Michael M. Wagner, MD, PhD
Dr. Wagner’s research focuses on real-time methods for detecting and characterizing disease outbreaks, including the development and testing of operational biosurveillance systems. In his role as director of the RODS Laboratory, Dr. Wagner led the development and implementation of two widely used biosurveillance systems: the RODS system and the National Retail Data Monitor (NRDM).
Biosurveillance is a systematic process that detects and characterizes disease outbreaks. The process involves data collection, analysis, and decision making. The purpose of biosurveillance is to determine whether an outbreak exists, and if so, to identify the biological agent, source, route of transmission, geographic extent, and other characteristics that influence decisions about antibiotics, vaccines, quarantine, and other responses.
At present, Dr. Wagner is developing a third system called BioEcon. BioEcon is a decision analytic tool for use by analysts working in health departments. BioEcon is a logical extension of Dr. Wagner’s research in biosurveillance. BioEcon addresses the problem of what is the optimal action to take in response to incoming biosurveillance data. The success of Dr. Wagner’s research into methods to collect and analyze biosurveillance data have now produced a situation in which health departments have an abundance of biosurveillance data and face decisions about how to react to anomalies in those data.
Tajgardoon M, Wagner MM, Visweswaran S, Zimmerman RK. A Novel Representation of Vaccine Efficacy Trial Datasets for Use in Computer Simulation of Vaccination Policy. AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:389-398. eCollection 2018. PubMed PMID: 29888097. PMCID: PMC5961808.
Millett NE, Aronis JM, Wagner MM, Tsui F, Ye Y, Ferrar JP, Huag PJ, Gesteland PH, Cooper GF. The design and evaluation of a Bayesian System for detecting and characterizing outbreaks of influenza. Online Journal of Public Health Informatics, 11(2) (2019). Doi: https://doi.org/10.1101/435727.
Ferraro JP, Ye Y, Gesteland PH, Haug PJ, Tsui F-C, Cooper GF, Van Bree R, Ginter T, Nowalk AJ, Wagner MM. The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance. Appl Clin Inform. 2017 May 31;8(2):560-580. doi:10.4338/ACI-2016-12-RA-0211. PubMed PMID: 28561130.
Ye Y, Wagner MM, Cooper GF, Ferraro JP, Su H, Gesteland PH, Haug PJ, Millett NE, Aronis JM, Nowalk AJ, Ruiz VM, López Pineda A, Shi L, Van Bree R, Ginter T, Tsui F. A study of the transferability of influenza case detection systems between two large healthcare systems. PLoS One. 2017 Apr 5;12(4):e0174970. doi: 10.1371/journal.pone.0174970. eCollection 2017. PMID: 28380048. PMCID: PMC5381795.
Tsui F, Ye Y, Ruiz V, Cooper GF, Wagner MM. Automated influenza case detection for public health surveillance and clinical diagnosis using dynamic influenza prevalence method. J Public Health (Oxf). 2017 Oct 20:1-8. doi: 10.1093/pubmed/fdx141. [Epub ahead of print]. PMID: 29059331.
Hogan WR, Wagner MM, Brochhausen M, Levander J, Brown ST, Millett N, DePasse J, Hanna J. The Apollo Structured Vocabulary: an OWL2 ontology of phenomena in infectious disease epidemiology and population biology for use in epidemic simluation. J Biomed Semantics. 2016 Aug 18;7:50. doi: 10.1186/s13326-016-0092-y. PMID: 27538448. PMCID: PMC4989460.
Cooper GF, Villamarin R, Rich Tsui FC, Millett N, Espino JU, Wagner MM. A method for detecting and characterizing outbreaks of infectious disease from clinical reports. J Biomed Inform. 2015 Feb; 53:15-26. doi: 10.1016/j.jbi.2014.08.011. Epub 2014 Aug 30. PMID: 25181466. PMCID: PMC4441330.
López Pineda A, Ye Y, Visweswaran S, Cooper GF, Wagner MM, Tsui FR. Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports. J Biomed Inform. 2015 Dec;58:60-69. doi: 10.1016/j.jbi.2015.08.019. Epub 2015 Sep 16. PMID: 26385375. PMCID: PMC4684714.
Ye Y, Tsui FR, Wagner M, Espino JU, Li Q. Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers. J Am Med Inform Assoc. 2014 Sep-Oct; 21(5):815-23. doi: 10.1136/amiajnl-2013-001934. Epub 2014 Jan 9. PMID: 24406261. PMCID: PMC4147621.
Wagner MM, Levander JD, Brown S, Hogan WR, Millett N, Hanna J. Apollo: Giving application developers a single point of access to public health models using structured vocabularies and Web services. AMIA Annu Symp Proc. 2013 Nov 16;2013: 1415-24. eCollection 2013. PubMed PMID: 24551417. PMCID: PMC3900155.
Lee BY, Tai JH, McGlone SM, Bailey RR, Wateska AR, Zimmer SM, Zimmerman RK, Wagner MM. The potential economic value of a 'universal' (multi-year) influenza vaccine. Influenza and other respiratory viruses. 2012 May;6(3):167-75. doi: 10.1111/j.1750-2659.2011.00288.x. Epub 2011 Sep 21. PMID: 21933357. PMCID: PMC3253949.
Lee BY, Stalter RM, Bacon KM, Tai JH, Bailey RR, Zimmer SM, Wagner MM. Cost-effectiveness of adjuvanted versus nonadjuvanted influenza vaccine in adult hemodialysis patients. Am J Kidney Dis. 2011 May;57(5):724-32. doi: 10.1053/j.ajkd.2010.12.016. Epub 2011 Mar 10. PMID: 21396760. PMCID: PMC3085888.
