Conventional wisdom has it that the more people stay within their own social groups and avoid others, the less likely it is small disease outbreaks turn into full-blown epidemics. But the conventional wisdom is wrong, according to two SFI researchers, and the consequences could reach far beyond epidemiology.
In a paper published in the July 20 early edition of the Proceedings of the National Academy of Sciences, Laurent Hébert-Dufresne and Benjamin Althouse show that when two separate diseases interact with each other, a population clustered into relatively isolated groups can lead to epidemics that spread like wildfire.
“We thought we understood how clustering works,” Hébert-Dufresne says,”but it behaves exactly opposite to what we thought once interactions are added in. Our intuition was totally wrong.”
At the heart of the new study are two effects that have had a lot of attention in recent years—social clustering and coinfection, in which one disease can change the infection dynamics of another—but haven’t been studied together. That, Hébert-Dufresne and Althouse say, turns out to be a major omission
Ordinarily, the pair say, clustering limits outbreaks. Maybe kids in one preschool get sick, for example, but since those kids don’t see kids from other preschools as often, they’re not likely to spread the disease very far. Coinfection often works the other way. Once someone is sick with, say, pneumococcal pneumonia, they’re more likely than others to come down with the flu, lowering the bar for an epidemic of both diseases.
But put the effects together, Hébert-Dufresne and Althouse discovered, and you get something that is more—and different—than the sum of its parts. While clustering works to prevent single-disease epidemics, interactions between diseases like pneumonia and the flu help keep each other going within a social group long enough that one of them can break out into other clusters, becoming a foothold for the other—or perhaps a spark in a dry forest. Both diseases, Althouse says, “can catch fire.” The end result is a larger, more rapidly developing, epidemic than would otherwise be possible.
That conclusion has immediate consequences for public health officials, whose worst-case scenarios might be different or even tame compared with the outbreaks Hébert-Dufresne and Althouse hypothesize. But there are equally important consequences for network scientists and complex systems researchers, who often think in epidemiological terms. Two ideas, for example, might interact with each other so that both spread more rapidly than they would on their own, just as diseases do.
Now that they’ve realized the importance of such interactions, “we hope to take this work in new and different directions in epidemiology, social science, and the study of dynamic networks,” Althouse says. “There’s great potential.”
More information: “Complex dynamics of synergistic coinfections on realistically clustered networks.”PNAS 2015 ; published ahead of print July 20, 2015, DOI: 10.1073/pnas.1507820112
David L. Chandler | MIT News Office
January 14, 2015
Illustration: Jose-Luis Olivares/MIT
Sometimes the response to the outbreak of a disease can make things worse — such as when people panic and flee, potentially spreading the disease to new areas. The ability to anticipate when such overreactions might occur could help public health officials take steps to limit the dangers.
Now a new computer model could provide a way of making such forecasts, based on a combination of data collected from hospitals, social media, and other sources. The model was developed by researchers at MIT, Draper Laboratory, and Ascel Bio, and is described in a paper published in the journal Interface.
The research grew out of earlier studies of how behavior spreads through social networks, explains co-author Marta Gonzalez, an assistant professor of civil and environmental engineering at MIT. The spread of information — and misinformation — about disease outbreaks “had not been studied, and it’s hard to get detailed information on the panic reactions,” Gonzalez says. “How do you quantify panic?”
One way of analyzing those reactions is by studying news reporting on outbreaks, as well as messages posted on social media, and comparing those with data from hospital records about the actual incidence of the disease.
In many cases, the reaction to an outbreak can cause more harm than the disease itself: For example, the researchers say, curtailing travel and distribution of goods can create economic damage, or even lead to rioting and other behavior that can exacerbates a disease’s spread. Wide publicity of an outbreak can also cause health care facilities to be overrun by people concerned about minor symptoms, potentially making it difficult for those affected by the disease to obtain the care they need, the researchers add.
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To study the phenomenon, the team looked at data from three disease outbreaks: the 2009 spread of H1N1 flu in both Mexico and in Hong Kong, and the 2003 spread of SARS in Hong Kong. The model they developed could accurately reproduce the population-level behavior that accompanied those outbreaks.
