To help the public better understand how measles can spread, a team of infectious disease computer modelers at the University of Pittsburgh has launched a free, mobile-friendly tool that lets users simulate measles outbreaks in cities across the country.
The tool is part of the Pitt team’s Framework for Reconstructing Epidemiological Dynamics, or FRED, that it previously developed to simulate flu epidemics. FRED is based on anonymized U.S. census data that captures demographic and geographic distributions of different communities. It also incorporates details about the simulated disease, such as how contagious it is.
A free, mobile-friendly tool lets users simulate potential measles outbreaks in cities across the country. Credit: University of Pittsburgh Graduate School of Public Health.
Just as you might turn to Twitter or Facebook for a pulse on what’s happening around you, researchers involved in an infectious disease computational modeling project are turning to anonymized social media and other publicly available Web data to improve their ability to forecast emerging outbreaks and develop tools that can help health officials as they respond.
Mining Wikipedia Data
Incorporating real-time, anonymized data from Wikipedia and other novel sources of information is aiding efforts to forecast and respond to emerging outbreaks. Credit: Stock image.
“When it comes to infectious disease forecasting, getting ahead of the curve is problematic because data from official public health sources is retrospective,” says Irene Eckstrand of the National Institutes of Health, which funds the project, called Models of Infectious Disease Agent Study (MIDAS). “Incorporating real-time, anonymized data from social media and other Web sources into disease modeling tools may be helpful, but it also presents challenges.”
To help evaluate the Web’s potential for improving infectious disease forecasting efforts, MIDAS researcher Sara Del Valle of Los Alamos National Laboratory conducted proof-of-concept experiments involving data that Wikipedia releases hourly to any interested party. Del Valle’s research group built models based on the page view histories of disease-related Wikipedia pages in seven languages. The scientists tested the new models against their other models, which rely on official health data reported from countries using those languages. By comparing the outcomes of the different modeling approaches, the Los Alamos team concluded that the Wikipedia-based modeling results for flu and dengue fever performed better than those for other diseases. Continue reading “Forecasting Infectious Disease Spread with Web Data”
Forecasts give us a range of possible outcomes. In addition to being a useful public health tool to prepare for an outbreak, they’re an important research tool to test assumptions about how a disease may spread. When we compare the predicted and actual outcomes, we can confirm assumptions, such as the groups of individuals who are more likely to spread the infection to others. Continually doing this helps refine the models and ensure that their forecasts are as accurate as possible.
What are some of the challenges the modelers face?
Ebola virus particles (green) attached to and budding from a cell (blue). Credit: National Institute of Allergy and Infectious Diseases.
We need data to build and test models. The data available from this outbreak have been more limited than in most previous outbreaks of Ebola simply because the public health systems are overwhelmed with sick people, and recording information is a secondary priority.
Another issue with forecasting future trends is incorporating information about the deployment of resources and the implementation of interventions that actually slowed the outbreak. We also need to incorporate changes in people’s behavior. If people think an outbreak is leveling off, they may relax the precautions they’ve been taking—and that could lead to another spike in the disease.
What other Ebola-related projects are the MIDAS modelers working on?
The MIDAS researchers are:
Modeling logistical factors such as the number and placement of treatment beds and staffing needs.
Tracking potential transmission within and between communities and at hospitals and funerals.
Developing a method to estimate the amount of underreporting of case data.
Applying models of “tipping points” to look for evidence that the disease curve is slowing.
Estimating the number of people who are infected but not symptomatic.
Creating new resources for Ebola modelers, including standards for using infectious disease data.
Calculating the risk of importation of cases for a wide variety of countries based on travel networks.
How are the modelers working together?
The MIDAS modelers conference call 1-2 times a week to discuss results, modeling strategies, data sources and questions amenable to modeling. They also participate in discussions with government and other academic groups, so there’s a sizable number of modelers working on a wide variety of public health, logistical and basic research questions.
A new study finds that people with lingering sepsis may have suppressed immune systems. Credit: Stock image.
Each year, more than 200,000 people in the United States die from sepsis, a condition caused by an overwhelming immune response that can quickly lead to organ failure. While many people with sepsis survive this immediate threat, they may die days or even months later from secondary infections.
A research team that included Richard Hotchkiss, Jonathan Green and Gregory Storch of Washington University School of Medicine in St. Louis suspected that when sepsis lasts for more than a few days, it compromises the immune system. To test this hypothesis, the scientists compared viral activity in sepsis patients, other critically ill patients and healthy individuals. They looked for viruses like Epstein-Barr and herpes-simplex that are often dormant and innocuous in healthy people but can reactivate and cause problems in those with suppressed immune systems.
