Faculty Profile


Lauren Charles

Lauren Charles

Senior Data Scientist, Pacific Northwest National Laboratory
Research Professor, Paul Allen School for Global Animal Health

Affiliated Organizations

  • International Society for Disease Surveillance
  • One Health Surveillance Workgroup
  • American Association of Wildlife Veterinarians
  • American Veterinary Medical Association
  • WA Veterinary Medical Association
  • National Security Directorate Innovation Q-Camp Program
  • WSU Community Health Analytics Initiative

lauren.charles@wsu.edu
Phone: (509) 372-4976

 

Biography

Dr. Lauren Charles is a Veterinarian with a Data Science PhD trained in public health, epidemiology, and zoonotic diseases as well as mathematics, biology, environmental and geographic information science, plant pathology, and bioinformatics.

Personal Statement

As a data scientist, her interests lie in identifying connections between living things and the environments they share through quantifiable data to promote public health security, environmental awareness, early warning, and understanding of infectious disease processes.

Education and Training

  • Ph.D. Fisheries, Wildlife, and Conservation Biology Program, College of Veterinary Medicine, College of Natural Resources, and College of Agricultural and Life Sciences, North Carolina State University, Raleigh, NC.
  • DVM. College of Veterinary Medicine, North Carolina State University, Raleigh, NC
  • MS. Department of Plant Pathology, College of Agricultural and Life Sciences, North Carolina State University Raleigh, NC  
  • BA. Department of Mathematics, Boston College Chestnut Hill, MA.

Other: 

  • Data Science and Data Engineering;  Data Science Dojo, Bellevue, WA
  • PNNL Science and Engineering Development Program (SEDP)

General Research / Expertise

Dr. Charles is a Veterinarian with a multidisciplinary Data Scientist PhD background with training in Mathematics, Biology, Environmental Science, Geographic Information Science, Plant Pathology, and BioInformatics. Current research combines statistics, machine learning, and deep learning methods for developing systems that support integrated, early warning for chemical and biological surveillance of humans, animals, and plants.

Research Contributions

Her past research has focused on wildlife population health, epidemiology and ecology, and the interface between wildlife, humans, domestic, and agricultural animals. Dr. Charles’ current research integrates heterogeneous data sources, such as disease reporting with social and news media, natural disasters, meteorological and climatic data, behavioral and cultural factors, into complex epidemiological models to advance current biosurveillance through a one health approach. Her most recent work at Pacific Northwest National Laboratory focuses on One Health and National Security solutions in support of non-governmental organizations, the Washington Department of Health and the U.S. Departments of Energy, Health and Human Services, Agency for International Development, Defense, and Homeland Security.

Current Funded Research

  1. Global Biodetection Architecture (DHS OHA)
  2. Automated Aggregation and Integration of Data for Biosurveillance (DHS S&T)
  3. Maritime Domain Situational Awareness for IUU Fishing (DARPA STO)
  4. Threat Surveillance – Intern Competition (DoD DTRA)

Research Details

Improving biosurveillance for human, animal, and plant health. In effort to improve biosurveillance for actionable decision making, my contribution has been to evaluate new data sources, integrate traditional and non-traditional sources, and develop new biosurveillance tools with cutting-edge analytics. Among my publications, I highlight a literature review showing promise for social media data as a “soft” data source to increase the speed of disease detection within a population. Building upon this, my colleagues and I have shown that social media data can be used to understand the health of populations. By combining social media information with other data, such as clinical diagnoses from medical reports (US Military) or disease case counts and natural disasters (Philippines), an analytic tool can identify when and where an outbreak may occur. In conjunction, I have focused research efforts on refining text analytics for biosurveillance from concept extraction to anomaly detection using various types of text-based data, e.g., news, online articles, social media, medical reports. These research efforts have founded open source global surveillance tools for human, animal, and plant health covering the biological and chemical domain.

http://www.evidenceaid.org/using-social-media-for-actionable-disease-surveillance-and-outbreak-management/

Environmental and physiological influences on animal movements and disease patterns. In my research, I investigated the disease prevalence of Cryptosporidium in an enclosed, zoological population of Sifaka with known environmental, social, and physiologic factors. Here, environmental factors (i.e., temperature and seasonality) and physiological factors (i.e., age) correlated with oocyst shedding. In effort to understand environmental and physiological influences on cross-species disease transmission, I designed a database management integration system that utilized existing collared wildlife movement data (i.e., GPS and activity monitors), integrated it with weather station, drought, and severe weather reports, and overlaid it on a geospatially defined landscape map with geospatial analysis tools. This tool was utilized to understand how weather affected deer and coyote use of lowland cover and water resources and how weather, physiologic, and landscape correlated with their intra- and interspecies interactions. Also, I used the tool to identify relationships between specific weather conditions and aggressive interactions between wild mesocarnivores and humans, domestic, and/or agricultural animals.

Identifying factors that reduce nematode parasitism. My earliest work aimed to understand nematode-plant interactions and identifying methods to naturally protect important crop species from nematode invasion. The two biological control methods that I focused my attention on were 1) identifying natural resistance genes in plants to nematode invasion, and 2) characterizing the pathogenicity of a natural pathogen of nematodes. The work on plant resistance has led to the discovery of and characterization of root-knot nematode resistance in plants, which has been utilized in focused breeding efforts to naturally overcome nematode parasitism in important crop species, such as tomato, pepper, and soybean. Outcomes from research on the bacterial pathogen of root-knot nematodes led to 1) the discovery of new methods in bioinformatics for phylogenetic analysis (e.g., the advantage of multiple housekeeping genes over single 16S rRNA sequences for bacterial characterization), 2) the identification of the nematode parasite, Pastueria penetrans, as an ancestor to Bacillus sp., and 3) the identification of collagen-like motifs that could be key factors in bacteria-nematode pathogen interaction.

Select Publications
  • Volkova S, Charles-Smith L, Harrison J, Corley CD. (2017) Uncovering the Relationships Between Military Community Health and Affects Expressed in Social Media.  EPJ Data Science 6(9). DOI 10.1140/epjds/s13688-017-0102-z PMID: PMCID:
  • Charles LE, Smith W, Rounds J, Mendoza J. (2017) Chapter: Text-based Analytics for Biosurveillance. VK, Giabbanelli PJ, Papageorgiou EI, editors.  Advanced Data Analytics in Health. New York: Springer. pp 117-131. https://link.springer.com/book/10.1007%2F978-3-319-77911-9 PMID: PMCID:
  • Charles-Smith LE, Reynolds TL, Cameron MA, Conway M, Lau EHY, Olsen JM, Palvin JA, Shigematsu M, Streichert LC, Suda KJ, Corley C. (2015) Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review. PLoS One. 10(10): e0139701. PMID: 26437454 PMCID: PMC4593536
  • Charles-Smith LE, Cowen P, Schopler B. (2010) Environmental and physiological factors contributing to outbreaks of Cryptosporidium parvum in Propithecus coquereli (Coquerel’s Sifaka) at the Duke Lemur Center: 1999-2007.  Journal of Zoo and Wildlife Medicine 41(3): 438–444. PMID: PMCID:
  • Charles L, Carbone I, Davis KG, Bird D, Burke M, Kerry BR, Opperman CH. (2005) Phylogenetic analysis of Pasteuria penetrans by use of multiple genetic loci. Journal of Bacteriology 187(16):5700-8. PMID: 16077116 PMCID: PMC1196054
  • PNNL Exceptional Contribution Program (ECP) Award
  • PNNL NSD Outstanding Performance Award
  • PNNL Runner up for Business Developer of the Year Award
  • Max MacGraw Wildlife Foundation Scholarship
  • Wildlife Management Institute Scholarship
  • Southeast Climate Science Center Global Change Fellowship
  • Pi Epsilon Award for Mathematics
  • Kernel of Kentucky Award