In these cases, public response was often disproportionate to actual risk; in general, the research showed, diseases that are rare or unusual frequently receive attention that far outpaces the true risk. For example, the SARS outbreak in Hong Kong produced a much stronger public response than H1N1, even though the rate of infection with H1N1 was hundreds of times greater than that of SARS.
This analysis did not specifically address the ongoing Ebola epidemic in West Africa — but once again, Gonzalez says, “The response [is] just not justified by the extent of the disease.”
Three scenarios depicting the simulated spread of a simple epidemic from a single point outbreak. Long-range jumps — mimicking air travel, for example — lead to sub-outbreaks. If long-distance jumps are rare, the main outbreak will quickly merge with the satellite outbreaks, leading to a rippling, wave-like growth (left). As the likelihood of long-distance jumps increases, the epidemic spread exhibits a super-linear power-law growth (center) or a stretched exponential or “metastatic” growth. (Simulations by Oskar Hallatschek, UC Berkeley, and Daniel Fisher, Stanford. Video editing by Christian Collins.)
The current Ebola outbreak shows how quickly diseases can spread with global jet travel.
Yet knowing how to predict the spread of these epidemics is still uncertain, because the complicated models used are not fully understood, says a UC Berkeley biophysicist.
Using a very simple model of disease spread, Oskar Hallatschek, assistant professor of physics, proved that one common assumption is actually wrong. Most models have taken for granted that if disease vectors, such as humans, have any chance of “jumping” outside the initial outbreak area – by plane or train, for example – the outbreak quickly metastasizes into an epidemic.
Hallatschek and co-author Daniel Fisher of Stanford University found instead that if the chance of long-distance dispersal is low enough, the disease spreads quite slowly, like a wave rippling out from the initial outbreak. This type of spread was common centuries ago when humans rarely traveled. The Black Death spread through 14th-century Europe as a wave, for example.
But if the chance of jumping is above a threshold level – which is often the situation today with frequent air travel –the diseases can generate enough satellite outbreaks to spread like wildfire. And the greater the chance that people can hop around the globe, the faster the spread.
“With our simple model, we clearly show that one of the key factors that controls the spread of infection is how common long-range jumps are in the dispersal of a disease,” said Hallatschek, who is the William H. McAdams Chair in physics and a member of the UC Berkeley arm of the California Institute for Quantitative Biosciences (QB3). “And what matters most are the rare cases of extremely long jumps, the individuals who take plane trips to distant places and potentially spread the disease.”
To find out when whooping cough started making a comeback in Ohio, or how often measles kills in America, we turn to historical records. But those records aren’t very useful when they’re squirreled away in a distant office basement. The same goes for when they are embedded in a report—you can only look at them in the same way you might admire a painting, but you cannot drop the data into a spreadsheet and hunt for statistical significance. If you are only looking at a couple years’ worth of information that formatting dilemma is not such a big deal. You can scour the data and manually punch it into your analysis. It only becomes a huge problem when you are looking at hundreds or thousands of data points.
Such is the problem that public health experts at University of Pittsburgh encountered when they were exploring old medical data and developing models that predict future outbreaks. “We found ourselves going back and pulling out historical datasets repeatedly. We kept doing it over and over and finally got to the point where we thought it would be not only a service to ourselves but everybody if all the data was made digital and open access,” says Donald Burke, the dean of Pittsburgh’s graduate school of public health.
Four years ago, buoyed by funds from the National Institutes of Health and the Gates Foundation, they started the process of digitalizing 125 years worth of medical records. The endeavor was dubbed Project Tycho, named for the Danish nobleman Tycho Brahe who made the voluminous astronomical observations that Kepler later tapped to develop the laws of planetary motion. (But no pressure, right?)
In the research world, that’s a big accomplishment. Making this data usable takes more than casually monitoring a scanner while sipping coffee. The data has to be made uniform, a tedious process of manual input with unenviable tasks like removing periods, dashes and other inconsistencies while identifying data gaps.
