Scientists Are Building an “AccuWeather” for Germs to Predict Your Risk of Getting the Flu
Applied mathematician Sara del Valle works at the U.S.'s foremost nuclear weapons lab: Los Alamos. Once colloquially called Atomic City, it's a hidden place 45 minutes into the mountains northwest of Santa Fe. Here, engineers developed the first atomic bomb.
Like AccuWeather, an app for disease prediction could help people alter their behavior to live better lives.
Today, Los Alamos still a small science town, though no longer a secret, nor in the business of building new bombs. Instead, it's tasked with, among other things, keeping the stockpile of nuclear weapons safe and stable: not exploding when they're not supposed to (yes, please) and exploding if someone presses that red button (please, no).
Del Valle, though, doesn't work on any of that. Los Alamos is also interested in other kinds of booms—like the explosion of a contagious disease that could take down a city. Predicting (and, ideally, preventing) such epidemics is del Valle's passion. She hopes to develop an app that's like AccuWeather for germs: It would tell you your chance of getting the flu, or dengue or Zika, in your city on a given day. And like AccuWeather, it could help people alter their behavior to live better lives, whether that means staying home on a snowy morning or washing their hands on a sickness-heavy commute.
Sara del Valle of Los Alamos is working to predict and prevent epidemics using data and machine learning.
Since the beginning of del Valle's career, she's been driven by one thing: using data and predictions to help people behave practically around pathogens. As a kid, she'd always been good at math, but when she found out she could use it to capture the tentacular spread of disease, and not just manipulate abstractions, she was hooked.
When she made her way to Los Alamos, she started looking at what people were doing during outbreaks. Using social media like Twitter, Google search data, and Wikipedia, the team started to sift for trends. Were people talking about hygiene, like hand-washing? Or about being sick? Were they Googling information about mosquitoes? Searching Wikipedia for symptoms? And how did those things correlate with the spread of disease?
It was a new, faster way to think about how pathogens propagate in the real world. Usually, there's a 10- to 14-day lag in the U.S. between when doctors tap numbers into spreadsheets and when that information becomes public. By then, the world has moved on, and so has the disease—to other villages, other victims.
"We found there was a correlation between actual flu incidents in a community and the number of searches online and the number of tweets online," says del Valle. That was when she first let herself dream about a real-time forecast, not a 10-days-later backcast. Del Valle's group—computer scientists, mathematicians, statisticians, economists, public health professionals, epidemiologists, satellite analysis experts—has continued to work on the problem ever since their first Twitter parsing, in 2011.
They've had their share of outbreaks to track. Looking back at the 2009 swine flu pandemic, they saw people buying face masks and paying attention to the cleanliness of their hands. "People were talking about whether or not they needed to cancel their vacation," she says, and also whether pork products—which have nothing to do with swine flu—were safe to buy.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place.
They watched internet conversations during the measles outbreak in California. "There's a lot of online discussion about anti-vax sentiment, and people trying to convince people to vaccinate children and vice versa," she says.
Today, they work on predicting the spread of Zika, Chikungunya, and dengue fever, as well as the plain old flu. And according to the CDC, that latter effort is going well.
Since 2015, the CDC has run the Epidemic Prediction Initiative, a competition in which teams like de Valle's submit weekly predictions of how raging the flu will be in particular locations, along with other ailments occasionally. Michael Johannson is co-founder and leader of the program, which began with the Dengue Forecasting Project. Its goal, he says, was to predict when dengue cases would blow up, when previously an area just had a low-level baseline of sick people. "You'll get this massive epidemic where all of a sudden, instead of 3,000 to 4,000 cases, you have 20,000 cases," he says. "They kind of come out of nowhere."
But the "kind of" is key: The outbreaks surely come out of somewhere and, if scientists applied research and data the right way, they could forecast the upswing and perhaps dodge a bomb before it hit big-time. Questions about how big, when, and where are also key to the flu.
A big part of these projects is the CDC giving the right researchers access to the right information, and the structure to both forecast useful public-health outcomes and to compare how well the models are doing. The extra information has been great for the Los Alamos effort. "We don't have to call departments and beg for data," says del Valle.
When data isn't available, "proxies"—things like symptom searches, tweets about empty offices, satellite images showing a green, wet, mosquito-friendly landscape—are helpful: You don't have to rely on anyone's health department.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place. But del Valle wants more than weekly numbers on a government website; she wants that weather-app-inspired fortune-teller, incorporating the many diseases you could get today, standing right where you are. "That's our dream," she says.
