The Algorithm Will See You Now
There's a quiet revolution going on in medicine. It's driven by artificial intelligence, but paradoxically, new technology may put a more human face on healthcare.
AI's usefulness in healthcare ranges far and wide.
Artificial intelligence is software that can process massive amounts of information and learn over time, arriving at decisions with striking accuracy and efficiency. It offers greater accuracy in diagnosis, exponentially faster genome sequencing, the mining of medical literature and patient records at breathtaking speed, a dramatic reduction in administrative bureaucracy, personalized medicine, and even the democratization of healthcare.
The algorithms that bring these advantages won't replace doctors; rather, by offloading some of the most time-consuming tasks in healthcare, providers will be able to focus on personal interactions with patients—listening, empathizing, educating and generally putting the care back in healthcare. The relationship can focus on the alleviation of suffering, both the physical and emotional kind.
Challenges of Getting AI Up and Running
The AI revolution, still in its early phase in medicine, is already spurring some amazing advances, despite the fact that some experts say it has been overhyped. IBM's Watson Health program is a case in point. IBM capitalized on Watson's ability to process natural language by designing algorithms that devour data like medical articles and analyze images like MRIs and medical slides. The algorithms help diagnose diseases and recommend treatment strategies.
But Technology Review reported that a heavily hyped partnership with the MD Anderson Cancer Center in Houston fell apart in 2017 because of a lack of data in the proper format. The data existed, just not in a way that the voraciously data-hungry AI could use to train itself.
The hiccup certainly hasn't dampened the enthusiasm for medical AI among other tech giants, including Google and Apple, both of which have invested billions in their own healthcare projects. At this point, the main challenge is the need for algorithms to interpret a huge diversity of data mined from medical records. This can include everything from CT scans, MRIs, electrocardiograms, x-rays, and medical slides, to millions of pages of medical literature, physician's notes, and patient histories. It can even include data from implantables and wearables such as the Apple Watch and blood sugar monitors.
None of this information is in anything resembling a standard format across and even within hospitals, clinics, and diagnostic centers. Once the algorithms are trained, however, they can crunch massive amounts of data at blinding speed, with an accuracy that matches and sometimes even exceeds that of highly experienced doctors.
Genome sequencing, for example, took years to accomplish as recently as the early 2000s. The Human Genome Project, the first sequencing of the human genome, was an international effort that took 13 years to complete. In April of this year, Rady Children's Institute for Genomic Medicine in San Diego used an AI-powered genome sequencing algorithm to diagnose rare genetic diseases in infants in about 20 hours, according to ScienceDaily.
"Patient care will always begin and end with the doctor."
Dr. Stephen Kingsmore, the lead author of an article published in Science Translational Medicine, emphasized that even though the algorithm helped guide the treatment strategies of neonatal intensive care physicians, the doctor was still an indispensable link in the chain. "Some people call this artificial intelligence, we call it augmented intelligence," he says. "Patient care will always begin and end with the doctor."
One existing trend is helping to supply a great amount of valuable data to algorithms—the electronic health record. Initially blamed for exacerbating the already crushing workload of many physicians, the EHR is emerging as a boon for algorithms because it consolidates all of a patient's data in one record.
Examples of AI in Action Around the Globe
If you're a parent who has ever taken a child to the doctor with flulike symptoms, you know the anxiety of wondering if the symptoms signal something serious. Kang Zhang, M.D., Ph.D., the founding director of the Institute for Genomic Medicine at the University of California at San Diego, and colleagues developed an AI natural language processing model that used deep learning to analyze the EHRs of 1.3 million pediatric visits to a clinic in Guanzhou, China.
The AI identified common childhood diseases with about the same accuracy as human doctors, and it was even able to split the diagnoses into two categories—common conditions such as flu, and serious, life-threatening conditions like meningitis. Zhang has emphasized that the algorithm didn't replace the human doctor, but it did streamline the diagnostic process and could be used in a triage capacity when emergency room personnel need to prioritize the seriously ill over those suffering from common, less dangerous ailments.
AI's usefulness in healthcare ranges far and wide. In Uganda and several other African nations, AI is bringing modern diagnostics to remote villages that have no access to traditional technologies such as x-rays. The New York Times recently reported that there, doctors are using a pocket-sized, hand-held ultrasound machine that works in concert with a cell phone to image and diagnose everything from pneumonia (a common killer of children) to cancerous tumors.
The beauty of the highly portable, battery-powered device is that ultrasound images can be uploaded on computers so that physicians anywhere in the world can review them and weigh in with their advice. And the images are instantly incorporated into the patient's EHR.
Jonathan Rothberg, the founder of Butterfly Network, the Connecticut company that makes the device, told The New York Times that "Two thirds of the world's population gets no imaging at all. When you put something on a chip, the price goes down and you democratize it." The Butterfly ultrasound machine, which sells for $2,000, promises to be a game-changer in remote areas of Africa, South America, and Asia, as well as at the bedsides of patients in developed countries.
