U.S. from the years 2004 to 2012. The found notable and consistent patterns among different regions in the U.S. For instance, in the East, a drier than normal fall and spring was associated with an above average number of outbreaks. But patterns looked different in the West. Weather may influence breeding patterns as well as other vectors of the disease like birds.

The researchers are now in the process of using their findings to build a model using climate data to predict the risk of West Nile Virus transmission across the U.S. “If we can predict [West Nile virus] outbreaks, we can target public health messages to high risk regions of the country. And counties will have additional information to use for deciding about when, where, and if they should do mosquito control,” says researcher Micah Hahn a scientist at NCAR and CDC.

According to NCAR scientists Andrew Monaghan and Mary Hayden, who are also working on the model, additional data sets are being considered and implemented to help the model predict the number of cases expected in each U.S. county, including land use data, demographic data, and mosquito maps.

The hope is that the CDC will eventually adopt the model. According to Monaghan, having this information could help the CDC allocate resources to places that are likely going to be the most affected. The researchers want the model to be both informative and easily digestible to the average person. It’s also possible that the model could one day be translated to work for other mosquito-borne diseases in the United States besides West Nile.

Some researchers estimate that a functioning system will be available in about a year. Others involved are more broad in their estimations: “We continue to work on it but it may be several years before we have a validated model that we can use, if we get there at all,” says Dr. Marc Fischer of the CDC. Still, those in the community remain optimistic that such a system is possible, and may be available sooner rather than later.

TIME Mental Health/Psychology

Your Phone Knows If You’re Depressed

TIME.com stock photos Social Apps iPhone
Elizabeth Renstrom for TIME

Phone data could predict with 87% accuracy whether someone had depressive symptoms

Most of us are pretty attached to our phones, and researchers are starting to figure out what that connection can tell us about our health, including our mood. In fact, your phone may be able to tell if you’re depressed even better than a self-assessment of your own depression can, according to a small new study published in the Journal of Medical Internet Research.

“We found that the more time people spend on their phones, the more likely they are to be more depressed,” says David Mohr, one of the authors of the study and director of the Center for Behavioral Intervention Technologies at Northwestern University Feinberg School of Medicine. The researchers also found that spending lots of time at home was linked to depression—and that phone data like this could predict with 87% accuracy whether someone had symptoms of depression.

Northwestern researchers recruited 28 people ages 19-58 from Craigslist and souped up their smartphones with location-and-usage monitoring software. At the start of the study, they took a standardized questionnaire that measures depressive symptoms; half of the subjects had symptoms of depression, and half did not. For two weeks, the phones tracked GPS location information every five minutes and pinged the users with questions about their mood several times a day.

The phone data the researchers collected were rich: how many places the participants visited each day, how much time they spent in each of those places and how frequently they used their phones, says Sohrob Saeb, one of the study’s authors and a postdoctoral fellow and computer scientist in preventive medicine at Feinberg. The researchers then correlated this objective data with their depression test scores.

What they hoped to find was a connection between the objective markers of behavior—such as where the people were and how often they changed locations—and their depression test results. That way, the data derived from phones could become a useful way to track depression without the user having to report how they were feeling, which is often a barrier to depression treatment, says Mohr, who has studied depression for about 20 years. “One of the things that we find over and over again is that people don’t answer questions,” he says. “In apps, they’ll respond to questions for a few days and then get tired of it.”

Mohr and his team indeed found a strong correlation between these objective markers and depression. Phone data were even better than the daily questions the users answered to predict depression test results. “People who tend to spend more time in just one or two places—like people who stay at home or go to work and go back home—are more likely to have higher depression scores,” says Mohr. When a person moved around was important, too; people who stuck to a regular pattern of movement tended to be less depressed, they found. “This fits into a larger body of clinical research showing that people with mental health problems in general, their circadian rhythms get thrown off,” Mohr says. “Usually it’s looked at with sleep and activity, but here we’re seeing it also in terms of their movement through geographic space.” When people get depressed, he says, their mood may pull them off their routine.

Depressed people, too, spent an average of 68 minutes using their phones each day, while people without depression only spent about 17 minutes on their phones. The software didn’t track what people did on their phones—just whether or not they were using it. But the authors have some ideas about why they saw phone activity rise with depression. “One of the things we see when people are depressed is that people tend to start avoiding tasks or things they have to do, particularly when they’re uncomfortable,” Mohr explains. “Using the phone, going in and using an app, is kind of a distraction.”

It’s preliminary research, but Mohr hopes to add to the number of smartphone sensors and use these to subtly help manage depression and spot it more quickly, without requiring any work on behalf of the user. “Being able to get people timely treatment for depression is a critical failure point in public health right now,” Mohr says. An app that people download on their phones—without having to answer any questions—may help pinpoint their depressive states more effectively and help them get treatment.