Stebbins S, Cummings DA, Stark JH, Vukotich C, Mitruka K, Thompson W, Rinaldo C, Roth L, Wagner M, Wisniewski SR, Dato V, Eng H, Burke DS. Reduction in the incidence of Influenza A but not Influenza B associated with use of hand sanitizer and cough hygiene in schools: A randomized controlled trial. Pediatr Infect Dis J. 2011 Nov;30(11):921-6. doi: 10.1097/INF.0b013e3182218656. PubMed PMID: 21691245. PMCID: PMC3470868.
Wu TS, Shih FY, Yen MY, Wu JS, Lu SW, Chang KC, Hsiung C, Chou JH, Chu YT, Chang H, Chiu CH, Tsui FC, Wagner MM, Su IJ, King CC. Establishing a nationwide emergency department-based syndromic surveillance system for better public health responses in Taiwan. BMC Public Health. 2008 Jan 18;8:18. doi: 10.1186/1471-2458-8-18. PMID: 18201388. PMCID:PMC2249581.
Tsai M-C, Tsui F-C, Wagner MM. An Evaluation of Biosurveillance Grid—Dynamic Algorithm Distribution Across Multiple Computer Nodes. AMIA Annu Symp Proc. 2007:746-750. PMID: 18693936. PMCID:PMC2655926.
Shaffer L, Funk J, Rajala-Schultz P, Wallstrom G, Wagner MM, Saville W. Early Outbreak Detection using an Automated Data Feed of Test Orders from a Veterinary Diagnostic Laboratory. Lecture Notes in Computer Science: Intelligence and Security Informatics: Biosurveillance, 2007; 4506: 1-10. ISSN 0302-9743 (Print); 1611-3349 (Online).
Wagner MM, Robinson JM, Tsui FC, Espino JU, Hogan WR. Design of a national retail data monitor for public health surveillance. J Am Med Inform Assoc. 2003 Sep-Oct; 10(5):409-18. Epub 2003 Jun 4. PMID: 12807802. PMCID: PMC212777.
Gesteland PH, Gardner RM, Tsui FC, Espino JU, Rolfs RT, James BC, Chapman WW, Moore AW, Wagner MM. Automated syndromic surveillance for the 2002 Winter Olympics. J Amer Med Inform Assoc. 2003, 10(6): 547-54. PMID: 12925547. PMCID: PMC264432.
Current Research Projects and Collaborations
University of Pittsburgh Center for Advanced Study of Informatics in Public Health (CASIPH) 09/01/09-08/31/14
The purpose of this project is to establish a center at the University of Pittsburgh that brings together a diverse group of investigators to develop, validate, and translate methods for early detection and characterization of disease outbreaks; the project will emphasize the translation and dissemination of advanced surveillance technology for use by state and local public health agencies.
Decision Making in Biosurveillance 09/30/08-09/29/13
The major objective of this research is to advance the use of decision analysis in biosurveillance.
PA Biosurveillance 01/01/08-06/30/13
The purpose of the project is to maintain and expand the Commonwealth of Pennsylvania’s public health surveillance system
Biosurveillance Algorithms and Decision Environments 12/14/09-12/19/10
The objective of this project is to investigate and develop applications that explore biosurveillance informatics that focus on disease mobility models.
CASIPH project- the Trisano-CASIPH project collaboration
Trisano is the open source NEDSS solution that Dr. Espino’s team is extending for use by Allegheny County Department of Health and Tarrant County Health.
To improve the operational efficiency of Allegheny County Health Department, Drs Espino Voorhees Wagner and Dey have been designing a solution to the problem of managing laboratory results sent by Quest, which services doctors offices in the county (and state). ACHD currently receives paper reports from this company and the work process to process these data, and enter the data into PA NEDSS is manual and time consuming. The design process has included discussions with Philadelphia DOH and Quest.
Probabilistic Disease Surveillance 08/01/13-07/31/2016
The major goals of this project are to advance the development and integration of the components of a probabilistic disease surveillance system, including the ability to detect and characterize concurrent outbreaks and outbreaks of unknown diseases.
Apollo: Increasing Access and Use of Epidemic Models Through the Development and Adoption of a Standard Ontology 04/18/12-03/31/16
The major goals of this project are to (1) develop a standard vocabulary for the field of epidemic modeling using a tool called Protégé; (2) create two extensions to Protégé that are needed by the project; (3) develop a standard syntax using the vocabulary for representing the inputs (e.g., disease control measures) and outputs of epidemic models and to use this syntax in an existing system called the Apollo Web Services that makes it possible for other computer programs to access epidemic models; and (4) to increase the capacity to run epidemic models on supercomputers so as to demonstrate the value of the work of the first three aims.
MIDAS Informatics Services Group (ISG) 08/01/14-07/31/19
The broad goal of the project is to catalyze research in infectious disease epidemiology and to improve the related practice of disease control. The project will use the methods of service-oriented architectures and ontologies to build an informatics infrastructure that will enable MIDAS researchers to develop larger and more complex models and larger and more capable systems. The project itself will use the same methods to develop an ontology-based information management system that will index datasets, publications, existing models, and computer-interpretable information—the ‘raw materials’ of modeling. The project will also employ informatics methods from the field of knowledge representation to construct a library of computer-interpretable information that can be re-used. The re-use of information will enable the construction of potentially ecosystem-size models.