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.
Residents of Fountain Hills, a small town near Phoenix, Arizona, fought against the night sky pollution to restore their Milky Way skies.
Light pollution is defined as the excessive or wasteful use of artificial light.
Light-emitting diodes (LEDs) -- which became commercially available in 2002 and rapidly gained popularity in offices, schools, and hospitals when their price dropped six years later — inadvertently fueled the surge in light pollution. As traditional light sources like halogen, fluorescent, mercury, and sodium vapor lamps have been phased out or banned, LEDs became the main source of lighting globally in 2019. Switching to LEDs has been lauded as a win-win decision. Not only are they cheap but they also consume a fraction of electricity compared to their traditional counterparts.
But as cheap LED installations became omnipresent, they increased light pollution. “People have been installing LEDs thinking they are making a positive change for the environment. But LEDs are a lot brighter than traditional light sources,” explains Ashley Wilson, director of conservation at the International Dark-Sky Association (IDA). “Despite being energy-efficient, they are increasing our energy consumption. No one expected this kind of backlash from switching to LEDs.”
Light pollution impacts the circadian rhythms of all living beings — the natural internal process that regulates the sleep–wake cycle.
Currently, more than 80 percent of the world lives under light-polluted skies. In the U.S. and Europe, that figure is above 99 percent.
According to the IDA, $3 billion worth of electricity is lost to skyglow every year in the U.S. alone — thanks to unnecessary and poorly designed outdoor lighting installations. Worse, the resulting light pollution has insidious impacts on humans and wildlife — in more ways than one.
Disrupting the brain’s clock
Light pollution impacts the circadian rhythms of all living beings—the natural internal process that regulates the sleep–wake cycle. Humans and other mammals have neurons in their retina called intrinsically photosensitive retinal ganglion cells (ipRGCs). These cells collect information about the visual world and directly influence the brain’s biological clock in the hypothalamus.
The ipRGCs are particularly sensitive to the blue light that LEDs emit at high levels, resulting in suppression of melatonin, a hormone that helps us sleep. A 2020 JAMA Psychiatry study detailed how teenagers who lived in areas with bright outdoor lighting at night went to bed late and slept less, which made them more prone to mood disorders and anxiety.
“Many people are skeptical when they are told something as ubiquitous as lights could have such profound impacts on public health,” says Gena Glickman, director of the Chronobiology, Light and Sleep Lab at Uniformed Services University. “But when the clock in our brains gets exposed to blue light at nighttime, it could result in a lot of negative consequences like impaired cognitive function and neuro-endocrine disturbances.”
In the last 12 years, several studies indicated that light pollution exposure is associated with obesity and diabetes in humans and animals alike. While researchers are still trying to understand the exact underlying mechanisms, they found that even one night of too much light exposure could negatively affect the metabolic system. Studies have linked light pollution to a higher risk of hormone-sensitive cancers like breast and prostate cancer. A 2017 study found that female nurses exposed to light pollution have a 14 percent higher risk of breast cancer. The World Health Organization (WHO) identified long-term night shiftwork as a probable cause of cancer.
“We ignore our biological need for a natural light and dark cycle. Our patterns of light exposure have consequently become different from what nature intended,” explains Glickman.
Circadian lighting systems, designed to match individuals’ circadian rhythms, might help. The Lighting Research Center at Rensselaer Polytechnic Institute developed LED light systems that mimic natural lighting fluxes, required for better sleep. In the morning the lights shine brightly as does the sun. After sunset, the system dims, once again mimicking nature, which boosts melatonin production. It can even be programmed to increase blue light indoors when clouds block sunlight’s path through windows. Studies have shown that such systems might help reduce sleep fragmentation and cognitive decline. People who spend most of their day indoors can benefit from such circadian mimics.
When Diane Turnshek moved to Pittsburgh, she found it almost impossible to see a clear night sky because the city’s countless lights created a bright dome of light called skyglow.
Diane Turnshek
Leading to better LEDs
Light pollution disrupts the travels of millions of migratory birds that begin their long-distance journeys after sunset but end up entrapped within the sky glow of cities, becoming disoriented. A 2017 study in Nature found that nocturnal pollinators like bees, moths, fireflies and bats visit 62 percent fewer plants in areas with artificial lights compared to dark areas.
“On an evolutionary timescale, LEDs have triggered huge changes in the Earth’s environment within a relative blink of an eye,” says Wilson, the director of IDA. “Plants and animals cannot adapt so fast. They have to fight to survive with their existing traits and abilities.”