Of the three study groups, sepsis patients had much higher levels of these viruses, suggesting that their immune responses may be hindered. Immune suppression could make it difficult to defend against the reactivated viruses as well as new infections like pneumonia. The team now plans to test whether immune-boosting drugs can prevent deaths in people with lingering sepsis.
A knot-like structure in RNA enables flaviviruses to cause diseases like yellow fever, West Nile virus and dengue fever, which threaten roughly half the world’s population. Credit: Jeffrey Kieft.
Roughly half the world’s population is now at risk for mosquito-borne diseases other than malaria, such as yellow fever, West Nile virus and dengue fever. These three diseases are caused by flaviviruses, a type of virus that carries its genetic material as a single strand of RNA.
Flaviviruses have found a way not only to thwart our bodies’ normal defenses, but also to harness a human enzyme—paradoxically, one normally used to destroy RNA—to enhance their disease-causing abilities. A team of scientists led by Jeffrey Kieft at the University of Colorado at Denver found that flaviviruses accomplish both feats by bending and twisting a small part of their RNA into a knot-like structure.
The scientists set out to learn more about this unusual ability. First, they determined the detailed, three-dimensional architecture of the convoluted flaviviral RNA. Then, they examined several different variations of the RNA. In doing so, they pinpointed parts that are critical for forming the knot-like shape. If researchers can find a way to prevent the RNA from completing its potentially dangerous twist, they’ll be a step closer to developing a treatment for flaviviral diseases, which affect more than 100 million people worldwide.
This work also was supported by the National Institute of Allergy and Infectious Diseases and the National Cancer Institute.
Jeff Shaman Field: Climatology Works at: Columbia University’s Mailman School of Public Health, N.Y. Favorite high school subject: Biology First job: Guide at the Franklin Institute in Philadelphia, Pa. Alternative career: Opera singer Credit: Anne Foulke
Before he wrote any scientific papers, Jeff Shaman wrote operas. At the premiere of one of his operas, an 80-minute story about psychoanalysis, reviewers said the work “crackle[d] with invention.”
After 4 years of training to become an opera singer, Shaman realized that the work wouldn’t offer him career stability. He started thinking about his other interests. After college, where he majored in biology with a focus on ecology, he had volunteered to help with HIV clinical trials and developed a fascination with understanding infectious diseases. He wondered if the quantitative tools and methods used to study the physical sciences—another interest area—could inform how contagions spread and possibly even lead to systems for monitoring or predicting their transmission.
So Shaman returned to school—this time, for advanced degrees in earth and environmental sciences. He now studies the relationship between soil wetness and mosquito-borne diseases such as malaria in Africa and West Nile in Florida.
“I love science—probing questions, thinking about problems, finding solutions, pursuing my ideas,” says Shaman.
His Findings
A few years ago, Shaman took some of his scientific compositions in another direction by focusing on seasonal flu outbreaks. For more than 60 years, researchers have linked seasonal flu outbreaks with environmental data like humidity and temperature. Shaman analyzed this work and figured out that absolute humidity, rather than relative humidity, was the best predictor of outbreaks. Now he’s applied state-of-the-art mathematical modeling and real-time observational estimates of influenza incidence to predict when outbreaks will likely occur.
His forecasting technique mimics that used by meteorologists to predict weather conditions like temperatures, precipitation and even hurricane landfall. Shaman’s version incorporates variables like how transmissible a virus is, the number of days people are contagious and sick, and how much humidity is in the air.
The flu forecasts build on a series of studies in which Shaman and his colleagues used data from previous influenza seasons to test their predictions and improve reliability of their model. The work culminated with real-time predictions for 108 cities during the 2012-2013 influenza season. The forecasts could reliably estimate the peaks of flu outbreaks up to 9 weeks before they occurred.
For the 2013-2014 flu season, the researchers continued to make weekly predictions. But instead of first publishing the results in a scientific journal, they posted them on a newly launched influenza forecasts Web site where the public could view the projections.
“People understand the limitations and capabilities of weather forecasts,” says Shaman. “Our hope is that people will develop a similar familiarity with the flu forecasts and use that information to make sensible decisions.” For instance, the prediction of high influenza activity may motivate them to get vaccinated and practice other flu-prevention measures.
As he waits for the start of the next flu season, Shaman continues to tweak his forecast system to improve its reliability. He’s also beginning to address other questions, such as how to predict multiple outbreaks of different influenza strains and how to predict the spread of other respiratory illnesses.
A 5-year, randomized clinical trial helped resolve a long-standing debate about how best to manage sepsis patients.
