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.
Dr. May Edward Chinn, Kizzmekia Corbett, PhD., and Alice Ball, among others, have been behind some of the most important scientific work of the last century.
If you look back on the last century of scientific achievements, you might notice that most of the scientists we celebrate are overwhelmingly white, while scientists of color take a backseat. Since the Nobel Prize was introduced in 1901, for example, no black scientists have landed this prestigious award.
The work of black women scientists has gone unrecognized in particular. Their work uncredited and often stolen, black women have nevertheless contributed to some of the most important advancements of the last 100 years, from the polio vaccine to GPS.
Here are five black women who have changed science forever.
Dr. May Edward Chinn
Dr. May Edward Chinn practicing medicine in Harlem
George B. Davis, PhD.
Chinn was born to poor parents in New York City just before the start of the 20th century. Although she showed great promise as a pianist, playing with the legendary musician Paul Robeson throughout the 1920s, she decided to study medicine instead. Chinn, like other black doctors of the time, were barred from studying or practicing in New York hospitals. So Chinn formed a private practice and made house calls, sometimes operating in patients’ living rooms, using an ironing board as a makeshift operating table.
Chinn worked among the city’s poor, and in doing this, started to notice her patients had late-stage cancers that often had gone undetected or untreated for years. To learn more about cancer and its prevention, Chinn begged information off white doctors who were willing to share with her, and even accompanied her patients to other clinic appointments in the city, claiming to be the family physician. Chinn took this information and integrated it into her own practice, creating guidelines for early cancer detection that were revolutionary at the time—for instance, checking patient health histories, checking family histories, performing routine pap smears, and screening patients for cancer even before they showed symptoms. For years, Chinn was the only black female doctor working in Harlem, and she continued to work closely with the poor and advocate for early cancer screenings until she retired at age 81.
Alice Ball
Pictorial Press Ltd/Alamy
Alice Ball was a chemist best known for her groundbreaking work on the development of the “Ball Method,” the first successful treatment for those suffering from leprosy during the early 20th century.
In 1916, while she was an undergraduate student at the University of Hawaii, Ball studied the effects of Chaulmoogra oil in treating leprosy. This oil was a well-established therapy in Asian countries, but it had such a foul taste and led to such unpleasant side effects that many patients refused to take it.
So Ball developed a method to isolate and extract the active compounds from Chaulmoogra oil to create an injectable medicine. This marked a significant breakthrough in leprosy treatment and became the standard of care for several decades afterward.
Unfortunately, Ball died before she could publish her results, and credit for this discovery was given to another scientist. One of her colleagues, however, was able to properly credit her in a publication in 1922.
Henrietta Lacks
onathan Newton/The Washington Post/Getty
The person who arguably contributed the most to scientific research in the last century, surprisingly, wasn’t even a scientist. Henrietta Lacks was a tobacco farmer and mother of five children who lived in Maryland during the 1940s. In 1951, Lacks visited Johns Hopkins Hospital where doctors found a cancerous tumor on her cervix. Before treating the tumor, the doctor who examined Lacks clipped two small samples of tissue from Lacks’ cervix without her knowledge or consent—something unthinkable today thanks to informed consent practices, but commonplace back then.
As Lacks underwent treatment for her cancer, her tissue samples made their way to the desk of George Otto Gey, a cancer researcher at Johns Hopkins. He noticed that unlike the other cell cultures that came into his lab, Lacks’ cells grew and multiplied instead of dying out. Lacks’ cells were “immortal,” meaning that because of a genetic defect, they were able to reproduce indefinitely as long as certain conditions were kept stable inside the lab.
Gey started shipping Lacks’ cells to other researchers across the globe, and scientists were thrilled to have an unlimited amount of sturdy human cells with which to experiment. Long after Lacks died of cervical cancer in 1951, her cells continued to multiply and scientists continued to use them to develop cancer treatments, to learn more about HIV/AIDS, to pioneer fertility treatments like in vitro fertilization, and to develop the polio vaccine. To this day, Lacks’ cells have saved an estimated 10 million lives, and her family is beginning to get the compensation and recognition that Henrietta deserved.
Dr. Gladys West
Andre West
Gladys West was a mathematician who helped invent something nearly everyone uses today. West started her career in the 1950s at the Naval Surface Warfare Center Dahlgren Division in Virginia, and took data from satellites to create a mathematical model of the Earth’s shape and gravitational field. This important work would lay the groundwork for the technology that would later become the Global Positioning System, or GPS. West’s work was not widely recognized until she was honored by the US Air Force in 2018.
Dr. Kizzmekia "Kizzy" Corbett
TIME Magazine
At just 35 years old, immunologist Kizzmekia “Kizzy” Corbett has already made history. A viral immunologist by training, Corbett studied coronaviruses at the National Institutes of Health (NIH) and researched possible vaccines for coronaviruses such as SARS (Severe Acute Respiratory Syndrome) and MERS (Middle East Respiratory Syndrome).
At the start of the COVID pandemic, Corbett and her team at the NIH partnered with pharmaceutical giant Moderna to develop an mRNA-based vaccine against the virus. Corbett’s previous work with mRNA and coronaviruses was vital in developing the vaccine, which became one of the first to be authorized for emergency use in the United States. The vaccine, along with others, is responsible for saving an estimated 14 million lives.
In the realm of cancer, Dr. Song is similarly setting his sights on another group of patients for whom treatment options are few and far between: people with solid tumors. Whereas some gradual progress has been made in treating blood cancers such as certain leukemias in past few decades, solid tumors have been even more of a challenge. But Dr. Song’s approach of using natural killer cells to treat solid tumors is promising. You may have heard of CAR-T, which uses genetic engineering to introduce cells into the body that have a particular function to help treat a disease. NKGen focuses on other means to enhance the 40 plus receptors of natural killer cells, making them more receptive and sensitive to picking out cancer cells.
Paul Y. Song, MD is currently CEO and Vice Chairman of NKGen Biotech. Dr. Song’s last clinical role was Asst. Professor at the Samuel Oschin Cancer Center at Cedars Sinai Medical Center.
Dr. Song served as the very first visiting fellow on healthcare policy in the California Department of Insurance in 2013. He is currently on the advisory board of the Pritzker School of Molecular Engineering at the University of Chicago and a board member of Mercy Corps, The Center for Health and Democracy, and Gideon’s Promise.
Dr. Song graduated with honors from the University of Chicago and received his MD from George Washington University. He completed his residency in radiation oncology at the University of Chicago where he served as Chief Resident and did a brachytherapy fellowship at the Institute Gustave Roussy in Villejuif, France. He was also awarded an ASTRO research fellowship in 1995 for his research in radiation inducible gene therapy.
With Dr. Song’s leadership, NKGen Biotech’s work on natural killer cells represents cutting-edge science leading to key findings and important pieces of the puzzle for treating two of humanity’s most intractable diseases.
Show links
- Paul Song LinkedIn
- NKGen Biotech on Twitter - @NKGenBiotech
- NKGen Website: https://nkgenbiotech.com/
- NKGen appoints Paul Song
- Patient Story: https://pix11.com/news/local-news/long-island/promising-new-treatment-for-advanced-alzheimers-patients/
- FDA Clearance: https://nkgenbiotech.com/nkgen-biotech-receives-ind-clearance-from-fda-for-snk02-allogeneic-natural-killer-cell-therapy-for-solid-tumors/Q3 earnings data: https://www.nasdaq.com/press-release/nkgen-biotech-inc.-reports-third-quarter-2023-financial-results-and-business
