A mosquito under the microscope.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Our daily soundscape is a cacophony of earsplitting jets, motorcycles, and construction sites. Engineers know how to eliminate and control noise, but other countries are ahead of the U.S. when it comes to keeping the quiet - with related health benefits.
Walker ultimately sold her craft equipment and returned to school to study public health. Today she is assistant professor of epidemiology and director of the Community Noise Lab at the Brown University School of Public Health. “We treat noise like a first world problem—like a sacrifice we should have to make for modern conveniences. But it’s a serious environmental stressor,” she asserts.
Our daily soundscape is a cacophony of earsplitting jets, motorcycles, crying babies, construction sites or gunshots if you’re in the military. Noise exposure is the primary cause of preventable hearing loss. Researchers have identified links between excessive noise and a heightened risk of heart disease, metabolic disorders, anxiety, depression, sleep disorders, and impaired cognition. Even wildlife suffers. Blasting oil drills and loud shipping vessels impede the breeding, feeding and migration of whales and dolphins.
At one time, the federal government had our back… and our ears. Congress passed the Noise Control Act in 1972. The Environmental Protection Agency set up the Office of Noise Abatement and Control (ONAC) to launch research, explore solutions and establish noise emission standards. But ONAC was defunded in 1981 amidst a swirl of antiregulatory sentiment.
Impossibly Loud and Unhealthy
Daniel Fink. a physician, WHO consultant, and board chair of The Quiet Coalition, a program of the nonprofit Quiet Communities, likens the effect of noise to the invisible but cumulative harm of second-hand smoke. About 1 in 4 adults in the U.S. who report excellent to good hearing already have some hearing loss. The injury can happen after one loud concert or from years with a blaring TV. Some people are more genetically susceptible to noise-related hearing loss than others.
“People say noise isn’t a big deal but it bothers your body whether you realize it or not,” says Ted Rueter, director of Noise Free America: A Coalition to Promote Quiet. Noise can chip away at your ears or cardiovascular system even while you’re sleeping. Rueter became a “quiet advocate” while a professor at UCLA two decades ago. He was plagued by headaches, fatigue and sleep deprivation caused by the hubbub of Los Angeles, he says.
The louder a sound is, and the longer you are exposed to it, the more likely it will cause nerve damage and harmful fluid buildup in your inner ear. Normal speech is 50-60 decibels (dBs). The EPA recommends that 24-hour exposure to noise should be no higher than 70 weighted decibels over 24 hours (weighted to approximate how the human ear perceives the sound) to prevent hearing loss but a 55 dB limit is recommended to protect against other harms from noise, too.
The decibel scale is logarithmic. That means 80 dB is 10 times louder than 70 dB. Trucks and motorcycles run 90 dBs. A gas-powered leaf blower, jackhammer or snow blower will cost you 100 dBs. A rock concert is in the 110 dB range. Aircraft takeoffs or sirens? 120 dBs.
Walker, the Brown professor, says that sound measurements often use misleading metrics, though, because they don’t include low frequency sound that disturb the body. The high frequency of a screeching bus will register in decibels but the sound that makes your chest reverberate is not accounted for, she explains. ‘How loud?’ is a superficial take when it comes to noise, Walker says.
After realizing the impact of noise on her own health, Erica Walker was inspired to change careers and become director of the Community Noise Lab at the Brown University School of Public Health.
Erica Walker
Fink adds that the extent to which noise impairs hearing is underestimated. People assume hearing loss is due to age but it’s not inevitable, he says. He cites studies of older people living in quiet, isolated areas who maintain excellent hearing. Just like you can prevent wrinkles by using sunscreen, you can preserve hearing by using ear plugs when attending fireworks or hockey games.
You can enable push notifications on a Smart Watch to alert you at a bar exceeding healthy sound levels. Free apps like SoundPrint, iHEARu, or NoiseTube can do decibel checks, too, but you don’t need one, says Fink. “If you can’t carry a conversation at normal volume, it’s too loud and your auditory health is at risk,” he says.
About 40 million U.S. adults, ages 20-69, have noise-induced hearing loss. Fink is among them after experiencing tinnitus (ringing or buzzing in the ears) on leaving a raucous New Year’s Eve party in 2007. The condition is permanent and he wears earplugs now for protection.