This plot shows the the correlations between the online data stream, from Wikipedia, and various infectious diseases in different countries. The results of del Valle's predictive models are shown in brown, while the actual number of cases or illness rates are shown in blue.
(Courtesy del Valle)
The goal isn't to turn you into a germophobic agoraphobe. It's to make you more aware when you do go out. "If you know it's going to rain today, you're more likely to bring an umbrella," del Valle says. "When you go on vacation, you always look at the weather and make sure you bring the appropriate clothing. If you do the same thing for diseases, you think, 'There's Zika spreading in Sao Paulo, so maybe I should bring even more mosquito repellent and bring more long sleeves and pants.'"
They're not there yet (don't hold your breath, but do stop touching your mouth). She estimates it's at least a decade away, but advances in machine learning could accelerate that hypothetical timeline. "We're doing baby steps," says del Valle, starting with the flu in the U.S., dengue in Brazil, and other efforts in Colombia, Ecuador, and Canada. "Going from there to forecasting all diseases around the globe is a long way," she says.
But even AccuWeather started small: One man began predicting weather for a utility company, then helping ski resorts optimize their snowmaking. His influence snowballed, and now private forecasting apps, including AccuWeather's, populate phones across the planet. The company's progression hasn't been without controversy—privacy incursions, inaccuracy of long-term forecasts, fights with the government—but it has continued, for better and for worse.
Disease apps, perhaps spun out of a small, unlikely team at a nuclear-weapons lab, could grow and breed in a similar way. And both the controversies and public-health benefits that may someday spin out of them lie in the future, impossible to predict with certainty.
Waste smothering our oceans is worth billions – here’s what we can do with all that sh$t
There’s hardly a person out there who hasn’t heard of the Great Pacific Garbage Patch. That type of pollution is impossible to miss. It stares you in the face from pictures and videos of sea turtles with drinking straws up their noses and acres of plastic swirling in the sea.
It demands you to solve the problem—and it works. The campaign to raise awareness about plastic pollution in the oceans has resulted in new policies, including bans on microplastics in personal care products, technology to clean up the plastic, and even new plastic-like materials that are better for the environment.
But there’s a different type of pollution smothering the ocean as you read this. Unfortunately, this one is almost invisible, but no less damaging. In fact, it’s even more serious than plastic and most people have no idea it even exists. It is literally under our noses, destroying our oceans, lakes, and rivers – and yet we are missing it completely while contributing to it daily. In fact, we exacerbate it multiple times a day—every time we use the bathroom.
It is the way we do our sewage.
Most of us don’t think much about what happens after we flush the toilet. Most of us probably assume that the substances we flush go “somewhere” and are dealt with safely. But we typically don’t think about it beyond that.
Most of us also probably don’t think about what’s in the ocean or lakes we swim in. Since others are swimming, jumping in is just fine. But our waterways are far from clean. In fact, at times they are incredibly filthy. In the US, we are dumping 1.2 trillion of gallons of untreated sewage into the environment every year. Just New York City alone discharges 27 billion gallons into the Hudson River basin annually.
How does this happen? Part of it is the unfortunate side effect of our sewage system design that dates back to over a century ago when cities were smaller and fewer people were living so close together.
Back then, engineers designed the so-called “combine sewer overflow systems,” or CSOs, in which the storm water pipes are connected to the sanitary sewer pipes. In normal conditions, the sewage effluent from homes flows to the treatment plants where it gets cleaned and released into the waterways. But when it rains, the pipe system becomes so overwhelmed with water that the treatment plant can’t process it fast enough. So the treatment plant has to release the excess water through its discharge pipes—directly, without treatment, into streams, rivers and the ocean.
The 1.2 trillion gallons of CSO releases isn’t even the full picture. There are also discharges from poorly maintained septic systems, cesspools and busted pipes of the aging wastewater infrastructure. The state of Hawaii alone has 88,000 cesspools that need replacing and are currently leaking 53 million gallons of raw sewage daily into their coastal waters. You may think twice about swimming on your Hawaii vacations.
Overall, the US is facing a $271 billion backlog in wastewater infrastructure projects to update these aging systems. Across the Western world, countries are facing similar challenges with their aging sewage systems, especially the UK and European Union.
