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.
…
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.”
The headlines in the opening to this story are not taken from today’s newspapers. They were published in the Chicago Tribune 96 years ago. From 1918 to 1919, the world was in the throes of the greatest plague in recorded history. It was called the Spanish Flu, named for the country where people thought it had originated..
The headlines we are seeing today over fear of the spread of the Ebola virus are very real. Many of the events that have already taken place — such as the cruise ship being banned from entering Belize — adds to our fears, although the restrictions were probably unnecessary. We are a country that is totally unprepared for an epidemic of national proportions, yet this is not the first time wehave been tested.
The headlines in the opening to this story are not taken from today’s newspapers. They were published in the Chicago Tribune 96 years ago. From 1918 to 1919, the world was in the throes of the greatest plague in recorded history. It was called the Spanish Flu, named for the country where people thought it had originated……
Research by scientists at the University of Liverpool has found that greater consideration of the limitations and uncertainties present in every infectious disease model would improve its effectiveness/usefulness and value.
Infectious disease dynamical modelling plays a central role in planning for outbreaks of human and livestock diseases, in projecting how they might progress and guiding and informing policy responses.
Modelling is commissioned by governments or may be developed independently by researchers. It has been used to inform policy decisions for human and animal diseases such as SARS, H1N1 swine influenza, foot-and-mouth disease and is being used to inform action in the campaign to control bovine TB.
In a study published in PLOS One, researchers analysed scientific papers, interviews, policies, reports and outcomes of previous infectious diseases outbreaks in the UK to ascertain the role uncertainties played in previous models and how these were understood by both the designers of the model and the users of the model.
They found that many models used to respond to epidemics provided only cursory reference to the uncertainties of the information and data or the parameters used. Whilst the models were uncertain many still informed action.
Dr Rob Christley, from the University’s Institute of Infection and Global Health, said: “It is accepted that models will never be able to predict 100% the size, shape or form of an outbreak and it is recognised that a level of uncertainty always exists in modelling. However, modellers often fear detailed discussion of this uncertainty will undermine the model in the eyes of policy makers.
“This study found that the uncertainties and limitations of a model are sometimes hidden and sometimes revealed, and that which occurs is context dependent.
“Whilst it isn’t possible to calculate the level of uncertainty, a better understanding and communication of the model’s limitations is needed and could lead to better policy.”
A model is produced by individuals who have to decide what is important and need to bring together data and information which could include population data, age of population, proximity, type of disease. Uncertainty can occur at all stages of the process from weaknesses in the quality and type of data used, assumptions made about the infectious agent itself, and about the world in which the disease is circulating, all the way through to the technical aspects of the model.
###
The research team comprised veterinary scientists and epidemiologists, sociologists, microbiologists and environmental scientists.
The research, undertaken in collaboration with the University of Lancaster and funded by the UK Research Councils’ Rural Economy and Land Use is, is published in PLOS One.
New research has looked at whether social media could be used to track an event or phenomenon, such as flu outbreaks and rainfall rates. The study by academics at the University of Bristol‘s Intelligent Systems Laboratory is published online in ACM Transactions on Intelligent Systems and Technology.
Social networks, such as Facebook and microblogging services like Twitter, have only been around for a short time but in that time they have provided shapshots of real life by forming, electronically, public expression and interaction.
The research by Professor Nello Cristianini and Vasileios Lampos in the University’s Intelligent Systems Laboratory, geo-tagged user posts on the microblogging service of Twitter as their input data to investigate two case studies.
The first case study looked at levels of rainfall in a given location and time using the content of tweets. The second case study collected regional flu-like illness rates from tweets to find out if an epidemic was emerging.
The study builds on previous research that reported a methodology that used tweets to track flu-like illness rates in several UK regions. The research also demonstrated a tool, the Flu Detector, which uses the content of Twitter to map current flu rates in several UK regions.
Professor Nello Cristianini, speaking about the research, said: “Twitter, in particular, encouraged their 200 million users worldwide to make their posts, commonly known as tweets, publicly available as well as tagged with the user’s location. This has led to a new wave of experimentation and research using an independent stream of information.
“Our research has demonstrated a method, by using the content of Twitter, to track an event, when it occurs and the scale of it. We were able to turn geo-tagged user posts on the microblogging service of Twitter to topic-specific geolocated signals by selecting textual features that showed the content and understanding of the text.”…
HONG KONG (Reuters) – A growing number of livestock, such as cows and pigs, are fuelling new animal epidemics worldwide and posing more severe problems in developing countries as it threatens their food security, according to a report released on Friday.
Epidemics in recent years, such as SARS and the H1N1 swine flu, are estimated to have caused billions of dollars in economic costs.
Some 700 million people keep farm animals in developing countries and these animals generate up to 40 percent of household income, the report by the International Livestock Research Institute said.
“Wealthy countries are effectively dealing with livestock diseases, but in Africa and Asia, the capacity of veterinary services to track and control outbreaks is lagging dangerously behind livestock intensification,” John McDermott and Delia Grace at the Nairobi-based institute said in a statement on the report.
“This lack of capacity is particularly dangerous because many poor people in the world still rely on farm animals to feed their families, while rising demand for meat, milk and eggs among urban consumers in the developing world is fueling a rapid intensification of livestock production.”
Seventy-five percent of emerging infectious diseases originate in animals, they added. Of these 61 percent are transmissible between animals and humans.
“A new disease emerges every four months; many are trivial but HIV, SARS and avian influenza (eg. H5N1) illustrate the huge potential impacts,” McDermott and Grace wrote in the report.
HUGE ECONOMIC COSTS
Epidemics like SARS in 2003, sporadic outbreaks of the H5N1 avian flu since 1997 and the H1N1 swine flu pandemic of 2009 racked up enormous economic costs around the world.
While SARS cost between $50 billion to $100 billion, the report cited a World Bank estimate in 2010 which pinned the potential costs of an avian flu pandemic at $3 trillion.
The report warned that rapid urbanization and climate change could act as “wild cards,” altering the present distribution of diseases, sometimes “dramatically for the worse.”
The two researchers urged developing countries to improve animal disease surveillance and speed up testing procedures to help contain livestock epidemics before they become widespread.
(Reporting by Tan Ee Lyn; Editing by Yoko Nishikawa)
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.
While I will never be be able to keep up with the universe of current health/medical news, I subscribe to the following to glean entries for this blog.
Health and Medical News and Resources 