Fewer are aware of the link between noise pollution and heart disease. Piercing noise is stressful, raising blood pressure and heart rate. If you live near a freeway or constantly barking dog, the chronic sound stress can trigger systemic inflammation and the vascular changes associated with heart attacks and stroke.
Researchers at Rutgers University’s Robert Wood Johnson Medical School, working with data from the state’s Bureau of Transportation, determined that 1 in 20 heart attacks in New Jersey during 2018 were due to noise from highways, trains and air traffic. That’s 800 heart attack hospitalizations in the state that year.
Another study showed that incidence of hypertension and hardening arteries decreased during the Covid-19 air lockdown among Poles in Krakow routinely exposed to aircraft noise. The authors, comparing their pre-pandemic 2015 results to 2020 data, concluded it was no coincidence.
Mental health takes a hit, too. Chronic noise can provoke anxiety, depression and violence. Cognitively, there is ample evidence that noise disturbance lowers student achievement and worker productivity, and hearing loss among older people can speed up cognitive decline.
Noise also contributes to health disparities. People in neighborhoods with low socioeconomic status and a higher percentage of minority residents bear the brunt of noise. Affluent people have the means to live far from airports, factories, and honking traffic.
Out, Out, Damn Noise
Europe is ahead of the U.S. in tackling noise pollution. The World Health Organization developed policy guidelines used by the European Environment Agency to establish noise regulations and standards, and progress reports are issued.
Americans are relying too much on personal protective equipment (PPE) instead of eliminating or controlling noise. The Centers of Disease Control and Prevention rank PPE as the least useful response. Earplugs and muffs are effective, says Walker, but these devices are “a band-aid on a waterfall.”
Editing out noise during product design is the goal. Engineers have an arsenal of techniques and know-how for that. The problem is that these solutions aren’t being applied.
A better way to lower the volume is by maintaining or substituting equipment intended for common use. Piercing building alarms can be replaced with visual signals that flash alerts. Clanking chain and gear drives can be swapped out with belt drives. Acoustical barriers can wall off highway noise. Hospitals can soften beeping monitors and limit loudspeaker blasts. Double paned windows preserve quiet.
Editing out noise during product design is the goal. Engineers have an arsenal of techniques and know-how for that. The problem is that these solutions aren’t being applied, says Jim Thompson, an engineer and editor of the Noise Control Engineering Journal, published by the Institute of Noise Control Engineering of the USA
Engineers have materials to insulate, absorb, reflect, block, seal or diffuse noise. Building walls can be padded. Metal gears and parts can be replaced with plastic. Clattering equipment wheels can be rubberized. In recent years, building certifications such as LEED have put more emphasis on designs that minimize harmful noise.
Walker faults urban planners, too. A city’s narrow streets and taller buildings create a canyon effect which intensifies noise. City planners could use bypasses, rerouting, and other infrastructure strategies to pump down traffic volume. Sound-absorbing asphalt pavement exists, too.
Some municipalities are taking innovative measures on their own. Noise cameras have been installed in Knoxville, Miami and New York City this year and six California cities will join suit next year. If your muffler or audio system registers 86 dB or higher, you may receive a warning, fine or citation, similar to how a red-light camera works. Rueter predicts these cameras will become commonplace.
Based on understanding how metabolic processes affect noise-induced hearing loss in animal models, scientists are exploring whether pharmacological interventions might work to inhibit cellular damage or improve cellular defenses against noise.
Washington, DC, and the University of Southern California have banned gas-powered leaf blowers in lieu of quieter battery-powered models to reduce both noise and air pollution. California will be the first state to ban the sale of gas-powered lawn equipment starting 2024.
New York state legislators enacted the SLEEP (Stop Loud and Excessive Exhaust Pollution) Act in 2021. This measure increases enforcement and fines against motorists and repair shops that illegally modify mufflers and exhaust systems for effect.
“A lot more basic science and application research is needed [to control noise],” says Thompson, noting that funding for this largely dried up after the 1970s. Based on understanding how metabolic processes affect noise-induced hearing loss in animal models, scientists are exploring whether pharmacological interventions might work to inhibit cellular damage or improve cellular defenses against noise.