AI algorithms are rapidly emerging in healthcare across the U.S. and the world. China has become a major international player, set to surpass the U.S. this year in AI capital investment, the translation of AI research into marketable products, and even the number of often-cited research papers on AI. So far the U.S. is still the leader, but some experts describe the relationship between the U.S. and China as an AI cold war.
"The future of machine learning isn't sentient killer robots. It's longer human lives."
The U.S. Food and Drug Administration expanded its approval of medical algorithms from two in all of 2017 to about two per month throughout 2018. One of the first fields to be impacted is ophthalmology.
One algorithm, developed by the British AI company DeepMind (owned by Alphabet, the parent company of Google), instantly scans patients' retinas and is able to diagnose diabetic retinopathy without needing an ophthalmologist to interpret the scans. This means diabetics can get the test every year from their family physician without having to see a specialist. The Financial Times reported in March that the technology is now being used in clinics throughout Europe.
In Copenhagen, emergency service dispatchers are using a new voice-processing AI called Corti to analyze the conversations in emergency phone calls. The algorithm analyzes the verbal cues of callers, searches its huge database of medical information, and provides dispatchers with onscreen diagnostic information. Freddy Lippert, the CEO of EMS Copenhagen, notes that the algorithm has already saved lives by expediting accurate diagnoses in high-pressure situations where time is of the essence.
Researchers at the University of Nottingham in the UK have even developed a deep learning algorithm that predicts death more accurately than human clinicians. The algorithm incorporates data from a huge range of factors in a chronically ill population, including how many fruits and vegetables a patient eats on a daily basis. Dr. Stephen Weng, lead author of the study, published in PLOS ONE, said in a press release, "We found machine learning algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert."
New digital technologies are allowing patients to participate in their healthcare as never before. A feature of the new Apple Watch is an app that detects cardiac arrhythmias and even produces an electrocardiogram if an abnormality is detected. The technology, approved by the FDA, is helping cardiologists monitor heart patients and design interventions for those who may be at higher risk of a cardiac event like a stroke.
If having an algorithm predict your death sends a shiver down your spine, consider that algorithms may keep you alive longer. In 2018, technology reporter Tristan Greene wrote for Medium that "…despite the unending deluge of panic-ridden articles declaring AI the path to apocalypse, we're now living in a world where algorithms save lives every day. The future of machine learning isn't sentient killer robots. It's longer human lives."
The Risks of AI Compiling Your Data
To be sure, the advent of AI-infused medical technology is not without its risks. One risk is that the use of AI wearables constantly monitoring our vital signs could turn us into a nation of hypochondriacs, racing to our doctors every time there's a blip in some vital sign. Such a development could stress an already overburdened system that suffers from, among other things, a shortage of doctors and nurses. Another risk has to do with the privacy protections on the massive repository of intimately personal information that AI will have on us.
In an article recently published in the Journal of the American Medical Association, Australian researcher Kit Huckvale and colleagues examined the handling of data by 36 smartphone apps that assisted people with either depression or smoking cessation, two areas that could lend themselves to stigmatization if they fell into the wrong hands.
Out of the 36 apps, 33 shared their data with third parties, despite the fact that just 25 of those apps had a privacy policy at all and out of those, only 23 stated that data would be shared with third parties. The recipients of all that data? It went almost exclusively to Facebook and Google, to be used for advertising and marketing purposes. But there's nothing to stop it from ending up in the hands of insurers, background databases, or any other entity.
Even when data isn't voluntarily shared, any digital information can be hacked. EHRs and even wearable devices share the same vulnerability as any other digital record or device. Still, the promise of AI to radically improve efficiency and accuracy in healthcare is hard to ignore.
AI Can Help Restore Humanity to Medicine
Eric Topol, director of the Scripps Research Translational Institute and author of the new book Deep Medicine, says that AI gives doctors and nurses the most precious gift of all: time.
Topol welcomes his patients' use of the Apple Watch cardiac feature and is optimistic about the ways that AI is revolutionizing medicine. He says that the watch helps doctors monitor how well medications are working and has already helped to prevent strokes. But in addition to that, AI will help bring the humanity back to a profession that has become as cold and hard as a stainless steel dissection table.
"When I graduated from medical school in the 1970s," he says, "you had a really intimate relationship with your doctor." Over the decades, he has seen that relationship steadily erode as medical organizations demanded that doctors see more and more patients within ever-shrinking time windows.
"Doctors have no time to think, to communicate. We need to restore the mission in medicine."
