Trading syphilis for malaria: How doctors treated one deadly disease by infecting patients with another
If you had lived one hundred years ago, syphilis – a bacterial infection spread by sexual contact – would likely have been one of your worst nightmares. Even though syphilis still exists, it can now be detected early and cured quickly with a course of antibiotics. Back then, however, before antibiotics and without an easy way to detect the disease, syphilis was very often a death sentence.
To understand how feared syphilis once was, it’s important to understand exactly what it does if it’s allowed to progress: the infections start off as small, painless sores or even a single sore near the vagina, penis, anus, or mouth. The sores disappear around three to six weeks after the initial infection – but untreated, syphilis moves into a secondary stage, often presenting as a mild rash in various areas of the body (such as the palms of a person’s hands) or through other minor symptoms. The disease progresses from there, often quietly and without noticeable symptoms, sometimes for decades before it reaches its final stages, where it can cause blindness, organ damage, and even dementia. Research indicates, in fact, that as much as 10 percent of psychiatric admissions in the early 20th century were due to dementia caused by syphilis, also known as neurosyphilis.
Like any bacterial disease, syphilis can affect kids, too. Though it’s spread primarily through sexual contact, it can also be transmitted from mother to child during birth, causing lifelong disability.
The poet-physician Aldabert Bettman, who wrote fictionalized poems based on his experiences as a doctor in the 1930s, described the effect syphilis could have on an infant in his poem Daniel Healy:
I always got away clean
when I went out
With the boys.
The night before
I was married
I went out,—But was not so fortunate;
And I infected
My bride.
When little Daniel
Was born
His eyes discharged;
And I dared not tell
That because
I had seen too much
Little Daniel sees not at all
Given the horrors of untreated syphilis, it’s maybe not surprising that people would go to extremes to try and treat it. One of the earliest remedies for syphilis, dating back to 15th century Naples, was using mercury – either rubbing it on the skin where blisters appeared, or breathing it in as a vapor. (Not surprisingly, many people who underwent this type of “treatment” died of mercury poisoning.)
Other primitive treatments included using tinctures made of a flowering plant called guaiacum, as well as inducing “sweat baths” to eliminate the syphilitic toxins. In 1910, an arsenic-based drug called Salvarsan hit the market and was hailed as a “magic bullet” for its ability to target and destroy the syphilis-causing bacteria without harming the patient. However, while Salvarsan was effective in treating early-stage syphilis, it was largely ineffective by the time the infection progressed beyond the second stage. Tens of thousands of people each year continued to die of syphilis or were otherwise shipped off to psychiatric wards due to neurosyphilis.
It was in one of these psychiatric units in the early 20th century that Dr. Julius Wagner-Juaregg got the idea for a potential cure.
Wagner-Juaregg was an Austrian-born physician trained in “experimental pathology” at the University of Vienna. Wagner-Juaregg started his medical career conducting lab experiments on animals and then moved on to work at different psychiatric clinics in Vienna, despite having no training in psychiatry or neurology.
Wagner-Juaregg’s work was controversial to say the least. At the time, medicine – particularly psychiatric medicine – did not have anywhere near the same rigorous ethical standards that doctors, researchers, and other scientists are bound to today. Wagner-Juaregg would devise wild theories about the cause of their psychiatric ailments and then perform experimental procedures in an attempt to cure them. (As just one example, Wagner-Juaregg would sterilize his adolescent male patients, thinking “excessive masturbation” was the cause of their schizophrenia.)
But sometimes these wild theories paid off. In 1883, during his residency, Wagner-Juaregg noted that a female patient with mental illness who had contracted a skin infection and suffered a high fever experienced a sudden (and seemingly miraculous) remission from her psychosis symptoms after the fever had cleared. Wagner-Juaregg theorized that inducing a high fever in his patients with neurosyphilis could help them recover as well.
Eventually, Wagner-Juaregg was able to put his theory to the test. Around 1890, Wagner-Juaregg got his hands on something called tuberculin, a therapeutic treatment created by the German microbiologist Robert Koch in order to cure tuberculosis. Tuberculin would later turn out to be completely ineffective for treating tuberculosis, often creating severe immune responses in patients – but for a short time, Wagner-Juaregg had some success in using tuberculin to help his dementia patients. Giving his patients tuberculin resulted in a high fever – and after completing the treatment, Wagner-Jauregg reported that his patient’s dementia was completely halted. The success was short-lived, however: Wagner-Juaregg eventually had to discontinue tuberculin as a treatment, as it began to be considered too toxic.
By 1917, Wagner-Juaregg’s theory about syphilis and fevers was becoming more credible – and one day a new opportunity presented itself when a wounded soldier, stricken with malaria and a related fever, was accidentally admitted to his psychiatric unit.
