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.
Katalin Karikó, pictured, and Drew Weissman won the Nobel Prize for advances in mRNA research that led to the first Covid vaccines.
The work for which they garnered the illustrious award and its accompanying $1,000,000 cash windfall was completed about two decades ago, wrought through long hours in the lab over many arduous years. But humanity collectively benefited from its life-saving outcome three years ago, when both Moderna and Pfizer/BioNTech’s mRNA vaccines against COVID were found to be safe and highly effective at preventing severe disease. Billions of doses have since been given out to protect humans from the upstart viral scourge.
“I thought of going somewhere else, or doing something else,” said Katalin Karikó. “I also thought maybe I’m not good enough, not smart enough. I tried to imagine: Everything is here, and I just have to do better experiments.”
Unlocking the power of mRNA
Weissman and Karikó unlocked mRNA vaccines for the world back in the early 2000s when they made a key breakthrough. Messenger RNA molecules are essentially instructions for cells’ ribosomes to make specific proteins, so in the 1980s and 1990s, researchers started wondering if sneaking mRNA into the body could trigger cells to manufacture antibodies, enzymes, or growth agents for protecting against infection, treating disease, or repairing tissues. But there was a big problem: injecting this synthetic mRNA triggered a dangerous, inflammatory immune response resulting in the mRNA’s destruction.
While most other researchers chose not to tackle this perplexing problem to instead pursue more lucrative and publishable exploits, Karikó stuck with it. The choice sent her academic career into depressing doldrums. Nobody would fund her work, publications dried up, and after six years as an assistant professor at the University of Pennsylvania, Karikó got demoted. She was going backward.
“I thought of going somewhere else, or doing something else,” Karikó told Stat in 2020. “I also thought maybe I’m not good enough, not smart enough. I tried to imagine: Everything is here, and I just have to do better experiments.”
A tale of tenacity
Collaborating with Drew Weissman, a new professor at the University of Pennsylvania, in the late 1990s helped provide Karikó with the tenacity to continue. Weissman nurtured a goal of developing a vaccine against HIV-1, and saw mRNA as a potential way to do it.
“For the 20 years that we’ve worked together before anybody knew what RNA is, or cared, it was the two of us literally side by side at a bench working together,” Weissman said in an interview with Adam Smith of the Nobel Foundation.
In 2005, the duo made their 2023 Nobel Prize-winning breakthrough, detailing it in a relatively small journal, Immunity. (Their paper was rejected by larger journals, including Science and Nature.) They figured out that chemically modifying the nucleoside bases that make up mRNA allowed the molecule to slip past the body’s immune defenses. Karikó and Weissman followed up that finding by creating mRNA that’s more efficiently translated within cells, greatly boosting protein production. In 2020, scientists at Moderna and BioNTech (where Karikó worked from 2013 to 2022) rushed to craft vaccines against COVID, putting their methods to life-saving use.
The future of vaccines
Buoyed by the resounding success of mRNA vaccines, scientists are now hurriedly researching ways to use mRNA medicine against other infectious diseases, cancer, and genetic disorders. The now ubiquitous efforts stand in stark contrast to Karikó and Weissman’s previously unheralded struggles years ago as they doggedly worked to realize a shared dream that so many others shied away from. Katalin Karikó and Drew Weissman were brave enough to walk a scientific path that very well could have ended in a dead end, and for that, they absolutely deserve their 2023 Nobel Prize.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
With conventional fuel cells as their model, researchers learned to use similar chemical reactions to make a fuel from microbes in pee.
Plus, urine contains water, phosphorus, potassium and nitrogen, the key ingredients plants need to grow and survive. Human urine could replace about 25 percent of current nitrogen and phosphorous fertilizers worldwide and could save water for gardens and crops. The average U.S. resident flushes a toilet bowl containing only pee and paper about six to seven times a day, which adds up to about 3,500 gallons of water down per year. Plus cows in the U.S. produce 231 gallons of the stuff each year.
Pee power
A conventional fuel cell uses chemical reactions to produce energy, as electrons move from one electrode to another to power a lightbulb or phone. Ioannis Ieropoulos, a professor and chair of Environmental Engineering at the University of Southampton in England, realized the same type of reaction could be used to make a fuel from microbes in pee.
Bacterial species like Shewanella oneidensis and Pseudomonas aeruginosa can consume carbon and other nutrients in urine and pop out electrons as a result of their digestion. In a microbial fuel cell, one electrode is covered in microbes, immersed in urine and kept away from oxygen. Another electrode is in contact with oxygen. When the microbes feed on nutrients, they produce the electrons that flow through the circuit from one electrod to another to combine with oxygen on the other side. As long as the microbes have fresh pee to chomp on, electrons keep flowing. And after the microbes are done with the pee, it can be used as fertilizer.
These microbes are easily found in wastewater treatment plants, ponds, lakes, rivers or soil. Keeping them alive is the easy part, says Ieropoulos. Once the cells start producing stable power, his group sequences the microbes and keeps using them.