TIME Solutions That Matter

Graphene: The Material Of Tomorrow

Meet the wonder material that is one hundred thousandth of the thickness of a human hair, yet is a hundred times stronger than steel. Graphene has been called "the most exciting material of the 21st century," yet we have barely scratched the surface of what it is capable of doing

TIME Innovation

Here’s How IBM Is Helping Towns Predict Disasters

Widespread Damage And Casualties After Tornadoes Rip Through South
Joe Raedle—Getty Images An ominous looking cloud hangs above the remains of a home that was destroyed by a tornado on April 29, 2014 in Tupelo, Mississippi.

IBM's new prediction tool marries live weather forecasts with a hyperlocal map, painting in yellow and red the damages to come

Every city has what emergency response crews call its “critical assets.” They’re roads, power stations, water pumps and pipes — the collective infrastructure that has the power to keep a city humming or bring life to a grinding halt. The question for city officials is when and if, exactly, these critical assets might fail. In severe weather, they typically find out the hard way.

Just this week, severe thunderstorms and twisters forced more than 15 fire departments and rescue teams to fan out across central Illinois. With crews already stretched thin, forecasters are predicting another round of severe weather to pummel the region. Help might arrive sooner if emergency crews had a map of which roads would become treacherous, which power stations would fail and which water mains would burst before the storm rolled into town.

That, in a nutshell, is the pitch for a new predictive tool IBM has unveiled in partnership with The Weather Company, parent company of The Weather Channel. Dubbed the “Intelligent Operations Center for Emergency Management,” the new platform marries live weather forecasts with a hyperlocal map of a city’s infrastructure, painting in yellow and red the predicted damage to come. With natural and man-made catastrophes taking 7,700 lives and upwards of $110 billion in damages in 2014, according to estimates by insurer Swiss Re, the market potential for a predictive tool is promising, to say the least.

The new software comes amid a surge of investment in big data solutions for public safety. Startup Mark43 is digitizing police records in an attempt to map out criminal networks. Motorola is embedding sensors into equipment used by police and fire crews to tap into a live feed of data from first responders. But IBM’s solution marks perhaps the most ambitious attempt to tease out the underlying order of chaotic events.

“I’m watching how those assets are affected to figure out, ‘Where do you begin?'” says Stephen Russo, director of emergency management solutions at IBM. “How do you get the biggest bang for your efforts?'”

Russo and his team used historic data from natural catastrophes to ascertain the breakpoint of certain assets — like when a power line might snap under high winds. The risk of failure is often as capricious as the wind itself. “It’s not a linear relationship,” says Russo. Power outages rise exponentially as wind speeds climbs from 20 mph to 40 mph, for instance. “When it goes from 40 to 50 mph, the amount of outages is much greater,” says Russo. And that’s the easy part of IBM’s catastrophic calculus.

Harder still is predicting which critical buildings — hospitals, schools and shelters — are most likely to suffer an outage. Throw in a few more assets, and no human statistician could ever hope to calculate the odds. IBM is betting machine learning algorithms can weigh the probabilities of failure in an instant and spit out ever-changing snapshots of disaster zones.

“Everybody sees the same picture of what’s going on,” says Mark Gildersleeve, president of The Weather Company’s professional division. “You’ve quantified the impact. Objectively, what are the areas most under stress?”

IBM envisions the map as a canvas for communication between emergency workers in command centers and various outposts. Top officials will have a bird’s eye view of relief efforts, while field workers can populate the map with reports from the ground. In its most ambitious form, the map could scale out to actors in the private sector. “The big ox carriers during a disaster, the Walmart’s and Costco’s, could position their resources more accurately and take more of the burden off of the public sector, so that they are not having to stock a warehouse full of water and generators.” Insurers might also use the maps to send pinpointed alerts to policyholders, warning them to cover their cars, for instance, before a hail storm bursts overhead.

But first, IBM will have to get all of those myriad actors on board its system. Fortunately for its sales team, the platform doubles as an ordinary emergency response system, recording routine traffic accidents as well as nature’s most brutal events. But the true test of the system will come at those unpredictable moments when the skies open up and the earth shakes beneath users’ feet. With severe thunderstorms threatening 50 million Americans this week, the solution can’t come soon enough.

TIME Transportation

How Uber and Lyft Are Trying to Solve America’s Carpooling Problem

Rush hour in Los Angeles
Getty Images Rush hour in Los Angeles

These startups are finally starting to look like true ride-sharing services

First, the bad news: carpooling has been on the decline in America for nearly four decades. That practice could be helping the environment and America’s commuters, who are needlessly stuck for hours each day on packed highways. Multiple people sharing a single ride to a common destination is a simple act that has the potential to reduce CO2 emissions, ease traffic, lessen fossil fuel dependency, reduce stress on commuters, and even drive down rents in dense cities. Yet the practice fell out of favor after reaching a peak in the 1970s.

Now, the good news: popular tech companies Lyft and Uber are leading a wave of new services that have the potential to revive shared rides. “What fascinates me about these things is: can they move us closer toward a vision of an integrated public transit system?” asks Susan Shaheen, co-director of the Transportation Sustainability Research Center at the University of California, Berkeley. “And can it move us closer to filling empty seats in vehicles?”