That’s not to say that other parts of the planet are in better shape. Out of the 7+ billion people populating our earth, 4.2 billion don’t have access to safe sanitation. Included in this insane number are roughly 2 billion people who have no toilet at all. Whether washed by rains or dumped directly into the waterways, a lot of this sludge pollutes the environment, the drinking water, and ultimately the ocean.
Pipes pour water onto a rocky shore in Jakarta, Indonesia.
Tom Fisk
What complicates this from an ocean health perspective is that it’s not just poop and pee that gets dumped into nearby waterways. It is all the things we put in and on our bodies and flush down our drains. That vicious mix of chemicals includes caffeine, antibiotics, antidepressants, painkillers, hormones, microplastics, cocaine, cooking oils, paint thinners, and PFAS—the forever chemicals present in everything from breathable clothing to fire retardant fabrics of our living room couches. Recent reports have found all of the above substances in fish—and then some.
Why do we allow so much untreated sewage spill into the sea? Frankly speaking, for decades scientists and engineers thought that the ocean could handle it. The mantra back then was “dilution is the solution to pollution,” which might’ve worked when there were much fewer people living on earth—but not now. Today science is telling us that this old approach doesn’t hold. That marine habitats are much more sensitive than we had expected and can’t handle the amount of wastewater we are discharging into them.
The excess nitrogen and phosphorus that the sewage (and agricultural runoff) dumps into the water causes harmful algal blooms, more commonly known as red or brown tides. The water column is overtaken by tiny algae that sucks up all the oxygen from the water, creating dead zones like the big fish kills in the Gulf of Mexico. These algae also cause public health issues by releasing gases toxic to people and animals, including dementia, neurological damage, and respiratory illness. Marshes and mangroves end up with weakened root systems and start dying off. In a wastewater modeling study I published last year, we found that 31 percent of salt marshes globally were heavily polluted with human sewage. Coral reefs get riddled with disease and overgrown by seaweed.
We could convert sewage into high-value goods. It can be used to generate electricity, fertilizer, and drinking water. The technologies not only exist but are getting better and more efficient all the time.
Moreover, by way of our sewage, we managed to transmit a human pathogen—Serratia marcescens, which causes urinary, respiratory and other infections in people—to corals! Recent reports from the Florida Keys are showing white pox disease popping up in elk horn corals caused by S.marcescens, which somehow managed to jump species. Many recent studies have documented just how common this type of pollution is across the globe.
Yet, there is some good news in that abysmal sewage flow. Just like with plastic pollution, realizing that there’s a problem is the first step, so awareness is key. That’s exactly why I co-founded Ocean Sewage Alliance last year—a nonprofit that aims to “re-potty train the world” by breaking taboos in talking about the poop and pee problem, as well as uniting experts from various key sectors to work together to end sewage pollution in coastal areas.
To end this pollution, we have to change the ways we handle our sewage. Even more exciting is that by solving the sewage problem we can create all sorts of economic benefits. In 2015, human poop was valued at $9.5 billion a year globally, which today would be $11.5 billion per year.
What would one do with that sh$t?
We could convert it into high-value goods. Sewage can be used to generate electricity, fertilizer, and drinking water. The technologies not only exist but are getting better and more efficient all the time. Some exciting examples include biodigesters and urine diversion (or peecycling) systems that can produce fertilizer and biogas, essentially natural gas. The United Nations estimates that the biogas produced from poop could provide electricity for 138 million homes. And the recovered and cleaned water can be used for irrigation, laundry and flushing toilets. It can even be refined to the point that it is safe for drinking water – just ask the folks in Orange County, CA who have been doing so for the last few decades.
How do we deal with all the human-made pollutants in our sewage? There is technology for that too. Called pyrolysis, it heats up sludge to high temperatures in the absence of oxygen, which causes most of the substances to degrade and fall apart.
There are solutions to the problems—as long as we acknowledge that the problems exist. The fact that you are reading this means that you are part of the solution already. The next time you flush your toilet, think about where this output may flow. Does your septic system work properly? Does your local treatment plant discharge raw sewage on rainy days? Can that plant implement newer technologies that can upcycle waste? These questions are part of re-potty training the world, one household at a time. And together, these households are the force that can turn back the toxic sewage tide. And keep our oceans blue.