Pittsburgh researchers also gave their new data trove a test drive to illustrate what could be done with the data. They mined Tycho for information on eight common diseases detailed in the records—polio, measles, rubella, mumps, hepatitis A, diphtheria and pertussis. Looking at available records before and after vaccines were discovered for those diseases, they estimated that 103 million cases of those contagious diseases have been prevented since 1924, (assuming the reductions were all attributable to vaccination programs). Their findings are published in this week’sNew England Journal of Medicine. The data also points to what can happen when communities become too lax about vaccinations (among other factors). They quantified the resurgence in recent years of pertussis throughout the country, particularly in the Midwest to Northwest and in the Northeast and also ongoing cases of mumps. “Reported rates of vaccine refusal or delay are increasing,” the authors write. “Failure to vaccinate is believed to have contributed to the reemergence of pertussis, including the large 2012 epidemic.”
When vaccines work well, sometimes “people no longer fear the disease and they undervalue the vaccine and in some ways that is what is going on right now,” says Burke, pointing to the discredited vaccine-autism link which prompted some parents to turn away from childhood vaccines. With this newly available data collection, more can be done than simply looking at where the disease is happening—or not happening. Researchers can begin looking for drivers of disease and identifying patterns about the burden of disease by say, climate or socioeconomic-status.
Flip through some of the data yourself here after it becomes searchable to the public on November 28.
[One has to register to view data, for institution I just entered private citizen and my registration was accepted. The database interface is very user friendly!]
December 6, 2013 |Project Tycho™ release featured in the New York Times
The release and publication of Project Tycho™ data has been featured in an article of the New York Times online and print version of Thursday November 28th entitled “The Vaccination Effect: 100 Million Cases of Contagious Disease Prevented”. It emphasizes that the large amount of data digitized by the project provides an invaluable resource for science and policy and the importance of vaccination programs in the United States.
December 6, 2013 |Project Tycho™ data available on HealthData.gov
Through a collaboration with the Open Government Initiative, Project Tycho™ data have been listed on HealthData.gov as new open access resource for governmental data. In addition on the listing, HealthData.gov has agreed to host Project Tycho™level 1 and level 2 data that can each be downloaded from this site as a one CSV file with a single click. Comments on this release have been made in the HealthData.gov blog.
November 28, 2013 |Project Tycho™ Data Version 1.0.0 released for public access
After four years of data digitization and processing, the Project Tycho™ Web site provites open access to newly digitized and integrated data from the entire 125 years history of United States weekly nationally notifiable disease surveillance data since 1888. These data can now be used by scientists, decision makers, investors, and the general public for any purpose. The Project Tycho™ aim is to advance the availability and use of public health data for science and decision making in public health, leading to better programs and more efficient control of diseases. Read full press release.
Three levels of data have been made available: Level 1 data include data that have been standardized for specific analyses, Level 2 datainclude standardized data that can be used immediately for analysis, and Level 3 data are raw data that cannot be used for analysis without extensive data management. See the video tutoral.
November 28, 2013 |A Project Tycho™ study estimates that 100 million cases of contagious diseases have been prevented by vaccination programs in the United States since 1924
In a paper published in the New England Journal of Medicine entitled “Contagious diseases in the United States from 1888 to the present,” aProject Tycho™ study estimates that over 100 million cases have been prevented in the U.S. since 1924 by vaccination programs against polio, measles, mumps, rubella, hepatitis A, diphtheria, and pertussis (whooping cough). Vaccination programs against these diseases have been in place for decades but epidemics continue to occur. Despite the availability of a pertussis vaccine since the 1920s, the largest pertussis epidemic in the U.S. since 1959 occurred last year. This study was funded by the Bill & Melinda Gates Foundation and the National Institutes of Health and all data used for this study have been released through the online Project Tycho™ data system as level 1 data.
“Historical records are a precious yet undervalued resource. As Danish philosopher Soren Kierkegaard said, we live forward but understand backward,” explained Dr. Burke, senior author on the paper. “By ‘rescuing’ these historical disease data and combining them into a single, open-access, computable system, we can now better understand the devastating impact of epidemic diseases, and the remarkable value of vaccines in preventing illness and death.” See an interview with the authors and an animation on the analysis.
Do you want to be a disease detective? the Centers for Disease Control and Prevention (CDC) have released a new app, Solve the Outbreak.