Like many promising technologies, scaling these devices for mass consumption won’t be easy, says Kevin Orner, a civil engineering professor at West Virginia University. But it’s moving in the right direction. Ieropoulos’s device has shrunk from the size of about three packs of cards to a large glue stick. It looks and works much like a AAA battery and produce about the same power. By itself, the device can barely power a light bulb, but when stacked together, they can do much more—just like photovoltaic cells in solar panels. His lab has produced 1760 fuel cells stacked together, and with manufacturing support, there’s no theoretical ceiling, he says.
Although pure urine produces the most power, Ieropoulos’s devices also work with the mixed liquids of the wastewater treatment plants, so they can be retrofit into urban wastewater utilities.
This image shows how the pee-powered system works. Pee feeds bacteria in the stack of fuel cells (1), which give off electrons (2) stored in parallel cylindrical cells (3). These cells are connected to a voltage regulator (4), which smooths out the electrical signal to ensure consistent power to the LED strips lighting the toilet.
Courtesy Ioannis Ieropoulos
Key to the long-term success of any urine reclamation effort, says Orner, is avoiding what he calls “parachute engineering”—when well-meaning scientists solve a problem with novel tech and then abandon it. “The way around that is to have either the need come from the community or to have an organization in a community that is committed to seeing a project operate and maintained,” he says.
Success with urine reclamation also depends on the economy. “If energy prices are low, it may not make sense to recover energy,” says Orner. “But right now, fertilizer prices worldwide are generally pretty high, so it may make sense to recover fertilizer and nutrients.” There are obstacles, too, such as few incentives for builders to incorporate urine recycling into new construction. And any hiccups like leaks or waste seepage will cost builders money and reputation. Right now, Orner says, the risks are just too high.
Despite the challenges, Ieropoulos envisions a future in which urine is passed through microbial fuel cells at wastewater treatment plants, retrofitted septic tanks, and building basements, and is then delivered to businesses to use as agricultural fertilizers. Although pure urine produces the most power, Ieropoulos’s devices also work with the mixed liquids of the wastewater treatment plants, so they can be retrofitted into urban wastewater utilities where they can make electricity from the effluent. And unlike solar cells, which are a common target of theft in some areas, nobody wants to steal a bunch of pee.
When Ieropoulos’s team returned to wrap up their pilot project 18 months later, the school’s director begged them to leave the fuel cells in place—because they made a major difference in students’ lives. “We replaced it with a substantial photovoltaic panel,” says Ieropoulos, They couldn’t leave the units forever, he explained, because of intellectual property reasons—their funders worried about theft of both the technology and the idea. But the photovoltaic replacement could be stolen, too, leaving the girls in the dark.
The story repeated itself at another school, in Nairobi, Kenya, as well as in an informal settlement in Durban, South Africa. Each time, Ieropoulos vowed to return. Though the pandemic has delayed his promise, he is resolute about continuing his work—it is a moral and legal obligation. “We've made a commitment to ourselves and to the pupils,” he says. “That's why we need to go back.”
Urine as fertilizer
Modern day industrial systems perpetuate the broken cycle of nutrients. When plants grow, they use up nutrients the soil. We eat the plans and excrete some of the nutrients we pass them into rivers and oceans. As a result, farmers must keep fertilizing the fields while our waste keeps fertilizing the waterways, where the algae, overfertilized with nitrogen, phosphorous and other nutrients grows out of control, sucking up oxygen that other marine species need to live. Few global communities remain untouched by the related challenges this broken chain create: insufficient clean water, food, and energy, and too much human and animal waste.
The Rich Earth Institute in Vermont runs a community-wide urine nutrient recovery program, which collects urine from homes and businesses, transports it for processing, and then supplies it as fertilizer to local farms.
One solution to this broken cycle is reclaiming urine and returning it back to the land. The Rich Earth Institute in Vermont is one of several organizations around the world working to divert and save urine for agricultural use. “The urine produced by an adult in one day contains enough fertilizer to grow all the wheat in one loaf of bread,” states their website.
Notably, while urine is not entirely sterile, it tends to harbor fewer pathogens than feces. That’s largely because urine has less organic matter and therefore less food for pathogens to feed on, but also because the urinary tract and the bladder have built-in antimicrobial defenses that kill many germs. In fact, the Rich Earth Institute says it’s safe to put your own urine onto crops grown for home consumption. Nonetheless, you’ll want to dilute it first because pee usually has too much nitrogen and can cause “fertilizer burn” if applied straight without dilution. Other projects to turn urine into fertilizer are in progress in Niger, South Africa, Kenya, Ethiopia, Sweden, Switzerland, The Netherlands, Australia, and France.
Eleven years ago, the Institute started a program that collects urine from homes and businesses, transports it for processing, and then supplies it as fertilizer to local farms. By 2021, the program included 180 donors producing over 12,000 gallons of urine each year. This urine is helping to fertilize hay fields at four partnering farms. Orner, the West Virginia professor, sees it as a success story. “They've shown how you can do this right--implementing it at a community level scale."