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Project Tycho™ study estimates that 100 million cases of contagious diseases have been prevented by vaccination programs in the United States since 1924

Project Tycho™In a paper published November 28, 2013, in the New England Journal of Medicine entitled "Contagious diseases in the United States from 1888 to the Present", Project Tycho™ authors describe how U.S. disease surveillance data have been used to estimate that over 100 million cases have been prevented 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 over 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 (www.tycho.pitt.edu). "Historical records are a precious yet undervalued resource. As Danish philosopher Soren Kierkegaard said, we live forward but understand backward," explained Dr. Donald 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."

 

Lack of Reliable Transportation Undermines Delivery of Lifesaving Vaccines

"Transportation of vaccines is a critical component for improving vaccination rates in low-income countries and warrants more attention, according to a computer simulation by the HERMES Logistics Modeling Team at the University of Pittsburgh and Pittsburgh Supercomputing Center (PSC). The team recently reported their findings in the PLOS ONE online journal (http://dx.plos.org/10.1371/journal.pone.0064303)."

Read the full article at the Pittsburgh Supercomputing Center website

UMass Amherst, International Research Team Improve Immunization Strategies for Dengue Fever in Thailand

Reich, NickAMHERST, Mass. – Using a unique data set spanning 40 years of dengue fever incidence in Thailand, an international team led by biostatistician Nicholas Reich at the University of Massachusetts Amherst has for the first time estimated from data that after an initial dengue infection, a person is protected from infection with other strains for between one and three years.
 
Their results have implications for designing more effective vaccine studies, say Reich and colleagues at the Johns Hopkins Bloomberg School of Public Health, the University of Michigan and the Armed Forces Research Institute of Medical Sciences (AFRIMS) in Bangkok. Findings appear in the current issue of the Journal of the Royal Society Interface.
 
Dengue fever is a mosquito-transmitted viral infection that sickens 5 percent of the world’s population each year and recently has begun to emerge in parts of the southeast United States. Building on a long-standing collaboration with Thai health officials and AFRIMS, Reich and his co-authors used the Bangkok dataset to characterize this important clinical and epidemiological feature, cross-protection. It means one’s immune response to infection with one dengue strain offers some protection against future infection with others.
 
They report the first explicit quantitative evidence that short-term cross-protection exists since human experimental infection studies performed in the 1940s and 1950s by Albert Sabin. By extending existing methods for analyzing infectious disease time-series data, they have created and applied a new framework for estimating the duration and strength of cross-protection between multiple strains of an infectious disease, Reich points out.
 
“This dataset from Bangkok is unique,” he says. “We don’t know of any other data like this in the world. AFRIMS has been collecting this data, with strain-specific information on individual cases of dengue, for over 40 years. It provided us with a unique opportunity to analyze long-term disease patterns in ways that we are not able to do with datasets of shorter duration.”
 
Epidemiologists know that many multi-strain diseases confer at least partial short-term cross-protection to people who come down with one. But cross-protection introduces “significant challenges” to researchers trying to create an accurate model of disease transmission or to evaluate vaccine effectiveness. Knowing how long cross-protection may last can help in planning well-controlled vaccine studies.
 
“Dengue is a unique and convenient disease for studying these dynamics of cross-protection,” says Reich. “Many diseases either evolve too rapidly, like influenza, or have too many circulating subtypes, like malaria, for us to gain good traction on studying cross-protection. But dengue is sort of a ‘goldilocks’ virus in this way. It has four circulating strains, which means that it’s in sort of a genetic diversity ‘sweet spot.’ There are not too many strains and not too few, which provides us with fertile ground for such study.”
 
Reich and colleagues’ database consists of monthly, lab-confirmed dengue fever case counts for each strain for 38 years, from 1973 to 2010, at Queen Sirikit National Institute of Child Health (NICH) in Bangkok. Analyzing a total of 12,197 infections, they developed a new type of statistical model to help them weigh the evidence for and against the existence of cross-protection.
 