In addition to that, EHRs have meant that doctors and nurses are getting buried in paperwork and administrative tasks. This is no doubt one reason why a recent study by the World Health Organization showed that worldwide, about 50 percent of doctors suffer from burnout. People who are utterly exhausted make more mistakes, and medical clinicians are no different from the rest of us. Only medical mistakes have unacceptably high stakes. According to its website, Johns Hopkins University recently announced that in the U.S. alone, 250,000 people die from medical mistakes each year.
"Doctors have no time to think, to communicate," says Topol. "We need to restore the mission in medicine." AI is giving doctors more time to devote to the thing that attracted them to medicine in the first place—connecting deeply with patients.
There is a real danger at this juncture, though, that administrators aware of the time-saving aspects of AI will simply push doctors to see more patients, read more tests, and embrace an even more crushing workload.
"We can't leave it to the administrators to just make things worse," says Topol. "Now is the time for doctors to advocate for a restoration of the human touch. We need to stand up for patients and for the patient-doctor relationship."
AI could indeed be a game changer, he says, but rather than squander the huge benefits of more time, "We need a new equation going forward."
A vaccine for Lyme disease could be coming. But will patients accept it?
For more than two decades, Marci Flory, a 40-year-old emergency room nurse from Lawrence, Kan., has battled the recurring symptoms of chronic Lyme disease, an illness which she believes began after being bitten by a tick during her teenage years.
Over the years, Flory has been plagued by an array of mysterious ailments, ranging from fatigue to crippling pain in her eyes, joints and neck, and even postural tachycardia syndrome or PoTS, an abnormal increase in heart rate after sitting up or standing. Ten years ago, she began to experience the onset of neurological symptoms which ranged from brain fog to sudden headaches, and strange episodes of leg weakness which would leave her unable to walk.
“Initially doctors thought I had ALS, or less likely, multiple sclerosis,” she says. “But after repeated MRI scans for a year, they concluded I had a rare neurological condition called acute transverse myelitis.”
But Flory was not convinced. After ordering a variety of private blood tests, she discovered she was infected with a range of bacteria in the genus Borrelia that live in the guts of ticks, the infectious agents responsible for Lyme disease.
“It made sense,” she says. “Looking back, I was bitten in high school and misdiagnosed with mononucleosis. This was probably the start, and my immune system kept it under wraps for a while. The Lyme bacteria can burrow into every tissue in the body, go into cyst form and become dormant before reactivating.”
The reason why cases of Lyme disease are increasing is down to changing weather patterns, triggered by climate change, meaning that ticks are now found across a much wider geographic range than ever before.
When these species of bacteria are transmitted to humans, they can attack the nervous system, joints and even internal organs which can lead to serious health complications such as arthritis, meningitis and even heart failure. While Lyme disease can sometimes be successfully treated with antibiotics if spotted early on, not everyone responds to these drugs, and for patients who have developed chronic symptoms, there is no known cure. Flory says she knows of fellow Lyme disease patients who have spent hundreds of thousands of dollars seeking treatments.
Concerningly, statistics show that Lyme and other tick-borne diseases are on the rise. Recently released estimates based on health insurance records suggest that at least 476,000 Americans are diagnosed with Lyme disease every year, and many experts believe the true figure is far higher.
The reason why the numbers are growing is down to changing weather patterns, triggered by climate change, meaning that ticks are now found across a much wider geographic range than ever before. Health insurance data shows that cases of Lyme disease have increased fourfold in rural parts of the U.S. over the last 15 years, and 65 percent in urban regions.
As a result, many scientists who have studied Lyme disease feel that it is paramount to bring some form of protective vaccine to market which can be offered to people living in the most at-risk areas.
“Even the increased awareness for Lyme disease has not stopped the cases,” says Eva Sapi, professor of cellular and molecular biology at the University of New Haven. “Some of these patients are looking for answers for years, running from one doctor to another, so that is obviously a very big cost for our society at so many levels.”
Emerging vaccines – and backlash
But with the rising case numbers, interest has grown among the pharmaceutical industry and research communities. Vienna-based biotech Valneva have partnered with Pfizer to take their vaccine – a seasonal jab which offers protection against the six most common strains of Lyme disease in the northern hemisphere – into a Phase III clinical trial which began in August. Involving 6,000 participants in a number of U.S. states and northern Europe where Lyme disease is endemic, it could lead to a licensed vaccine by 2025, if it proves successful.
“For many years Lyme was considered a small market vaccine,” explains Monica E. Embers, assistant professor of parasitology at Tulane University in New Orleans. “Now we know that this is a much bigger problem, Pfizer has stepped up to invest in preventing this disease and other pharmaceutical companies may as well.”
Despite innovations, patient communities and their representatives remain ambivalent about the idea of a vaccine. Some of this skepticism dates back to the failed LYMErix vaccine which was developed in the late 1990s before being withdrawn from the market.