Studying biochemical or known genetic markers for noise risk could lead to other methods for preventing hearing loss. This would offer an opportunity to identify people with significant risk so those more susceptible to hearing loss could start taking precautions to avoid noise or protect their ears in childhood.
These efforts could become more pressing in the near future, with the anticipated onslaught of drones, rising needs for air conditioners, and urban sprawl boding poorly for the soundscape. This, as deforestation destroys natural carbon absorption reservoirs and removes sound-buffering trees.
“Local and state governments don’t have a plan to deal with [noise] now or in the future,” says Walker. “We need to think about this with intentionality.”
In March, researchers published a review that lists which organic chemicals match up with certain diseases and biomarkers in the skin, saliva and urine. It’s an important step in creating a robot nose that can reliably detect diseases.
All diseases alter the organic compounds found in the body and their odors. Volatolomics is an emerging branch of chemistry that uses the smell of gases emitted by breath, urine, blood, stool, tears or sweat to diagnose disease. When someone is sick, the normal biochemical process is disrupted, and this alters the makeup of the gas, including a change in odor.
“These metabolites show a snapshot of what’s going on with the body,” says Cristina Davis, a biomedical engineer and associate vice chancellor of Interdisciplinary Research and Strategic Initiatives at the University of California, Davis. This opens the door to diagnosing conditions even before symptoms are present. It’s possible to detect a sweet, fruity smell in the breath of someone with diabetes, for example.
Hippocrates may have been the first to note that people with certain diseases give off an odor but dogs provided the proof of concept. Scientists have published countless studies in which dogs or other high-performing smellers like rodents have identified people with cancer, lung disease or other conditions by smell alone. The brain region that analyzes smells is proportionally about 40 times greater in dogs than in people. The noses of rodents are even more powerful.
Take prostate cancer, which is notoriously difficult to detect accurately with standard medical testing. After sniffing a tiny urine sample, trained dogs were able to pick out prostate cancer in study subjects more than 96 percent of the time, and earlier than a physician could in some cases.
But using dogs as bio-detectors is not practical. It is labor-intensive, complicated and expensive to train dogs to bark or lie down when they smell a certain VOC, explains Bruce Kimball, a chemical ecologist at the Monell Chemical Senses Center in Philadelphia. Kimball has trained ferrets to scratch a box when they smell a specific VOC so he knows. The lab animal must be taught to distinguish the VOC from background odors and trained anew for each disease scent.
In the lab of chemical ecologist Bruce Kimball, ferrets were trained to scratch a box when they identified avian flu in mallard ducks.
Glen J. Golden
There are some human super-smellers among us. In 2019, Joy Milne of Scotland proved she could unerringly identify people with Parkinson’s disease from a musky scent emitted from their skin. Clinical testing showed that she could distinguish the odor of Parkinson’s on a worn t-shirt before clinical symptoms even appeared.
Hossam Haick, a professor at Technion-Israel Institute of Technology, maintains that volatolomics is the future of medicine. Misdiagnosis and late detection are huge problems in health care, he says. “A precise and early diagnosis is the starting point of all clinical activities.” Further, this science has the potential to eliminate costly invasive testing or imaging studies and improve outcomes through earlier treatment.
The Nose Knows a Lot
“Volatolomics is not a fringe theory. There is science behind it,” Davis stresses. Every VOC has its own fingerprint, and a method called gas chromatography-mass spectrometry (GCMS) uses highly sensitive instruments to separate the molecules of these VOCs to determine their structures. But GCMS can’t discern the telltale patterns of particular diseases, and other technologies to analyze biomarkers have been limited.
We have technology that can see, hear and sense touch but scientists don’t have a handle yet on how smell works. The ability goes beyond picking out a single scent in someone’s breath or blood sample. It’s the totality of the smell—not the smell of a single chemical— which defines a disease. The dog’s brain is able to infer something when they smell a VOC that eludes human analysis so far.
Odor is a complex ecosystem and analyzing a VOC is compounded by other scents in the environment, says Kimball. A person’s diet and use of tobacco or alcohol also will affect the breath. Even fluctuations in humidity and temperature can contaminate a sample.
If successful, a sophisticated AI network can imitate how the dog brain recognizes patterns in smells. Early versions of robot noses have already been developed.