New outbreaks happen every day and CDC’s disease detectives are on the front lines, working 24/7 to save lives and protect people. When a new outbreak happens, disease detectives are sent in to figure out how outbreaks are started, before they can spread. with this new, free app for the iPad, you can play the role of an Epidemic Intelligence Service agent. Find clues about outbreaks and make tough decisions about what to do next: Do you quarantine the village? Talk to people who are sick? Ask for more lab results?
With fictional outbreaks based on real-life cases, you’ll have to puzzle through the evidence to earn points for each clue. The better your answers, the higher your score – and the more quickly you’ll save lives…
The massive collaborative project known as The Global Burden of Disease Study 2010 (GBD 2010) has just been published. The GBD is a comprehensive assessment of mortality and loss of health due to diseases, injuries and risk factors in all regions of the world.
From the Executive Summary:
The Global Burden of Disease Study 2010 (GBD 2010) is the largest ever systematic effort to describe the global distribution and causes of a wide array of major diseases, injuries, and health risk factors. The results show that infectious diseases, maternal and child illness, and malnutrition now cause fewer deaths and less illness than they did twenty years ago. As a result, fewer children are dying every year, but more young and middle-aged adults are dying and suffering from disease and injury, as non-communicable diseases, such as cancer and heart disease, become the dominant causes of death and disability worldwide. Since 1970…
It’s winter, flu season, and you’re at your computer feeling a bit woozy, with an unwanted swelling in the back of your throat and a headache coming on. If you’re like millions of other people, you might engage in a moment of Internet-enabled self-diagnosis. You pop your symptoms into a search engine, and in the blink of an eye dozens of health-related websites appear on your screen. That search supplied you with information—some useful and some not—but in today’s hyper-connected world, it also supplied a data point for those who survey disease outbreaks by monitoring how people report symptoms via social media. In fact, social media, cell phones, and other communication modes have opened up a two-way street in health research, supplying not just a portal for delivering information to the public but also a channel by which people reveal their concerns, locations, and physical movements from one place to another.
That two-way street is transforming disease surveillance and the way that health officials respond to disasters and pandemics. It’s also raising hard questions about privacy and about how data streams generated by cell-phone and social-media use might be made available for health research. “There’s a challenge here in that some of these [data] systems are tightening in terms of access,” says John Brownstein, director of the computational epidemiology group at Children’s Hospital Boston and an associate professor of pediatrics at Harvard Medical School. “But we are seeing a movement towards data philanthropy in that companies are looking for ways to release data for health research without risking privacy. And at the same time, government officials and institutions at all levels see the data’s value and potential. To me, that’s very exciting.”
(Read the entire article for insights in improving surveillance, investigating social networks, and accuracy of social networks)
A pioneer in this field, Brownstein worked with collaborators at Children’s Hospital Boston to launch one of the earliest social media tools in infectious disease surveillance, a website called HealthMap (http://healthmap.org/) that mines news websites, government alerts, eyewitness accounts, and other data sources for outbreaks of various illnesses reported around the world. The site aggregates those cases on a global map, with outbreaks displayed in real time. Brownstein’s team recently launched Outbreaks Near Me, an iPhone application that delivers HealthMap directly to cell-phone users.
Flu Near You (https://flunearyou.org/), a website created with the American Public Health Association and the Skoll Global Threats Fund of San Francisco, California, which allows individuals to serve as potential disease sentinels by reporting their health status on a weekly basis.
Google launched Google Flu Trends (http://www.google.org/flutrends/), a website that allows people to compare volumes of flu-related search activity against reported incidence rates for the illness displayed graphically on a map.
This blog presents a sampling of health and medical news and resources for all. Selected articles and resources will hopefully be of general interest but will also encourage further reading through posted references and other links. Currently I am focusing on public health, basic and applied research and very broadly on disease and healthy lifestyle topics.
Several times a month I will post items on international and global health issues. My Peace Corps Liberia experience (1980-81) has formed me as a global citizen in many ways and has challenged me to think of health and other topics in a more holistic manner.
Do you have an informational question in the health/medical area? Email me at jmflahiff@yahoo.com I will reply within 48 hours.
My professional work experience and education includes over 15 years experience as a medical librarian and a Master’s in Library Science. In my most recent position I enjoyed contributing to our library’s blog, performing in depth literature searches, and collaborating with faculty, staff, students, and the general public.
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