The U.S. must fund more biotech innovation – or other countries will catch up faster than you think
The U.S. has approximately 58 percent of the market share in the biotech sector, followed by China with 11 percent. However, this market share is the result of several years of previous research and development (R&D) – it is a present picture of what happened in the past. In the future, this market share will decline unless the federal government makes investments to improve the quality and quantity of U.S. research in biotech.
The effectiveness of current R&D can be evaluated in a variety of ways such as monies invested and the number of patents filed. According to the UNESCO Institute for Statistics, the U.S. spends approximately 2.7 percent of GDP on R&D ($476,459.0M), whereas China spends 2 percent ($346,266.3M). However, investment levels do not necessarily translate into goods that end up contributing to innovation.
Patents are a better indication of innovation. The biotech industry relies on patents to protect their investments, making patenting a key tool in the process of translating scientific discoveries that can ultimately benefit patients. In 2020, China filed 1,497,159 patents, a 6.9 percent increase in growth rate. In contrast, the U.S. filed 597,172, a 3.9 percent decline. When it comes to patents filed, China has approximately 45 percent of the world share compared to 18 percent for the U.S.
So how did we get here? The nature of science in academia allows scientists to specialize by dedicating several years to advance discovery research and develop new inventions that can then be licensed by biotech companies. This makes academic science critical to innovation in the U.S. and abroad.
Academic scientists rely on government and foundation grants to pay for R&D, which includes salaries for faculty, investigators and trainees, as well as monies for infrastructure, support personnel and research supplies. Of particular interest to academic scientists to cover these costs is government support such as Research Project Grants, also known as R01 grants, the oldest grant mechanism from the National Institutes of Health. Unfortunately, this funding mechanism is extremely competitive, as applications have a success rate of only about 20 percent. To maximize the chances of getting funded, investigators tend to limit the innovation of their applications, since a project that seems overambitious is discouraged by grant reviewers.
Considering the difficulty in obtaining funding, the limited number of opportunities for scientists to become independent investigators capable of leading their own scientific projects, and the salaries available to pay for scientists with a doctoral degree, it is not surprising that the U.S. is progressively losing its workforce for innovation.
This approach affects the future success of the R&D enterprise in the U.S. Pursuing less innovative work tends to produce scientific results that are more obvious than groundbreaking, and when a discovery is obvious, it cannot be patented, resulting in fewer inventions that go on to benefit patients. Even though there are governmental funding options available for scientists in academia focused on more groundbreaking and translational projects, those options are less coveted by academic scientists who are trying to obtain tenure and long-term funding to cover salaries and other associated laboratory expenses. Therefore, since only a small percent of projects gets funded, the likelihood of scientists interested in pursuing academic science or even research in general keeps declining over time.
Efforts to raise the number of individuals who pursue a scientific education are paying off. However, the number of job openings for those trainees to carry out independent scientific research once they graduate has proved harder to increase. These limitations are not just in the number of faculty openings to pursue academic science, which are in part related to grant funding, but also the low salary available to pay those scientists after they obtain their doctoral degree, which ranges from $53,000 to $65,000, depending on years of experience.
Thus, considering the difficulty in obtaining funding, the limited number of opportunities for scientists to become independent investigators capable of leading their own scientific projects, and the salaries available to pay for scientists with a doctoral degree, it is not surprising that the U.S. is progressively losing its workforce for innovation, which results in fewer patents filed.
Perhaps instead of encouraging scientists to propose less innovative projects in order to increase their chances of getting grants, the U.S. government should give serious consideration to funding investigators for their potential for success -- or the success they have already achieved in contributing to the advancement of science. Such a funding approach should be tiered depending on career stage or years of experience, considering that 42 years old is the median age at which the first R01 is obtained. This suggests that after finishing their training, scientists spend 10 years before they establish themselves as independent academic investigators capable of having the appropriate funds to train the next generation of scientists who will help the U.S. maintain or even expand its market share in the biotech industry for years to come. Patenting should be given more weight as part of the academic endeavor for promotion purposes, or governmental investment in research funding should be increased to support more than just 20 percent of projects.
Remaining at the forefront of biotech innovation will give us the opportunity to not just generate more jobs, but it will also allow us to attract the brightest scientists from all over the world. This talented workforce will go on to train future U.S. scientists and will improve our standard of living by giving us the opportunity to produce the next generation of therapies intended to improve human health.
This problem cannot rely on just one solution, but what is certain is that unless there are more creative changes in funding approaches for scientists in academia, eventually we may be saying “remember when the U.S. was at the forefront of biotech innovation?”