Despite referring to themselves as “ride-sharing” companies, Lyft and Uber have largely been in the business of what transportation experts call “ride-sourcing,” because they essentially provide the same service as taxis through their own platforms. “I’ve studied ride-sharing for a long time, and the definition of ride-sharing is really carpooling,” Shaheen says. “And a carpool is an incidental trip.” That is, it’s a trip that a driver was going to take regardless of whether anyone else was with them in that car.

The distinction isn’t just academic. When the thousands of drivers working for Uber and Lyft in San Francisco are picking up a single fare and taking them from Point A to Point B, it’s probable that they’re adding to unnecessary congestion, pollution and fuel consumption. But last summer, within hours of each other, the companies announced that they were rolling out UberPOOL and Lyft Line in San Francisco, passenger-pooling options that would give riders cheaper fares if they’d be willing to share their vehicle with strangers traveling a similar route.

The companies say customer interest has been high so far. Each company has since expanded the service to Austin, Los Angeles, and New York City, and Uber has launched POOL in Paris. Lyft says that 50% of rides in San Francisco are Lyft Line rides, and a little more than 20% of all Lyft rides in the city start or end within a quarter mile of commuter rail stops. “That’s notable,” says Shaheen. “It means people are taking this short trip in one of these vehicles and connecting it to a longer line-haul transit trip. It’s basically enabling somebody to not take a single-occupant vehicle for this long commute trip and to rethink how they commute.”

Uber crunched the numbers on their “matched trips” for one month in San Francisco, comparing them to the number of miles that vehicles would have traveled if all those rides had been taken individually. They estimated that UberPOOL rides taken between February and March amounted to 674,000 miles of saved driving. That’s the equivalent of 240 people driving round trip from L.A. to New York. “UberPOOL is really about trying to reinvent cities from a transportation perspective,” says product manager Brian Tolkin. “Part of that means making Uber so affordable that it’s really available to anyone and a better alternative to, say, owning a car.”

Of course, before these companies start patting themselves on the back for saving the environment, they have to offset the number of cars they’ve brought onto the road. They aren’t releasing data about that, and there is other crucial information missing. The most important piece, Shaheen says, is knowing what the people using these services were doing beforehand. If someone is now using a combination of Lyft Line and public rail rather that driving alone in a car from San Francisco to Cupertino, that represents a greater environmental offset than if that person was previously taking public rail and a public bus.

Carpooling took off in America during World War II, when the government asked people to start sharing rides to work so they could conserve rubber for the war effort. The practice gained popularity through the 1970s, spurred by volatile energy prices, employer-sponsored programs and the advent of HOV lanes. But as gas prices dropped, cars got cheaper and more people and companies decamped for far-out suburbs and exurbs, more workers began taking their own cars to the office. Carpooling became associated with the inconveniences of neighbors’ inflexible schedules, awkward reimbursements and a lack of privacy. Nearly one in four people shared a ride to work in the 1970s. By the time census workers asked about that practice in 2010, the number had dropped to about 10%.

It’s too early to tell if Lyft and Uber’s early efforts will reverse that trend. But it is clear that they are benefiting from a changed landscape. Smartphone ownership has exploded, allowing people to connect and share useful information about where they are. Familiarity with social networks can encourage strangers to trust each other. The algorithms matching riders and drivers—while keeping routes convenient—are constantly improving. And though car sales have continued to climb in recent years, younger urban residents say they’re less interested in driving and owning their own vehicle.

Perhaps the most promising trend line for these services is that Uber and Lyft are finally solving the problem that has derailed past attempts to solve America’s carpooling problem with technology. “When you have a new system with a really small number of people in it—which any system will when it’s new—there’s a very, very low probability that you’ll have a match between all the potential origins and destinations of a driver and a passenger,” says Emily Castor, Lyft’s director of transportation policy. “So those systems that had tried to do that have been pretty uniformly unsuccessful, because they have a high failure rate.”

That’s what happened to Zimride, an early incarnation of Lyft. Among the key lessons for Zimride’s founders when they rebranded as Lyft: always have drivers available, lest you deter potential customers. “We’ve been able to build up a network that has enough density that it actually is getting to the point now where we do have a ton of people using it,” says Castor. “So we’ve kind of overcome that chicken and egg problem and we now can start doing really interesting things.”

Those experiments include “Driver Destination,” which allows a driver to specify where they’re headed and signal that they’re available to pick someone up. The app will only link the driver to a passenger going the same way. This type of trip can help eliminate wasted space in cars and potentially keep superfluous cars off the road–an efficiency that experts like Shaheen call the holy grail. “The next phase for Lyft is to look at how we can increase that commuter carpooling activity and to expand on our vision to make it so any time any driver is on the road, [they] can be using the empty seats in their cars to give rides to other people,” Castor says.

The key to long-term success may be money. For these services to truly take hold, drivers will need to see the upside of bringing a few strangers along for the ride. Old-fashioned carpooling was set up for passengers to reimburse a driver for just gas and wear and tear, a piddling bit of change per mile. “That’s just not enough to make people notice and think about doing that,” Castor says. “But if you could earn $15 on your way to work and your way home, that would probably raise your eyebrows.”

Your browser is out of date. Please update your browser at http://update.microsoft.com