“We made head-to-head comparisons of models that included cross-protection and those that did not,” Reich explains. “We essentially let different models compete with each other to explain the data best. And each model makes different hypotheses about how dengue works once it infects you. Consistently, when we look at models that are saying ‘cross-protection exists,’ they fit the data better than models that are saying ‘cross-protection does not exist.’ Also, these models are able to explain more of the variability that we see in the irregular size of annual epidemics in Thailand.”
 
The team’s statistical model tested a wide range of possible durations of cross-protection, using a statistical technique called maximum likelihood to find the single duration and the range of durations that best explain the patterns observed in the data.
 
Other members of the research team are Sourya Shrestha, Aaron King and Pejman Rohani of the University of Michigan, Justin Lessler and Derek Cummings of Johns Hopkins, Siripen Kalayanarooj of Queen Sirikit NICH, In-Kyu Yoon and Robert Gibbons of AFRIMS and Donald Burke of the University of Pittsburgh.
 
The work was funded by the National Institute of General Medical Sciences at the National Institutes of Health and the Bill and Melinda Gates Foundation Vaccine Modeling Initiative.
 
Contact: Janet Lathrop
Contact Phone: 413/545-0444

Vaccine Work Continues at Pitt Public Health

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Peter Salk (standing, at left) recently welcomed Dean Donald Burke and Director of Development Kristen de Paor to visit the historic archives and warehoused materials of his father, world-famous public health pioneer Jonas Salk.

As Pitt Public Health looks forward to the centennial of Jonas Salk’s birth in 2014, the school continues its tradition of world-class contributions to research in disease prevention. Pitt Public Health is home to the Public Health Dynamics Laboratory. Led by Director John Grefenstette and Dean Donald Burke, the effort builds life-like computer simulations of the transmission of communicable infectious diseases, such as influenza, tuberculosis, and dengue.

The Public Health Dynamics Laboratory has joined the fight for the global eradication of polio. “Polio transmission rates have been greatly reduced to only a few hundred cases per year worldwide,” explains Willem van Panhuis, assistant professor of epidemiology. “Computational modeling is increasingly necessary to support decision making on polio eradication strategies.” A research group including van Panhuis, Burke, and Grefenstette is working with the Bill and Melinda Gates Foundation and the U.S. Centers for Disease Control and Prevention to obtain new sources of data and to use computational modeling to evaluate potential eradication strategies before they are used in real-world settings.

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Public Health Data Rescue in the Mekong

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The Vaccine Modeling Initiative (VMI) has developed collaborations with countries in the Southeast Asia Mekong region including Thailand, Laos, Cambodia and Vietnam. Through a partnership with Institut of Research for Development (IRD) in France, a dengue surveillance data digitization field project has been started in Laos. Disaggregated dengue surveillance data will be digitized from district and provincial health departments in Laos. Students from the Institut for Tropical Medicine (ITM) in Laos are visiting Provincial and District Health Departments and health clinics to scan and digitize (rescue) dengue surveillance data in two pilot provinces.

On May 23, Dr. Wilbert van Panhuis, a VMI investigator, and Dr. Marc Choisy from IRD, visited the Vientiane Capital Health Office to start planning dengue surveillance data collection and digitization. The Vientiane Capital Region includes nine district health centers and six central hospitals that serve a total population of about 800,000 people. This region reports the most dengue cases annually and is of great importance to this project. Institut Francophone pour la Medicine Tropicale (IFMT) students will arrive in June to start data collection.

A team from the University of Pittsburgh, IRD and IFMT and the National Center for Laboratory and Epidemiology in Laos , recently visited Savannakhet province to assess progress on dengue surveillance data collection. During this two day visit, the provincial health department, the provincial malaria station and provincial hospital were visited to review dengue surveillance documentation. In addition, two districts and two health centers were visited.

A Laos field blog (https://www.vaccinemodeling.org/index.php/laos-field-blog) has been started to follow this digitization project that is being conducted by students in collaboration with the Laos Ministry of Health.

