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 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)."
UMass Amherst, International Research Team Improve Immunization Strategies for Dengue Fever in Thailand
Vaccine Work Continues at Pitt Public Health
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.
Public Health Data Rescue in the Mekong
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
In 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
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
On 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.