With today’s advances in data mining, AI and machine learning, scientists are trying to create mechanical devices that can draw on algorithms based on GCMS readings and data about diseases that dogs have sniffed out. If successful, a sophisticated AI network can imitate how the dog brain recognizes patterns in smells.
In March, Nano Research published a comprehensive review of volatolomics in health care authored by Haick and seven colleagues. The intent was to bridge gaps in the field for scientists trying to connect the biomarkers and sensor technology needed to develop a robot nose. This paper serves as a reference manual for the field that lists which VOCs are associated with what disease and the biomarkers in skin, saliva, breath, and urine.
Weiwei Wu, one of the co-authors and a professor at Xidian University in China, explains that creating a robotic nose requires the expertise of chemists, computer scientists, electrical engineers, material scientists, and clinicians. These researchers use different terms and methodologies and most have not collaborated before with the other disciplines. “The electrical engineers know the device but they don’t know as much about the biomarkers they need to detect,” Wu offers as an example.
This review is significant, Wu continues, because it can facilitate progress in the field by providing experts in all the disciplines with the basic knowledge needed to create an effective robot nose for diagnostic use. The paper also includes a systematic summary of the research methodology of volatolomics.
Once scientists build a stronger database of VOCs, they can program a device to identify critical patterns of specified diseases on a reliable basis. On a machine learning model, the algorithms automatically get better at diagnosing with each use. Wu envisions further tweaks in the next few years to make the devices smaller and consume less power.
A Whiff of the Future
Early versions of robot noses have already been developed. Some of them use chemical sensors to pick up smells in the breath or other body emission molecules. That data is sent through an electrical signal to a computer network for interpretation and possible linkage to a disease.
This electronic nose, or e-nose, has been successful in small pilot studies at labs around the world. At Ben-Gurion University in Israel, researchers detected breast cancer with electronic gas sensors with 95% accuracy, a higher sensitivity than mammograms. Other robot noses, called p-noses, use photons instead of electrical signals.
The mechanical noses being developed tap different methodologies and analytic techniques which makes it hard to compare them. Plus, the devices are intended for varying uses. One team, for example, is working on an e-nose that can be waved over a plate to screen for the presence of a particular allergen when you’re dining out.
A robot nose could be used as a real-time diagnostic tool in clinical practice. Kimball is working on one such tool that can distinguish between a viral and bacterial infection. This would enable physicians to determine whether an antibiotic prescription is appropriate without waiting for a lab result.
Davis is refining a hand-held device that identifies COVID-19 through a simple breath test. She sees the tool being used at crowded airports, sports stadiums and concert venues where PCR or rapid antigen testing is impractical. Background air samples are collected from the space so that those signals can be removed from the human breath measurement. “[The sensor tool] has the same accuracy as the rapid antigen test kits but exhaled breath is easier to collect,” she notes.
The NaNose, also known as the SniffPhone, uses tiny sensors boosted by AI to distinguish Alzheimer's, Crohn's disease, the early stages of several cancers, and other diseases with 84 to 98 percent accuracy.
Hossam Haick
Haick named his team’s robot nose, “NaNose,” since it is based on nanotechnology; the prototype is called the SniffPhone. Using tiny sensors boosted by AI, it can distinguish 23 diseases in human subjects with 84 to 98 percent accuracy. This includes early stages of several cancers, Alzheimer’s, tuberculosis and Crohn’s disease. His team has been raising the accuracy level by combining biomarker signals from both breath and skin, for example. The goal is to achieve 99.9 percent accuracy consistently so no other diagnostic tests would be needed before treating the patient. Plus, it will be affordable, he says.
Kimball predicts we’ll be seeing these diagnostic tools in the next decade. “The physician would narrow down what [the diagnosis] might be and then get the correct tool,” he says. Others are envisioning one device that can screen for multiple diseases by programming the software, which would be updated regularly with new findings.
Larger volatolomics studies must be conducted before these e-noses are ready for clinical use, however. Experts also need to learn how to establish normal reference ranges for e-nose readings to support clinicians using the tool.
“Taking successful prototypes from the lab to industry is the challenge,” says Haick, ticking off issues like reproducibility, mass production and regulation. But volatolomics researchers are unanimous in believing the future of health care is so close they can smell it.