For years, doctors have debated the best ways to identify, predict and treat sepsis. The condition, which is usually triggered by infection, is marked by body-wide inflammation and can lead to a dangerous drop in blood pressure known as septic shock. Sepsis affects more than 800,000 people each year and kills about 20 to 30 percent of them. It’s the most expensive condition treated in U.S. hospitals, costing more than $20 billion a year.
Now, a nationwide, 5-year clinical trial that set out to compare three different treatment approaches has shown that survival of patients with septic shock was the same regardless of whether they received treatment based on structured, standardized medical plans (protocols) or the usual high-level standard of care. If patients were diagnosed shortly after the onset of sepsis and treated promptly with fluids and antibiotics, they did equally well whether they received treatment based on either of two specific protocols—one less invasive than the other—or got the usual, high-level care provided by the academic hospitals where the study was conducted.
According to the study’s leaders, the trial “helps resolve a long-standing clinical debate about how best to manage sepsis patients, particularly during the critical first few hours of treatment,” and shows that “there is not a mandated need for more invasive care in all patients.”
Viral RNA (red) in an RSV-infected cell. Credit: Eric Alonas and Philip Santangelo, Georgia Institute of Technology and Emory University.
What looks like a colorful pattern produced as light enters a kaleidoscope is an image of a cell infected with respiratory syncytial virus (RSV) illuminated by a new imaging technology. Although relatively harmless in most children, RSV can lead to bronchitis and pneumonia in others. Philip Santangelo of the Georgia Institute of Technology and Emory University, along with colleagues nationwide, used multiply-labeled tetravalent RNA imaging probes (MTRIPS) to observe the entry, assembly and replication of RSV inside a living cell. Once introduced into RSV-plagued cells, the MTRIPS latched onto the viral RNA (in the image, red) without altering the level of infectivity. This led to fluorescent RSV viral particles that let the researchers track the viral RNA in host cells and better understand what the virus was doing. The knowledge gained from this new technique might aid in the development of RSV antiviral drugs and possibly a vaccine. Scientists could also one day use the imaging approach to study other RNA viruses, such as the flu and Ebola.
Incidence of influenza during the week starting 12/29/2013 (top); influenza incidence forecasts for selected cities (bottom). Credit: Columbia Prediction of Infectious Diseases.
News articles this weekend reported an uptick in flu cases in many parts of the country. When will your area be hardest hit? Infectious disease experts at Columbia University have launched an influenza forecast Web site that gives weekly predictions for rates of flu infection in 94 U.S. cities. The predictions indicate the number of cases in Chicago; Atlanta; Washington, D.C.; and Los Angeles will peak this week, with New York City, Boston, Miami and Providence peaking in following weeks. The forecasts are updated every Friday afternoon, so check back then for any changes.
The forecasting approach, which adapts techniques used in modern weather prediction, relies on real-time observational data of people with influenza-like illness, including those who actually tested positive for flu. The researchers have spent the last couple of years developing the forecasting system and testing it—first retrospectively predicting flu cases from 2003-2008 in New York City and then in real time during the 2012-2013 influenza season in 108 cities.
“People have become acclimated to understanding the capabilities and limitations of weather forecasts,” said Jeffrey Shaman, who’s led the flu forecasting project. “Making our forecasts available on the Web site will help people develop a similar familiarity and comfort.” Shaman and his team are hoping that, just as rainy forecasts prompt more people to carry umbrellas, an outlook for high influenza activity may motivate them to get vaccinated and practice other flu-prevention measures.
This work also was funded by NIH’s National Institute of Environmental Health Sciences.
Influenza virus proteins in the act of self-replication. Credit: Wilson, Carragher and Potter labs, Scripps Research Institute.
Flu viruses evolve rapidly, often staying one step ahead of efforts to vaccinate against infections or treat them with antiviral drugs. Work led by Jesse Bloom of the Fred Hutchinson Cancer Research Center has uncovered a surprising new flu mutation that allows influenza to infect cells in a novel way. Normally, a protein called hemagglutinin lets flu viruses attach to cells, and a protein called neuraminidase lets newly formed viruses escape from infected cells. Bloom’s lab has characterized a mutant flu virus where neuraminidase can enable the virus to attach to host cells even when hemagglutinin’s binding is blocked. Although the researchers generated the neuraminidase mutant studied in these experiments in their lab, the same mutation occurs naturally in strains from several recent flu outbreaks. There’s a possibility that flu viruses with such mutations may be able to escape antibodies that block the binding of hemagglutinin.
This work also was funded by NIH’s National Institute of Allergy and Infectious Diseases.
and a 