At the same time, scientists at Yale University are developing a messenger RNA vaccine which aims to train the immune system to respond to tick bites by exposing it to 19 proteins found in tick saliva. Whereas the Valneva vaccine targets the bacteria within ticks, the Yale vaccine attempts to provoke an instant and aggressive immune response at the site of the bite. This causes the tick to fall off and limits the potential for transmitting dangerous infections.
But despite these innovations, patient communities and their representatives remain ambivalent about the idea of a vaccine. Some of this skepticism dates back to the failed LYMErix vaccine which was developed in the late 1990s before being withdrawn from the market in 2002 after concerns were raised that it might induce autoimmune reactions in humans.
While this theory was ultimately disproved, the lingering stigma attached to LYMErix meant that most vaccine manufacturers chose to stay away from the disease for many years, something which Gregory Poland, head of the Mayo Clinic’s Vaccine Research Group in Minnesota, describes as a tragedy.
“Since 2002, we have not had a human Lyme vaccine in the U.S. despite the increasing number of cases,” says Poland. “Pretty much everyone in the field thinks they’re ten times higher than the official numbers, so you’re probably talking at least 400,000 each year. It’s an incredible burden but because of concerns about anti-vax protestors, until very recently, no manufacturer has wanted to touch this.”
Such was the backlash surrounding the failed LYMErix program that scientists have even explored the most creative of workarounds for protecting people in tick-populated regions, without needing to actually vaccinate them. One research program at the University of Tennessee came up with the idea of leaving food pellets containing a vaccine in woodland areas with the idea that rodents would eat the pellets, and the vaccine would then kill Borrelia bacteria within any ticks which subsequently fed on the animals.
Even the Pfizer-Valneva vaccine has been cautiously designed to try and allay any lingering concerns, two decades after LYMErix. “The concept is the same as the original LYMErix vaccine, but it has been made safer by removing regions that had the potential to induce autoimmunity,” says Embers. “There will always be individuals who oppose vaccines, Lyme or otherwise, but it will be a tremendous boost to public health to have the option.”
Vaccine alternatives
Researchers are also considering alternative immunization approaches in case sufficiently large numbers of people choose to reject any Lyme vaccine which gets approved. Researchers at UMass Chan Medical School have developed an artificially generated antibody, administered via an annual injection, which is capable of killing Borrelia bacteria in the guts of ticks before they can get into the human host.
So far animal studies have shown it to be 100 percent effective, while the scientists have completed a Phase I trial in which they tested it for safety on 48 volunteers in Nebraska. Because this approach provides the antibody directly, rather than triggering the human immune system to produce the antibody like a vaccine would, Embers predicts that it could be a viable alternative for the vaccine hesitant as well as providing an option for immunocompromised individuals who cannot produce enough of their own antibodies.
At the same time, many patient groups still raise concerns over the fact that numerous diagnostic tests for Lyme disease have been reported to have a poor accuracy. Without this, they argue that it is difficult to prove whether vaccines or any other form of immunization actually work. “If the disease is not understood enough to create a more accurate test and a universally accepted treatment protocol, particularly for those who weren’t treated promptly, how can we be sure about the efficacy of a vaccine?” says Natasha Metcalf, co-founder of the organization Lyme Disease UK.
Flory points out that there are so many different types of Borrelia bacteria which cause Lyme disease, that the immunizations being developed may only stop a proportion of cases. In addition, she says that chronic Lyme patients often report a whole myriad of co-infections which remain poorly understood and are likely to also be involved in the disease process.
Marci Flory undergoes an infusion in an attempt to treat her Lyme disease symptoms.
Marci Flory
“I would love to see an effective Lyme vaccine but I have my reservations,” she says. “I am infected with four types of Borrelia bacteria, plus many co-infections – Babesia, Bartonella, Erlichiosis, Rickettsia, and Mycoplasma – all from a single Douglas County Kansas tick bite. Lyme never travels alone and the vaccine won’t protect against all the many strains of Borrelia and co-infections.”
Valneva CEO Thomas Lingelbach admits that the Pfizer-Valneva vaccine is not perfect, but predicts that it will still have significant impact if approved.
“We expect the vaccine to have 75 percent plus efficacy,” he says. “There is this legacy around the old Lyme vaccines, but the world is very, very different today. The number of clinical manifestations known to be caused by infection with Lyme Borreliosis has significantly increased, and the understanding around severity has certainly increased.”
Embers agrees that while it will still be important for doctors to monitor for other tick-borne infections which are not necessarily covered by the vaccine, having any clinically approved jab would still represent a major step forward in the fight against the disease.
“I think that any vaccine must be properly vetted, and these companies are performing extensive clinical trials to do just that,” she says. “Lyme is the most common tick-borne disease in the U.S. so the public health impact could be significant. However, clinicians and the general public must remain aware of all of the other tick-borne diseases such as Babesia and Anaplasma, and continue to screen for those when a tick bite is suspected.”
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.