How thousands of first- and second-graders saved the world from a deadly disease
Exactly 67 years ago, in 1955, a group of scientists and reporters gathered at the University of Michigan and waited with bated breath for Dr. Thomas Francis Jr., director of the school’s Poliomyelitis Vaccine Evaluation Center, to approach the podium. The group had gathered to hear the news that seemingly everyone in the country had been anticipating for the past two years – whether the vaccine for poliomyelitis, developed by Francis’s former student Jonas Salk, was effective in preventing the disease.
Polio, at that point, had become a household name. As the highly contagious virus swept through the United States, cities closed their schools, movie theaters, swimming pools, and even churches to stop the spread. For most, polio presented as a mild illness, and was usually completely asymptomatic – but for an unlucky few, the virus took hold of the central nervous system and caused permanent paralysis of muscles in the legs, arms, and even people’s diaphragms, rendering the person unable to walk and breathe. It wasn’t uncommon to hear reports of people – mostly children – who fell sick with a flu-like virus and then, just days later, were relegated to spend the rest of their lives in an iron lung.
For two years, researchers had been testing a vaccine that would hopefully be able to stop the spread of the virus and prevent the 45,000 infections each year that were keeping the nation in a chokehold. At the podium, Francis greeted the crowd and then proceeded to change the course of human history: The vaccine, he reported, was “safe, effective, and potent.” Widespread vaccination could begin in just a few weeks. The nightmare was over.
The road to success
Jonas Salk, a medical researcher and virologist who developed the vaccine with his own research team, would rightfully go down in history as the man who eradicated polio. (Today, wild poliovirus circulates in just two countries, Afghanistan and Pakistan – with only 140 cases reported in 2020.) But many people today forget that the widespread vaccination campaign that effectively ended wild polio across the globe would have never been possible without the human clinical trials that preceded it.
As with the COVID-19 vaccine, skepticism and misinformation around the polio vaccine abounded. But even more pervasive than the skepticism was fear. The consequences of polio had arguably never been more visible.
The road to human clinical trials – and the resulting vaccine – was a long one. In 1938, President Franklin Delano Roosevelt launched the National Foundation for Infantile Paralysis in order to raise funding for research and development of a polio vaccine. (Today, we know this organization as the March of Dimes.) A polio survivor himself, Roosevelt elevated awareness and prevention into the national spotlight, even more so than it had been previously. Raising funds for a safe and effective polio vaccine became a cornerstone of his presidency – and the funds raked in by his foundation went primarily to Salk to fund his research.
The Trials Begin
Salk’s vaccine, which included an inactivated (killed) polio virus, was promising – but now the researchers needed test subjects to make global vaccination a possibility. Because the aim of the vaccine was to prevent paralytic polio, researchers decided that they had to test the vaccine in the population that was most vulnerable to paralysis – young children. And, because the rate of paralysis was so low even among children, the team required many children to collect enough data. Francis, who led the trial to evaluate Salk’s vaccine, began the process of recruiting more than one million school-aged children between the ages of six and nine in 272 counties that had the highest incidence of the disease. The participants were nicknamed the “Polio Pioneers.”
Double-blind, placebo-based trials were considered the “gold standard” of epidemiological research back in Francis's day - and they remain the best approach we have today. These rigorous scientific studies are designed with two participant groups in mind. One group, called the test group, receives the experimental treatment (such as a vaccine); the other group, called the control, receives an inactive treatment known as a placebo. The researchers then compare the effects of the active treatment against the effects of the placebo, and every researcher is “blinded” as to which participants receive what treatment. That way, the results aren’t tainted by any possible biases.
But the study was controversial in that only some of the individual field trials at the county and state levels had a placebo group. Researchers described this as a “calculated risk,” meaning that while there were risks involved in giving the vaccine to a large number of children, the bigger risk was the potential paralysis or death that could come with being infected by polio. In all, just 200,000 children across the US received a placebo treatment, while an additional 725,000 children acted as observational controls – in other words, researchers monitored them for signs of infection, but did not give them any treatment.
As with the COVID-19 vaccine, skepticism and misinformation around the polio vaccine abounded. But even more pervasive than the skepticism was fear. President Roosevelt, who had made many public and televised appearances in a wheelchair, served as a perpetual reminder of the consequences of polio, as an infection at age 39 had rendered him permanently unable to walk. The consequences of polio had arguably never been more visible, and parents signed up their children in droves to participate in the study and offer them protection.
The Polio Pioneer Legacy
In a little less than a year, roughly half a million children received a dose of Salk’s polio vaccine. While plenty of children were hesitant to get the shot, many former participants still remember the fear surrounding the disease. One former participant, a Polio Pioneer named Debbie LaCrosse, writes of her experience: “There was no discussion, no listing of pros and cons. No amount of concern over possible side effects or other unknowns associated with a new vaccine could compare to the terrifying threat of polio.” For their participation, each kid received a certificate – and sometimes a pin – with the words “Polio Pioneer” emblazoned across the front.
When Francis announced the results of the trial on April 12, 1955, people did more than just breathe a sigh of relief – they openly celebrated, ringing church bells and flooding into the streets to embrace. Salk, who had become the face of the vaccine at that point, was instantly hailed as a national hero – and teachers around the country had their students to write him ‘thank you’ notes for his years of diligent work.
But while Salk went on to win national acclaim – even accepting the Presidential Medal of Freedom for his work on the polio vaccine in 1977 – his success was due in no small part to the children (and their parents) who took a risk in order to advance medical science. And that risk paid off: By the early 1960s, the yearly cases of polio in the United States had gone down to just 910. Where before the vaccine polio had caused around 15,000 cases of paralysis each year, only ten cases of paralysis were recorded in the entire country throughout the 1970s. And in 1979, the virus that once shuttered entire towns was declared officially eradicated in this country. Thanks to the efforts of these brave pioneers, the nation – along with the majority of the world – remains free of polio even today.
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
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.
Scientists use AI to predict how hospital stays will go
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers