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Matthew Scotch

Assoc Professor
Faculty, SCOTTSDALE Campus, Mailcode 9020
Faculty Member
Faculty, SCOTTSDALE Campus, Mailcode 9020
Biography: 

Matthew Scotch is an associate professor of biomedical informatics in the College of Health Solutions and assistant director of the Biodesign Center for Environmental Health Engineering at Arizona State University. His research focuses on the theory and application of phylogeography to study the migration of zoonotic RNA viruses with a particular interest in influenza A viruses. Work in his lab includes the integration, analysis, and presentation of viral genetics for public health/animal health surveillance.

Current projects include studying approaches to advance phylogeography models and geospatial metadata in virus sequence databases for virus surveillance (funding: NIH/NIAID R01AI117011) and analysis of viruses from wastewater using bioinformatics (funding: NIH/NLM 1R01LM013129).

His lab group is also interested in the molecular epidemiology of zoonotic viruses including the amplification and sequencing of influenza A genes for studying spread among avian and human hosts.

Education: 
  • Postdoctoral Fellowship, Yale University 2006-2008
  • M.P.H. Yale University 2007
  • Ph.D. Biomedical Informatics, University of Pittsburgh 2006
  • M.A. Biomedical Informatics, Columbia University 2002
  • B. A. Health and Society, University of Rochester, NY 1998
Research Interests: 
  • Bioinformatics for Public Health (systems that leverage molecular sequence data to support decision-making at health agencies)
  • Zoonotic disease surveillance (linking health data on animal and humans)
  • Phylogeography of zoonotic RNA viruses
  • Molecular epidemiology of zoonotic RNA viruses
  • Sequencing and analysis of the Influenza A genome
  • Geographical Information Systems (GIS)
  • Natural Language Processing (NLP)

Related Links:

Publications: 

Articles indexed in PubMed.

1.    Yoo, W., et al., Patterns and Disparities in Human Papillomavirus (HPV) Vaccine Uptake for Young Female Adolescents among US States: NIS-Teen (2008-2016). Cancer Epidemiol Biomarkers Prev, 2020.
2.    Stelzer-Braid, S., et al., Next generation sequencing of human enterovirus strains from an outbreak of enterovirus A71 shows applicability to outbreak investigations. J Clin Virol, 2020. 122: p. 104216.
3.    Scotch, M., et al., Incorporating sampling uncertainty in the geospatial assignment of taxa for virus phylogeography. Virus Evol, 2019. 5(1): p. vey043.
4.    Magge, A., et al., Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature. Pac Symp Biocomput, 2019. 24: p. 100-111.
5.    Beard, R. and M. Scotch, Identifying current and emerging resources and tools utilized for detection, prediction, and visualization of viral zoonotic disease clusters: a Delphi study. JAMIA Open, 2019. 2(3): p. 306-311.
6.    Adam, D.C., M. Scotch, and C.R. MacIntyre, Phylodynamics of Influenza A/H1N1pdm09 in India Reveals Circulation Patterns and Increased Selection for Clade 6b Residues and Other High Mortality Mutants. Viruses, 2019. 11(9).
7.    Tahsin, T., et al., GeoBoost: accelerating research involving the geospatial metadata of virus GenBank records. Bioinformatics, 2018. 34(9): p. 1606-1608.
8.    Raina MacIntyre, C., et al., Converging and emerging threats to health security. Environ Syst Decis, 2018. 38(2): p. 198-207.
9.    Njoto, E.N., et al., Phylogeography of H5N1 avian influenza virus in Indonesia. Transbound Emerg Dis, 2018. 65(5): p. 1339-1347.
10.    Morin, C.W., et al., Avian influenza virus ecology and evolution through a climatic lens. Environ Int, 2018. 119: p. 241-249.
11.    Magge, A., et al., Deep neural networks and distant supervision for geographic location mention extraction. Bioinformatics, 2018. 34(13): p. i565-i573.
12.    Magee, H.Y., et al., U.S. nationwide reconnaissance of ten infrequently monitored antibiotics in municipal biosolids. Sci Total Environ, 2018. 643: p. 460-467.
13.    Magee, D., J.E. Taylor, and M. Scotch, The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models. Sci Rep, 2018. 8(1): p. 5905.
14.    Magee, D. and M. Scotch, The effects of random taxa sampling schemes in Bayesian virus phylogeography. Infect Genet Evol, 2018. 64: p. 225-230.
15.    Bui, C.M., et al., Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis. Emerg Microbes Infect, 2018. 7(1): p. 184.
16.    Beard, R., E. Wentz, and M. Scotch, A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks. Int J Health Geogr, 2018. 17(1): p. 38.
17.    Adam, D.C., M. Scotch, and C.R. MacIntyre, Bayesian Phylogeography and Pathogenic Characterization of Smallpox Based on HA, ATI, and CrmB Genes. Mol Biol Evol, 2018. 35(11): p. 2607-2617.
18.    Weissenbacher, D., et al., Extracting geographic locations from the literature for virus phylogeography using supervised and distant supervision methods. AMIA Jt Summits Transl Sci Proc, 2017. 2017: p. 114-122.
19.    Tahsin, T., et al., Named entity linking of geospatial and host metadata in GenBank for advancing biomedical research. Database (Oxford), 2017. 2017.
20.    Namilae, S., et al., Multiscale model for pedestrian and infection dynamics during air travel. Phys Rev E, 2017. 95(5-1): p. 052320.
21.    Magee, D., M.A. Suchard, and M. Scotch, Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction. PLoS Comput Biol, 2017. 13(2): p. e1005389.
22.    Adam, D.C., et al., Does influenza pandemic preparedness and mitigation require gain-of-function research? Influenza Other Respir Viruses, 2017. 11(4): p. 306-310.
23.    Tahsin, T., et al., A high-precision rule-based extraction system for expanding geospatial metadata in GenBank records. J Am Med Inform Assoc, 2016. 23(5): p. 934-41.
24.    Sarker, A., et al., Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter. Drug Saf, 2016. 39(3): p. 231-40.
25.    Weissenbacher, D., et al., Knowledge-driven geospatial location resolution for phylogeographic models of virus migration. Bioinformatics, 2015. 31(12): p. i348-56.
26.    Veljkovic, V., et al., Evolution of 2014/15 H3N2 Influenza Viruses Circulating in US: Consequences for Vaccine Effectiveness and Possible New Pandemic. Front Microbiol, 2015. 6: p. 1456.
27.    Veljkovic, V., et al., In silico analysis suggests interaction between Ebola virus and the extracellular matrix. Front Microbiol, 2015. 6: p. 135.
28.    Scotch, M., M.A. Suchard, and P.M. Rabinowitz, Analysis of Viral Genetics for Estimating Diffusion of Influenza A H6N1. AMIA Jt Summits Transl Sci Proc, 2015. 2015: p. 36-40.
29.    Magee, D. and M. Scotch, Conceptualizing a Novel Quasi-Continuous Bayesian Phylogeographic Framework for Spatiotemporal Hypothesis Testing. AMIA Jt Summits Transl Sci Proc, 2015. 2015: p. 212-6.
30.    Magee, D., et al., Combining phylogeography and spatial epidemiology to uncover predictors of H5N1 influenza A virus diffusion. Arch Virol, 2015. 160(1): p. 215-24.
31.    Magee, D., R. Beard, and M. Scotch, Analyses of Merging Clinical and Viral Genetic Data for Influenza Surveillance. AMIA Annu Symp Proc, 2015. 2015: p. 1995-2004.
32.    Tahsin, T., et al., Natural language processing methods for enhancing geographic metadata for phylogeography of zoonotic viruses. AMIA Jt Summits Transl Sci Proc, 2014. 2014: p. 102-11.
33.    Scotch, M., et al., Diffusion of influenza viruses among migratory birds with a focus on the Southwest United States. Infect Genet Evol, 2014. 26: p. 185-93.
34.    Kane, M.J., et al., Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinformatics, 2014. 15: p. 276.
35.    Beard, R., et al., Generalized linear models for identifying predictors of the evolutionary diffusion of viruses. AMIA Jt Summits Transl Sci Proc, 2014. 2014: p. 23-8.
36.    Womack, J.A., et al., Use of structured and unstructured data to identify contraceptive use in women veterans. Perspect Health Inf Manag, 2013. 10: p. 1e.
37.    Scotch, M., et al., Phylogeography of influenza A H5N1 clade 2.2.1.1 in Egypt. BMC Genomics, 2013. 14: p. 871.
38.    Scotch, M. and C. Mei, Phylogeography of swine influenza H3N2 in the United States: translational public health for zoonotic disease surveillance. Infect Genet Evol, 2013. 13: p. 224-9.
39.    Scotch, M., et al., Examining the differences in format and characteristics of zoonotic virus surveillance data on state agency websites. J Med Internet Res, 2013. 15(4): p. e90.
40.    Braithwaite, R.S. and M. Scotch, Using value of information to guide evaluation of decision supports for differential diagnosis: is it time for a new look? BMC Med Inform Decis Mak, 2013. 13: p. 105.
41.    Rabinowitz, P.M., et al., Comparison of human and animal surveillance data for H5N1 influenza A in Egypt 2006-2011. PLoS One, 2012. 7(9): p. e43851.
42.    Scotch, M., et al., Enhancing phylogeography by improving geographical information from GenBank. J Biomed Inform, 2011. 44 Suppl 1: p. S44-7.
43.    Scotch, M., P. Rabinowitz, and C. Brandt, State-level zoonotic disease surveillance in the United States. Zoonoses Public Health, 2011. 58(8): p. 523-8.
44.    Scotch, M., et al., A qualitative study of state-level zoonotic disease surveillance in new England. Zoonoses Public Health, 2011. 58(2): p. 131-9.
45.    Scotch, M., et al., Human vs. animal outbreaks of the 2009 swine-origin H1N1 influenza A epidemic. Ecohealth, 2011. 8(3): p. 376-80.
46.    Garla, V., et al., The Yale cTAKES extensions for document classification: architecture and application. J Am Med Inform Assoc, 2011. 18(5): p. 614-20.
47.    Womack, J.A., et al., A comparison of two approaches to text processing: facilitating chart reviews of radiology reports in electronic medical records. Perspect Health Inf Manag, 2010. 7: p. 1a.
48.    Scotch, M., et al., At the intersection of public-health informatics and bioinformatics: using advanced Web technologies for phylogeography. Epidemiology, 2010. 21(6): p. 764-8.
49.    Scotch, M., et al., Use of statistical analysis in the biomedical informatics literature. J Am Med Inform Assoc, 2010. 17(1): p. 3-5.
50.    Rabinowitz, P.M., M.L. Scotch, and L.A. Conti, Animals as sentinels: using comparative medicine to move beyond the laboratory. Ilar j, 2010. 51(3): p. 262-7.
51.    Ohl, M., et al., Rural residence is associated with delayed care entry and increased mortality among veterans with human immunodeficiency virus infection. Med Care, 2010. 48(12): p. 1064-70.
52.    Konovalov, S., et al., Biomedical informatics techniques for processing and analyzing web blogs of military service members. J Med Internet Res, 2010. 12(4): p. e45.
53.    Scotch, M., L. Odofin, and P. Rabinowitz, Linkages between animal and human health sentinel data. BMC Vet Res, 2009. 5: p. 15.
54.    Rabinowitz, P., M. Scotch, and L. Conti, Human and animal sentinels for shared health risks. Vet Ital, 2009. 45(1): p. 23-4.
55.    Liu, A., et al., Risk factors for human infection with West Nile Virus in Connecticut: a multi-year analysis. Int J Health Geogr, 2009. 8: p. 67.
56.    Scotch, M., K.Y. Yip, and K.H. Cheung, Development of grid-like applications for public health using Web 2.0 mashup techniques. J Am Med Inform Assoc, 2008. 15(6): p. 783-6.
57.    Scotch, M., B. Parmanto, and V. Monaco, Evaluation of SOVAT: an OLAP-GIS decision support system for community health assessment data analysis. BMC Med Inform Decis Mak, 2008. 8: p. 22.
58.    Parmanto, B., et al., Spatial and multidimensional visualization of Indonesia's village health statistics. Int J Health Geogr, 2008. 7: p. 30.
59.    Cheung, K.H., et al., HCLS 2.0/3.0: health care and life sciences data mashup using Web 2.0/3.0. J Biomed Inform, 2008. 41(5): p. 694-705.
60.    Boulos, M.N., et al., Web GIS in practice VI: a demo playlist of geo-mashups for public health neogeographers. Int J Health Geogr, 2008. 7: p. 38.
61.    Scotch, M., B. Parmanto, and V. Monaco, Usability Evaluation of the Spatial OLAP Visualization and Analysis Tool (SOVAT). J Usability Stud, 2007. 2(2): p. 76-95.
62.    Scotch, M., et al., Exploring the role of GIS during community health assessment problem solving: experiences of public health professionals. Int J Health Geogr, 2006. 5: p. 39.
63.    Scotch, M. and B. Parmanto, Development of SOVAT: a numerical-spatial decision support system for community health assessment research. Int J Med Inform, 2006. 75(10-11): p. 771-84.
64.    Parmanto, B., M. Scotch, and S. Ahmad, A framework for designing a healthcare outcome data warehouse. Perspect Health Inf Manag, 2005. 2: p. 3.
65.    Stetson, P.D., et al., The sublanguage of cross-coverage. Proc AMIA Symp, 2002: p. 742-6.

Research Activity: 

ACTIVE RESEARCH SUPPORT

COVID-19 Seed Grant    Scotch (PI)    06/01/2020-05/31/2021
ASU/CHS
Next-generation sequencing and genomic epidemiology of SARS-CoV-2 patients in Arizona

The goal of this seed grant is to use next-generation sequencing on processed clinical specimens of COVID-19 positive patients in Arizona seen at a Abrazo Health Network hospital and to link subsequent genetic sequence analysis to clinical phenotypes including disease severity.

CNS 2027529    Scotch (PI)    05/15/2020-04/30/2021
NSF    
Collaborative:RAPID: Leveraging new data sources to analyze the risk of COVID-19 in crowded locations

The goal of this RAPID project is to utilize new data sources such as location-based services data and videos of pedestrian movement to develop local and global models of COVID-19 risk.
Role: Co-PI

CBET 2028564    Halden    05/01/2020-04/30/2021
NSF    
Collaborative:RAPID: COVID-19’s impact on the urban environment, behavior, and wellbeing

The goal of this RAPID project is to leverage novel data sources from community wastewater including the concentrations of 130+ wastewater-borne biomarkers of environmental stress and human wellbeing and report the resultant data to city stakeholders via an online dashboard for public health decision-making during COVID-19.
Role: Co-PI

OAC 1931560    Scotch (PI)    11/01/2019–10/31/2022
NSF    
Collaborative:Elements:Cyberinfrastructure for pedestrian dynamics-based analysis of infection propagation through air travel

The goal of this project is to develop a novel software to simulate the movement of people in order to understand how movement patterns influence transmission of infection at local as well as global scales.

1R01LM013129-01    Scotch, Halden, Varsani (PIs)    06/03/2019-05/31/2023
NIH/NLM    
Bioinformatics framework for wastewater-based surveillance of infectious diseases
The goal of this project is to develop and evaluate a bioinformatics framework that uses metagenomic data generated from sampling of wastewater to monitor local epidemics and outbreaks of infectious diseases.

5R01AI117011-04    Scotch, Gonzalez-Hernandez (PIs)    04/01/2016–03/31/2021
NIH/NIAID    
Tracking evolution and spread of viral genomes by geospatial observation error
The goal of this project is to enhance the geospatial data used for the phylogeography of zoonotic viruses by applying natural language processing techniques to biomedical text and statistical phylogeography to viral genetic data.

