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.
Indigenous people in Australia dig pits next to a honeypot colony. Scientists think the honey can be used to make new antimicrobial drugs.
These hunts have become rarer, as many of the Tjupan people have moved away and, up until now, the exact antimicrobial properties of the ant honey remained unknown. But recently, scientists Andrew Dong and Kenya Fernandes from the University of Sydney, joined Ulrich, who runs the Honeypot Ants tours in Kalgoorlie, a city in Western Australia, on a honey-gathering expedition. Afterwards, they ran a series of experiments analyzing the honey’s antimicrobial activity—and confirmed that the Indigenous wisdom was true. The honey was effective against Staphylococcus aureus, a common pathogen responsible for sore throats, skin infections like boils and sores, and also sepsis, which can result in death. Moreover, the honey also worked against two species of fungi, Cryptococcus and Aspergillus, which can be pathogenic to humans, especially those with suppressed immune systems.
In the era of growing antibiotic resistance and the rising threat of pathogenic fungi, these findings may help scientists identify and make new antimicrobial compounds. “Natural products have been honed over thousands and millions of years by nature and evolution,” says Fernandes. “And some of them have complex and intricate properties that make them really important as potential new antibiotics. “
In an era of growing resistance to antibiotics and new threats of fungi infections, the latest findings about honeypot ants are helping scientists identify new antimicrobial drugs.
Danny Ulrich
Bee honey is also known for its antimicrobial properties, but bees produce it very differently than the ants. Bees collect nectar from flowers, which they regurgitate at the hive and pack into the hexagonal honeycombs they build for storage. As they do so, they also add into the mix an enzyme called glucose oxidase produced by their glands. The enzyme converts atmospheric oxygen into hydrogen peroxide, a reactive molecule that destroys bacteria and acts as a natural preservative. After the bees pack the honey into the honeycombs, they fan it with their wings to evaporate the water. Once a honeycomb is full, the bees put a beeswax cover on it, where it stays well-preserved thanks to the enzymatic action, until the bees need it.
Less is known about the chemistry of ants’ honey-making. Similarly to bees, they collect nectar. They also collect the sweet sap of the mulga tree. Additionally, they also “milk” the aphids—small sap-sucking insects that live on the tree. When ants tickle the aphids with their antennae, the latter release a sweet substance, which the former also transfer to their colonies. That’s where the honey management difference becomes really pronounced. The ants don’t build any kind of structures to store their honey. Instead, they store it in themselves.
The workers feed their harvest to their fellow ants called repletes, stuffing them up to the point that their swollen bellies outgrow the ants themselves, looking like amber-colored honeypots—hence the name. Because of their size, repletes don’t move, but hang down from the chamber’s ceiling, acting as living feedstocks. When food becomes scarce, they regurgitate their reserves to their colony’s brethren. It’s not clear whether the repletes die afterwards or can be restuffed again. “That's a good question,” Dong says. “After they've been stretched, they can't really return to exactly the same shape.”
These replete ants are the “treat” the Tjupan women dug for. Once they saw the round-belly ants inside the chambers, they would reach in carefully and get a few scoops of them. “You see a lot of honeypot ants just hanging on the roof of the little openings,” says Ulrich’s mother, Edie Ulrich. The women would share the ants with family members who would eat them one by one. “They're very delicate,” shares Edie Ulrich—you have to take them out carefully, so they don’t accidentally pop and become a wasted resource. “Because you’d lose all this precious honey.”
Dong stumbled upon the honeypot ants phenomenon because he was interested in Indigenous foods and went on Ulrich’s tour. He quickly became fascinated with the insects and their role in the Indigenous culture. “The honeypot ants are culturally revered by the Indigenous people,” he says. Eventually he decided to test out the honey’s medicinal qualities.
The researchers were surprised to see that even the smallest, eight percent concentration of honey was able to arrest the growth of S. aureus.
To do this, the two scientists first diluted the ant honey with water. “We used something called doubling dilutions, which means that we made 32 percent dilutions, and then we halve that to 16 percent and then we half that to eight percent,” explains Fernandes. The goal was to obtain as much results as possible with the meager honey they had. “We had very, very little of the honeypot ant honey so we wanted to maximize the spectrum of results we can get without wasting too much of the sample.”
After that, the researchers grew different microbes inside a nutrient rich broth. They added the broth to the different honey dilutions and incubated the mixes for a day or two at the temperature favorable to the germs’ growth. If the resulting solution turned turbid, it was a sign that the bugs proliferated. If it stayed clear, it meant that the honey destroyed them. The researchers were surprised to see that even the smallest, eight percent concentration of honey was able to arrest the growth of S. aureus. “It was really quite amazing,” Fernandes says. “Eight milliliters of honey in 92 milliliters of water is a really tiny amount of honey compared to the amount of water.”
Similar to bee honey, the ants’ honey exhibited some peroxide antimicrobial activity, researchers found, but given how little peroxide was in the solution, they think the honey also kills germs by a different mechanism. “When we measured, we found that [the solution] did have some hydrogen peroxide, but it didn't have as much of it as we would expect based on how active it was,” Fernandes says. “Whether this hydrogen peroxide also comes from glucose oxidase or whether it's produced by another source, we don't really know,” she adds. The research team does have some hypotheses about the identity of this other germ-killing agent. “We think it is most likely some kind of antimicrobial peptide that is actually coming from the ant itself.”
The honey also has a very strong activity against the two types of fungi, Cryptococcus and Aspergillus. Both fungi are associated with trees and decaying leaves, as well as in the soils where ants live, so the insects likely have evolved some natural defense compounds, which end up inside the honey.
It wouldn’t be the first time when modern medicines take their origin from the natural world or from the indigenous people’s knowledge. The bark of the cinchona tree native to South America contains quinine, a substance that treats malaria. The Indigenous people of the Andes used the bark to quell fever and chills for generations, and when Europeans began to fall ill with malaria in the Amazon rainforest, they learned to use that medicine from the Andean people.
The wonder drug aspirin similarly takes its origin from a bark of a tree—in this case a willow.
Even some anticancer compounds originated from nature. A chemotherapy drug called Paclitaxel, was originally extracted from the Pacific yew trees, Taxus brevifolia. The samples of the Pacific yew bark were first collected in 1962 by researchers from the United States Department of Agriculture who were looking for natural compounds that might have anti-tumor activity. In December 1992, the FDA approved Paclitaxel (brand name Taxol) for the treatment of ovarian cancer and two years later for breast cancer.
In the era when the world is struggling to find new medicines fast enough to subvert a fungal or bacterial pandemic, these discoveries can pave the way to new therapeutics. “I think it's really important to listen to indigenous cultures and to take their knowledge because they have been using these sources for a really, really long time,” Fernandes says. Now we know it works, so science can elucidate the molecular mechanisms behind it, she adds. “And maybe it can even provide a lead for us to develop some kind of new treatments in the future.”