But not all types of LEDs are inherently bad -- it all comes down to how much blue light they emit. During the day, the sun emits blue light waves. By sunset, it’s replaced by red and orange light waves that stimulate melatonin production. LED’s artificial blue light, when shining at night, disrupts that. For some unknown reason, there are more bluer color LEDs made and sold.
“Communities install blue color temperature LEDs rather than redder color temperature LEDs because more of the blue ones are made; they are the status quo on the market,” says Michelle Wooten, an assistant professor of astronomy at the University of Alabama at Birmingham.
Most artificial outdoor light produced is wasted as human eyes do not use them to navigate their surroundings.
While astronomers and the IDA have been educating LED manufacturers about these nuances, policymakers struggle to keep up with the growing industry. But there are things they can do—such as requiring LEDs to include dimmers. “Most LED installations can be dimmed down. We need to make the dimmable drivers a mandatory requirement while selling LED lighting,” says Nancy Clanton, a lighting engineer, designer, and dark sky advocate.
Some lighting companies have been developing more sophisticated LED lights that help support melatonin production. Lighting engineers at Crossroads LLC and Nichia Corporation have been working on creating LEDs that produce more light in the red range. “We live in a wonderful age of technology that has given us these new LED designs which cut out blue wavelengths entirely for dark-sky friendly lighting purposes,” says Wooten.
Dimming the lights to see better
The IDA and advocates like Turnshek propose that communities turn off unnecessary outdoor lights. According to the Department of Energy, 99 percent of artificial outdoor light produced is wasted as human eyes do not use them to navigate their surroundings.
In recent years, major cities like Chicago, Austin, and Philadelphia adopted the “Lights Out” initiative encouraging communities to turn off unnecessary lights during birds’ peak migration seasons for 10 days at a time. “This poses an important question: if people can live without some lights for 10 days, why can’t they keep them turned off all year round,” says Wilson.
Most communities globally believe that keeping bright outdoor lights on all night increases security and prevents crime. But in her studies of street lights’ brightness levels in different parts of the US — from Alaska to California to Washington — Clanton found that people felt safe and could see clearly even at low or dim lighting levels.
Clanton and colleagues installed LEDs in a Seattle suburb that provided only 25 percent of lighting levels compared to what they used previously. The residents reported far better visibility because the new LEDs did not produce glare. “Visual contrast matters a lot more than lighting levels,” Clanton says. Additionally, motion sensor LEDs for outdoor lighting can go a long way in reducing light pollution.
Flipping a switch to preserve starry nights
Clanton has helped draft laws to reduce light pollution in at least 17 U.S. states. However, poor awareness of light pollution led to inadequate enforcement of these laws. Also, getting thousands of counties and municipalities within any state to comply with these regulations is a Herculean task, Turnshek points out.
Fountain Hills, a small town near Phoenix, Arizona, has rid itself of light pollution since 2018, thanks to the community's efforts to preserve dark skies.
Until LEDs became mainstream, Fountain Hills enjoyed starry skies despite its proximity to Phoenix. A mountain surrounding the town blocks most of the skyglow from the city.
“Light pollution became an issue in Fountain Hills over the years because we were not taking new LED technologies into account. Our town’s lighting code was antiquated and out-of-date,” says Vicky Derksen, a resident who is also a part of the Fountain Hills Dark Sky Association founded in 2017. “To preserve dark skies, we had to work with the entire town to update the local lighting code and convince residents to follow responsible outdoor lighting practices.”
Derksen and her team first tackled light pollution in the town center which has a faux fountain in the middle of a lake. “The iconic centerpiece, from which Fountain Hills got its name, had the wrong types of lighting fixtures, which created a lot of glare,” adds Derksen. They then replaced several other municipal lighting fixtures with dark-sky-friendly LEDs.
The results were awe-inspiring. After a long time, residents could see the Milky Way with crystal clear clarity. Star-gazing activities made a strong comeback across the town. But keeping light pollution low requires constant work.
Derksen and other residents regularly measure artificial light levels in
Fountain Hills. Currently, the only major source of light pollution is from extremely bright, illuminated signs which local businesses had installed in different parts of the town. While Derksen says it is an uphill battle to educate local businesses about light pollution, Fountain Hills residents are determined to protect their dark skies.
“When a river gets polluted, it can take several years before clean-up efforts see any tangible results,” says Derksen. “But the effects are immediate when you work toward reducing light pollution. All it requires is flipping a switch.”