When his findings were published in 1918, Wagner-Juaregg’s so-called “fever therapy” swept the globe.
What Wagner-Juaregg did next was ethically deplorable by any standard: Before he allowed the soldier any quinine (the standard treatment for malaria at the time), Wagner-Juaregg took a small sample of the soldier’s blood and inoculated three syphilis patients with the sample, rubbing the blood on their open syphilitic blisters.
It’s unclear how well the malaria treatment worked for those three specific patients – but Wagner-Juaregg’s records show that in the span of one year, he inoculated a total of nine patients with malaria, for the sole purpose of inducing fevers, and six of them made a full recovery. Wagner-Juaregg’s treatment was so successful, in fact, that one of his inoculated patients, an actor who was unable to work due to his dementia, was eventually able to find work again and return to the stage. Two additional patients – a military officer and a clerk – recovered from their once-terminal illnesses and returned to their former careers as well.
When his findings were published in 1918, Wagner-Juaregg’s so-called “fever therapy” swept the globe. The treatment was hailed as a breakthrough – but it still had risks. Malaria itself had a mortality rate of about 15 percent at the time. Many people considered that to be a gamble worth taking, compared to dying a painful, protracted death from syphilis.
Malaria could also be effectively treated much of the time with quinine, whereas other fever-causing illnesses were not so easily treated. Triggering a fever by way of malaria specifically, therefore, became the standard of care.
Tens of thousands of people with syphilitic dementia would go on to be treated with fever therapy until the early 1940s, when a combination of Salvarsan and penicillin caused syphilis infections to decline. Eventually, neurosyphilis became rare, and then nearly unheard of.
Despite his contributions to medicine, it’s important to note that Wagner-Juaregg was most definitely not a person to idolize. In fact, he was an outspoken anti-Semite and proponent of eugenics, arguing that Jews were more prone to mental illness and that people who were mentally ill should be forcibly sterilized. (Wagner-Juaregg later became a Nazi sympathizer during Hitler’s rise to power even though, bizarrely, his first wife was Jewish.) Another problematic issue was that his fever therapy involved experimental treatments on many who, due to their cognitive issues, could not give informed consent.
Lack of consent was also a fundamental problem with the syphilis study at Tuskegee, appalling research that began just 14 years after Wagner-Juaregg published his “fever therapy” findings.
Still, despite his outrageous views, Wagner-Juaregg was awarded the Nobel Prize in Medicine or Physiology in 1927 – and despite some egregious human rights abuses, the miraculous “fever therapy” was partly responsible for taming one of the deadliest plagues in human history.
The Case for an Outright Ban on Facial Recognition Technology
[Editor's Note: This essay is in response to our current Big Question, which we posed to experts with different perspectives: "Do you think the use of facial recognition technology by the police or government should be banned? If so, why? If not, what limits, if any, should be placed on its use?"]
In a surprise appearance at the tail end of Amazon's much-hyped annual product event last month, CEO Jeff Bezos casually told reporters that his company is writing its own facial recognition legislation.
The use of computer algorithms to analyze massive databases of footage and photographs could render human privacy extinct.
It seems that when you're the wealthiest human alive, there's nothing strange about your company––the largest in the world profiting from the spread of face surveillance technology––writing the rules that govern it.
But if lawmakers and advocates fall into Silicon Valley's trap of "regulating" facial recognition and other forms of invasive biometric surveillance, that's exactly what will happen.
Industry-friendly regulations won't fix the dangers inherent in widespread use of face scanning software, whether it's deployed by governments or for commercial purposes. The use of this technology in public places and for surveillance purposes should be banned outright, and its use by private companies and individuals should be severely restricted. As artificial intelligence expert Luke Stark wrote, it's dangerous enough that it should be outlawed for "almost all practical purposes."
Like biological or nuclear weapons, facial recognition poses such a profound threat to the future of humanity and our basic rights that any potential benefits are far outweighed by the inevitable harms.
We live in cities and towns with an exponentially growing number of always-on cameras, installed in everything from cars to children's toys to Amazon's police-friendly doorbells. The use of computer algorithms to analyze massive databases of footage and photographs could render human privacy extinct. It's a world where nearly everything we do, everywhere we go, everyone we associate with, and everything we buy — or look at and even think of buying — is recorded and can be tracked and analyzed at a mass scale for unimaginably awful purposes.
Biometric tracking enables the automated and pervasive monitoring of an entire population. There's ample evidence that this type of dragnet mass data collection and analysis is not useful for public safety, but it's perfect for oppression and social control.
Law enforcement defenders of facial recognition often state that the technology simply lets them do what they would be doing anyway: compare footage or photos against mug shots, drivers licenses, or other databases, but faster. And they're not wrong. But the speed and automation enabled by artificial intelligence-powered surveillance fundamentally changes the impact of that surveillance on our society. Being able to do something exponentially faster, and using significantly less human and financial resources, alters the nature of that thing. The Fourth Amendment becomes meaningless in a world where private companies record everything we do and provide governments with easy tools to request and analyze footage from a growing, privately owned, panopticon.