VMI Dengue Data and Modeling Workshop in Laos

group photoIn collaboration with the Laos Ministry of Health, the Vaccine Modeling Initiative (VMI) and the Institut of Research for Development (IRD) have started a project to digitize disaggregated dengue surveillance data from province and district health departments in Laos, starting in Savannakhet and Vientiane Municipality. These data will be used for computational modeling of dengue to inform control programs. A Laos data rescue field blog has been created and will be maintained by two students from the Institut de la Francophonie pour la Médicine Tropicale (IFMT), University of Pittsburgh VMI investigators, and IRD investigators. Click here to follow the activity of this project.

On May 21-22, 2012, dengue policy makers from Lao PDR and investigators from the VMI and the IRD, conducted a workshop focusing on how to use  dengue surveillance data for computational modeling. Attending the workshop were representatives from the National Emerging Infectious Diseases Coordination Office (NEIDCO), the National Center for Laboratory and Epidemiology (NCLE), the IFMT, the Savannakhet Provincial Health Department and the Vientiane Municipality Health Department.

Data Symposium 2012

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Blood Research Institute, Milwaukee, Wisconsin.

On March 1, 2012, the Southeast Wisconsin High Performance Cyberinfrastructure (SeWHiP) and Clinical & Translational Science Institute (CTSI) of Southeast Wisconsin sponsored a data symposium to explore the challenges associated with storage, access, visualization, sharing integration and scaling of research data.  The symposium titled “Discover Solutions to Challenges of Data Intensive Research” was held at the Blood Research Institute in Milwaukee, Wisconsin, and was the first of this kind that brought together investigators from bioinformatics, translational sciences, cyberinfrastructure, andecology.  Most discussions were on how to transport large scale data, how and how long digital research data should be preserved, and implementation of the data management plans from NSF and NIH.

Dr. Wilbert Van Panhuis, VMI Investigator from the University of Pittsburgh, attended this conference and shares some key points:

  • There are various initiatives/products to assist in the technical aspects of data sharing (transport, storage, etc.) such as Globus Online and DataOne. Principles of these initiatives could be used to advance data sharing of relatively small (but growing) datasets in public health.
  • Data sharing is facilitated by metadata standards, transfer, and processing protocols and preservation guidelines. These tools and methods need to be developed in public health to facilitate data sharing.
  • Digital data preservation is expensive and manageable decisions need to be made about how long and what data will be preserved. There are insufficient resources to preserve everything. Distinctions should be made between experimental and observational data. Experiments could be repeated (but perhaps not exactly replicated) but observational data would be lost forever. Public health surveillance data is mostly observational. NSF and NIH allow budget lines for preservation of research data and some tools are available to estimate the cost of this.
  • NIH will increasingly enforce data management plans and will incorporate track recordsof data sharing in the grant review process. It is unclear, however, what strategies should be used to deal with the data deluge, required infrastructure and training.
  • NSF faces a similar challenge - data sharing plans are required but a lack of resourcesand facilities may clog data sharing pipelines in the face of the data deluge. Funding of databases, however necessary, seems to be a challenge.
  • A very interesting example of crowdsourcing in ecology was given: eBird where bird observers contribute to species registration: http://ebird.org/content/ebird/

To view the powerpoint presentations from this Symposium, visit the SeWHiP website at http://www.sewhip.org/.


VMI Dengue Modeling in Laos

20120220 03On February 20, 2012, investigators from the Vaccine Modeling Initiative (VMI) and the Institute of Research for Development (IRD, http://www.irdlaos.org) met with partners in Lao PDR to begin field work on dengue surveillance. Representatives attended from the Laos National Emerging Infectious Diseases Coordinating Office, the Center for Malaria, Parasitology and Entomology, and the Provincial Health Departments of Vientiane Capital Region and Savannakhet province. Project partners discussed field data collection and digitization of dengue surveillance data for modeling of spatio-temporal dynamics of dengue transmission in Laos. In 2010, this country experienced the worst dengue epidemic in its history and models will be developed to improve disease control strategies. The VMI and IRD are working with the Institute Francophone for Tropical Medicine (IFMT), the Wellcome Trust unit at Mahosot Hospital, and the World Health Organization (WHO) on this project that will combine surveillance, entomological, serological and other data sources for computational modeling of dengue control in Lao PDR.

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