Summer 2020
Course NumberCourse Title
BMI 482Capstone I
BMI 483Capstone II
BMI 484Internship
BMI 560Teachng Biomedical Informatics
BMI 584Internship
BMI 792Research
BMI 799Dissertation
Spring 2020
Course NumberCourse Title
BMI 482Capstone I
BMI 483Capstone II
BMI 484Internship
CHS 493Honors Thesis
MBB 495Undergraduate Research
BMI 560Teachng Biomedical Informatics
BMI 584Internship
BMI 593Applied Project
BMI 595Continuing Registration
BMI 790Reading and Conference
CHS 791Seminar
BMI 792Research
BMI 799Dissertation
Summer 2019
Course NumberCourse Title
BMI 584Internship
BMI 792Research
Spring 2019
Course NumberCourse Title
BMI 482Capstone I
BMI 483Capstone II
BMI 484Internship
BMI 493Honors Thesis
MBB 495Undergraduate Research
BMI 560Teachng Biomedical Informatics
BMI 593Applied Project
BMI 790Reading and Conference
BMI 792Research
BMI 799Dissertation
Fall 2018
Course NumberCourse Title
BMI 484Internship
BMI 492Honors Directed Study
MBB 495Undergraduate Research
BMI 560Teachng Biomedical Informatics
BMI 584Internship
BMI 590Reading and Conference
BMI 593Applied Project
BMI 790Reading and Conference
BMI 792Research
BMI 799Dissertation
Summer 2018
Course NumberCourse Title
BMI 484Internship
BMI 584Internship
BMI 792Research
Summer 2017
Course NumberCourse Title
BMI 484Internship
BMI 584Internship
BMI 593Applied Project
BMI 792Research
BMI 799Dissertation
Spring 2017
Course NumberCourse Title
BMI 493Honors Thesis
BMD 502Foundations BMI Methods I
BMI 560Teachng Biomedical Informatics
BMI 570BMI Symposium
BMI 590Reading and Conference
BMI 593Applied Project
BMI 790Reading and Conference
BMI 792Research
BMI 799Dissertation
Fall 2016
Course NumberCourse Title
BMI 102Intro Public Hlth/Imagng Infor
BMI 492Honors Directed Study
MBB 495Undergraduate Research
BMI 560Teachng Biomedical Informatics
BMI 593Applied Project
BMI 792Research
BMI 799Dissertation
Summer 2016
Course NumberCourse Title
BMI 792Research
BMI 799Dissertation
Spring 2016
Course NumberCourse Title
BMD 502Foundations BMI Methods I
BMI 560Teachng Biomedical Informatics
BMI 592Research
BMI 593Applied Project
BMI 790Reading and Conference
BMI 792Research
Fall 2015
Course NumberCourse Title
BMI 102Intro Public Hlth/Imagng Infor
BMI 502Foundations BMI Methods I
BMI 560Teachng Biomedical Informatics
BMI 592Research
BMI 593Applied Project
BMI 792Research
BMI 799Dissertation
Presentations: 
  • Matthew Scotch. A pipeline for virus phylogeography that accounts for geospatial observation error. 12th Annual Rocky Mountain Bioinformatics Conference (Dec 2014).
  • Matthew Scotch and Graciela Gonzalez. Text processing and geospatial uncertainty for phylogeography of zoonotic viruses. Webinar on NIH-funded projects on spatial uncertainty, surveillance research program (Oct 2014).
  • Matthew Scotch. Challenges and promises of bioinformatics for translational applications. Lecture to first-year medical students at UA-COM (Sep 2014).
  • Matthew Scotch. Tracking the spread of viruses. Spirit of the senses salon (Sep 2014).
  • Matthew Scotch and Daniel Magee. Phylogeographic generalized linear model for identifying predictors driving H5N1 diffusion within Egypt. Intelligent Systems for Molecular Biology (ISMB) (Jul 2014).
  • Matthew Scotch. Translational public health: using viral sequence data for zoonotic disease surveillance. Epi Presents! series (Apr 2014).
  • Matthew Scotch. Phylogeography of avian and human influenza in the Southwest United States. Influenza2013: One Influenza, One World, One Health, Oxford, United Kingdom (Sep 2013).
  • Matthew Scotch. Phylogeography of avian and human influenza in the southwest United States. 10th Annual Rocky Bioinformatics Conference (Dec 2012).
  • Matthew Scotch. Phylogeography of influenza A in human and avian species in the southwest United States. Molecular Epidemiology and Evolutionary Genetics of Infectious Disease (MEEGID)11 (Nov 2012).
  • Matthew Scotch. Public health informatics to support public health decision making. Society for Medical Decision Making Annual Meeting (Oct 2012).
  • Scotch M. Integrated Human-Animal Surveillance Systems for Emerging Threats to Health. Electronic Medical Records: How They Can Improve Animal Health conference (Nov 2010).
Service: 
  • Infection Genetics Evolution, Editorial Board (2014 - Present)
  • Influenza Research Database and Virus Pathogen Resource, Member, User Advisory Group (2014 - Present)
  • Infection, Genetics, and Evolution, Editorial Board (2013 - Present)
  • Influenza Research Database and Virus Pathogen Resource, Member, User Advisory Group (2013 - Present)
  • Biomedical Informatics Academic Program Committee, Member (2014 - 2014)
  • NIH/NLM Information Resource Grants to Reduce Health Disparities (G08), ZLM1 ZH-G 01, Reviewer (2014 - 2014)
  • Biomedical Informatics Admissions Committee, Chair (2014 - 2014)
  • NIH/NLM Special Emphasis Panel, ZLM1 ZH-C 01, Reviewer (2014 - 2014)
  • Biomedical Informatics Academic Program Committee, Member (2013 - 2013)
  • Biomedical Informatics Admissions Committee, Chair (2013 - 2013)
  • Biomedical Informatics Academic Program Committee, Member (2012 - 2012)
  • Biomedical Informatics Admissions Committee, Chair (2012 - 2012)
  • Biomedical Informatics Media Team, Member (2012 - 2012)
  • NIH/NLM Information Resource Grants to Reduce Health Disparities (G08), ZLM1 ZH-G 01, Reviewer (2012 - 2012)
  • NIH/NIAID, Bioinformatics Integration Support Contract (BISC), ZAI1-QV-I (C1), Reviewer (2012 - 2012)
  • Biomedical Informatics Admissions Committee, Chair (2011 - 2011)
  • American Medical Informatics Association, Public Health Informatics Working Group, Past Chair (2011 - 2011)
  • American Medical Informatics Association, Public Health Informatics Working Group, Chair (2009 - 2010)
  • American Medical Informatics Association, Student Working Group, Executive Committee, Member-at-Large (2008 - 2010)