Tech giants like Microsoft and Amazon insist that facial recognition will be a lucrative boon for humanity, as long as there are proper safeguards in place. This disingenuous call for regulation is straight out of the same lobbying playbook that telecom companies have used to attack net neutrality and Silicon Valley has used to scuttle meaningful data privacy legislation. Companies are calling for regulation because they want their corporate lawyers and lobbyists to help write the rules of the road, to ensure those rules are friendly to their business models. They're trying to skip the debate about what role, if any, technology this uniquely dangerous should play in a free and open society. They want to rush ahead to the discussion about how we roll it out.
We need spaces that are free from government and societal intrusion in order to advance as a civilization.
Facial recognition is spreading very quickly. But backlash is growing too. Several cities have already banned government entities, including police and schools, from using biometric surveillance. Others have local ordinances in the works, and there's state legislation brewing in Michigan, Massachusetts, Utah, and California. Meanwhile, there is growing bipartisan agreement in U.S. Congress to rein in government use of facial recognition. We've also seen significant backlash to facial recognition growing in the U.K., within the European Parliament, and in Sweden, which recently banned its use in schools following a fine under the General Data Protection Regulation (GDPR).
At least two frontrunners in the 2020 presidential campaign have backed a ban on law enforcement use of facial recognition. Many of the largest music festivals in the world responded to Fight for the Future's campaign and committed to not use facial recognition technology on music fans.
There has been widespread reporting on the fact that existing facial recognition algorithms exhibit systemic racial and gender bias, and are more likely to misidentify people with darker skin, or who are not perceived by a computer to be a white man. Critics are right to highlight this algorithmic bias. Facial recognition is being used by law enforcement in cities like Detroit right now, and the racial bias baked into that software is doing harm. It's exacerbating existing forms of racial profiling and discrimination in everything from public housing to the criminal justice system.
But the companies that make facial recognition assure us this bias is a bug, not a feature, and that they can fix it. And they might be right. Face scanning algorithms for many purposes will improve over time. But facial recognition becoming more accurate doesn't make it less of a threat to human rights. This technology is dangerous when it's broken, but at a mass scale, it's even more dangerous when it works. And it will still disproportionately harm our society's most vulnerable members.
Persistent monitoring and policing of our behavior breeds conformity, benefits tyrants, and enriches elites.
We need spaces that are free from government and societal intrusion in order to advance as a civilization. If technology makes it so that laws can be enforced 100 percent of the time, there is no room to test whether those laws are just. If the U.S. government had ubiquitous facial recognition surveillance 50 years ago when homosexuality was still criminalized, would the LGBTQ rights movement ever have formed? In a world where private spaces don't exist, would people have felt safe enough to leave the closet and gather, build community, and form a movement? Freedom from surveillance is necessary for deviation from social norms as well as to dissent from authority, without which societal progress halts.
Persistent monitoring and policing of our behavior breeds conformity, benefits tyrants, and enriches elites. Drawing a line in the sand around tech-enhanced surveillance is the fundamental fight of this generation. Lining up to get our faces scanned to participate in society doesn't just threaten our privacy, it threatens our humanity, and our ability to be ourselves.
[Editor's Note: Read the opposite perspective here.]
Scientists Are Building an “AccuWeather” for Germs to Predict Your Risk of Getting the Flu
Applied mathematician Sara del Valle works at the U.S.'s foremost nuclear weapons lab: Los Alamos. Once colloquially called Atomic City, it's a hidden place 45 minutes into the mountains northwest of Santa Fe. Here, engineers developed the first atomic bomb.
Like AccuWeather, an app for disease prediction could help people alter their behavior to live better lives.
Today, Los Alamos still a small science town, though no longer a secret, nor in the business of building new bombs. Instead, it's tasked with, among other things, keeping the stockpile of nuclear weapons safe and stable: not exploding when they're not supposed to (yes, please) and exploding if someone presses that red button (please, no).
Del Valle, though, doesn't work on any of that. Los Alamos is also interested in other kinds of booms—like the explosion of a contagious disease that could take down a city. Predicting (and, ideally, preventing) such epidemics is del Valle's passion. She hopes to develop an app that's like AccuWeather for germs: It would tell you your chance of getting the flu, or dengue or Zika, in your city on a given day. And like AccuWeather, it could help people alter their behavior to live better lives, whether that means staying home on a snowy morning or washing their hands on a sickness-heavy commute.
Sara del Valle of Los Alamos is working to predict and prevent epidemics using data and machine learning.
Since the beginning of del Valle's career, she's been driven by one thing: using data and predictions to help people behave practically around pathogens. As a kid, she'd always been good at math, but when she found out she could use it to capture the tentacular spread of disease, and not just manipulate abstractions, she was hooked.
When she made her way to Los Alamos, she started looking at what people were doing during outbreaks. Using social media like Twitter, Google search data, and Wikipedia, the team started to sift for trends. Were people talking about hygiene, like hand-washing? Or about being sick? Were they Googling information about mosquitoes? Searching Wikipedia for symptoms? And how did those things correlate with the spread of disease?
It was a new, faster way to think about how pathogens propagate in the real world. Usually, there's a 10- to 14-day lag in the U.S. between when doctors tap numbers into spreadsheets and when that information becomes public. By then, the world has moved on, and so has the disease—to other villages, other victims.
"We found there was a correlation between actual flu incidents in a community and the number of searches online and the number of tweets online," says del Valle. That was when she first let herself dream about a real-time forecast, not a 10-days-later backcast. Del Valle's group—computer scientists, mathematicians, statisticians, economists, public health professionals, epidemiologists, satellite analysis experts—has continued to work on the problem ever since their first Twitter parsing, in 2011.
They've had their share of outbreaks to track. Looking back at the 2009 swine flu pandemic, they saw people buying face masks and paying attention to the cleanliness of their hands. "People were talking about whether or not they needed to cancel their vacation," she says, and also whether pork products—which have nothing to do with swine flu—were safe to buy.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place.
They watched internet conversations during the measles outbreak in California. "There's a lot of online discussion about anti-vax sentiment, and people trying to convince people to vaccinate children and vice versa," she says.
Today, they work on predicting the spread of Zika, Chikungunya, and dengue fever, as well as the plain old flu. And according to the CDC, that latter effort is going well.
Since 2015, the CDC has run the Epidemic Prediction Initiative, a competition in which teams like de Valle's submit weekly predictions of how raging the flu will be in particular locations, along with other ailments occasionally. Michael Johannson is co-founder and leader of the program, which began with the Dengue Forecasting Project. Its goal, he says, was to predict when dengue cases would blow up, when previously an area just had a low-level baseline of sick people. "You'll get this massive epidemic where all of a sudden, instead of 3,000 to 4,000 cases, you have 20,000 cases," he says. "They kind of come out of nowhere."
But the "kind of" is key: The outbreaks surely come out of somewhere and, if scientists applied research and data the right way, they could forecast the upswing and perhaps dodge a bomb before it hit big-time. Questions about how big, when, and where are also key to the flu.
A big part of these projects is the CDC giving the right researchers access to the right information, and the structure to both forecast useful public-health outcomes and to compare how well the models are doing. The extra information has been great for the Los Alamos effort. "We don't have to call departments and beg for data," says del Valle.
When data isn't available, "proxies"—things like symptom searches, tweets about empty offices, satellite images showing a green, wet, mosquito-friendly landscape—are helpful: You don't have to rely on anyone's health department.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place. But del Valle wants more than weekly numbers on a government website; she wants that weather-app-inspired fortune-teller, incorporating the many diseases you could get today, standing right where you are. "That's our dream," she says.
This plot shows the the correlations between the online data stream, from Wikipedia, and various infectious diseases in different countries. The results of del Valle's predictive models are shown in brown, while the actual number of cases or illness rates are shown in blue.
(Courtesy del Valle)
The goal isn't to turn you into a germophobic agoraphobe. It's to make you more aware when you do go out. "If you know it's going to rain today, you're more likely to bring an umbrella," del Valle says. "When you go on vacation, you always look at the weather and make sure you bring the appropriate clothing. If you do the same thing for diseases, you think, 'There's Zika spreading in Sao Paulo, so maybe I should bring even more mosquito repellent and bring more long sleeves and pants.'"
They're not there yet (don't hold your breath, but do stop touching your mouth). She estimates it's at least a decade away, but advances in machine learning could accelerate that hypothetical timeline. "We're doing baby steps," says del Valle, starting with the flu in the U.S., dengue in Brazil, and other efforts in Colombia, Ecuador, and Canada. "Going from there to forecasting all diseases around the globe is a long way," she says.
But even AccuWeather started small: One man began predicting weather for a utility company, then helping ski resorts optimize their snowmaking. His influence snowballed, and now private forecasting apps, including AccuWeather's, populate phones across the planet. The company's progression hasn't been without controversy—privacy incursions, inaccuracy of long-term forecasts, fights with the government—but it has continued, for better and for worse.
Disease apps, perhaps spun out of a small, unlikely team at a nuclear-weapons lab, could grow and breed in a similar way. And both the controversies and public-health benefits that may someday spin out of them lie in the future, impossible to predict with certainty.