About

Kelly Garrett is a Professor and the Director of the School of Communication at The Ohio State University. Her research focuses on communication technology and society, health communication and social influence, mass communication uses and effects, and political communication and public opinion. She is involved in various research groups and programs, including the Communication Research Experience Program (C-REP) and the Time-sharing Experiments for the School of Communication (TESoC). Garrett has contributed to the field through her publications and scholarly work, and she maintains a website with additional information about her research, publications, and CV. She is based in Derby Hall at Ohio State University and is accessible via email for professional inquiries.

Research topics

• Ecology
• Computer Science
• Business
• Biology
• Geography
• Economics
• Biotechnology
• Risk analysis (engineering)
• Natural resource economics
• Machine Learning
• Economic growth
• Data Mining
• Artificial Intelligence
• Data science
• Botany
• Marketing
• Environmental planning
• Agroforestry
• Engineering
• Agricultural economics
• Environmental science
• Environmental resource management
• Development economics
• Medicine

Selected publications

• GIRAF 1.0: A unified global framework to anticipate plant pest invasions

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-27

  articleSenior authorCorresponding

  Plant pests threaten 10-40% of global food production, resulting in $55-220 billion in annual economic losses. Despite these escalating risks, biosecurity remains largely reactive, lacking anticipatory frameworks that integrate pest-specific drivers governing transboundary spread. We present GIRAF 1.0 (Global Invasion Risk Assessment Framework), the first quantitative, data-driven system that unifies pest-specific multi-host landscapes, abiotic suitability, and global trade networks with international phytosanitary policies. We applied GIRAF to four globally devastating pests - ranging from viral to insect taxa - to reconstruct a century of transcontinental spread and generate the first multiscale atlases of future invasion potential. GIRAF reveals that 22-37% of Earth's land surface can contain host communities that largely overlap with environmentally suitable hotspots. Over 115 countries are highly vulnerable to trade-mediated pest introductions despite adopted phytosanitary policies. GIRAF provides a foundation for proactive surveillance and pandemic preparedness, offering a scalable path for transnational biosecurity agencies and global food industries.

  PublisherDOI

• Simple network models integrate global change, social dynamics and management interventions in biosecurity scenario analysis

  NeoBiota · 2026-03-13

  articleOpen access

  Global change and public participation are both areas of considerable uncertainty in estimating the success of biosecurity response strategies, but are poorly integrated in most available scenario analysis frameworks. We introduce INApest() , a novel network simulation method which integrates social and global change factors, as well as pest biology and multiple management variables in scenario analyses of biosecurity responses. INApest() separates the management response into four key parameters: probability of detection; management adoption; eradication of local populations; spread reduction (e.g. through movement restrictions or hygiene measures). It also permits simulation of biosecurity responses which evolve organically as new incidences of the pest are detected and information about the pest and management technologies spread through the network. We demonstrate selected functionality of INApest() using Nassella neesiana (Chilean Needle Grass; CNG), a slow-spreading pasture weed that impacts animal health, as a case-study. Realistic historical CNG spread rates are reproduced under a no management scenario using dispersal kernels derived from known natural and human-mediated spread mechanisms. Scenario analyses comparing over 15,000 parameter combinations reveal that communication of invasive threat to farms neighbouring known infestations significantly reduces the farm-scale eradication probability and spread reduction required for management success (i.e. success is achieved at lower levels of farm-scale management practice efficacy). We use targeted simulation experiments to show how INApest() permits assessment of cross-border consequences of local management decisions and how communication between landowners interacts with climate change and surveillance effort to impact management success. INApest() has the potential to be used at multiple scales and to explore a wide range of management, global change and social scenarios.

  PublisherOA PDFDOI

• Pathogens on fire: a scoping review of smoke-borne pathogen ecology in the One Health framework

  PeerJ · 2026-01-22 · 1 citations

  articleOpen accessSenior author

  Background: Wildland fires are increasing in both frequency and severity in many areas globally. Smoke from wildland fires (wildfires and prescribed burns), as well as agricultural burning, releases not only pollutants but also viable microorganisms, including pathogens capable of long-distance dispersal, potentially posing unrecognized risks to human, animal, and plant health. Objectives: This scoping review synthesizes knowledge about pathogenic microbial dispersal in smoke from wildland fires, identifies gaps in pathogen ecology and epidemiology, and outlines research priorities in a One Health framework. Methods: This review followed the Arksey & O'Malley framework with PRISMA-ScR guidance, using systematic searches in PubMed, Google Scholar, and grey literature sources (USDA Forest Service, World Health Organization, U.S. Environmental Protection Agency). After screening and applying inclusion criteria, 36 studies were retained that addressed microbial transport, viability, and disease associated with wildland fire smoke. Results: , and bacteria capable of forming heat-resistant spores. If microbes can remain viable in smoke across greater distances, there would be the possibility of long-distance dispersal while suspended in smoke plumes. However, data about infection outcomes, dose-response relationships, and host susceptibility are lacking. Current wildland fire smoke surveillance focuses almost exclusively on abiotic pollutants, leaving microbial risks largely ignored. Conclusions: A One Health approach integrates fire ecology, aerobiology, microbiology, and epidemiology across host species. After determining how important the role of dispersal in smoke is for human, animal, and plant health, priority actions may include improving pathogen viability sampling, incorporating microbial monitoring into smoke surveillance networks, and developing predictive models to assess health and ecological risks.

  PublisherDOI

• Pathogen and pest communities in agroecosystems across climate gradients: Anticipating future challenges in the highland tropics

  Agricultural Systems · 2025-12-26 · 1 citations

  articleOpen accessSenior authorCorresponding

  CONTEXT Tropical agricultural systems must respond to current and future pathogen and pest communities. An important research gap is how climate change may shift the geographic distribution of tropical pathogens and pests. OBJECTIVE We evaluated the geographic risk of 27 pathogens and pests in four food security crops (banana, cassava, potato, and sweetpotato) in the Great Lakes region of Africa, and potential future risk under climate change. We analyzed model performance for each pathogen and pest, assessing the potential for changes in geographic distribution, and for decision support systems to facilitate management. METHODS Cropland connectivity analysis identified locations likely important in the spread of crop-specific pathogens and pests. We surveyed the 27 economically important pathogens and pests in Rwanda and Burundi, mapping the distribution of each across climate gradients and quantifying associations. We used machine learning to model each species as a function of environmental variables, including host landscape. We also evaluated future temperatures across altitudes under climate change scenarios. RESULTS AND CONCLUSIONS Among ten algorithms evaluated, random forests and support vector machines generally performed best for predicting severity or infestation. Host landscape variables were useful predictors for some species. Based on climate matching, 44 % of the pathogens and pests could become more common with warmer temperatures at higher altitudes, while 17 % may become less common. SIGNIFICANCE These findings indicate how crop health in the region requires adaptation to multiple sustainability challenges. The results also indicate which pathogen and pest species have the potential for development of decision support models. • We sampled 27 crop pathogens and pests across altitudes in Rwanda and Burundi. • Machine learning identified environmental predictors of abundance. • Crop host density and cropland connectivity were predictors for some species. • Forty-four percent of the pathogens and pests were more common at lower altitudes. • Tropical highlands may experience greater crop losses under climate change.

  PublisherDOI

• Expert Knowledge Elicitation: Accessing the Big Data in Experts’ Brains

  Phytopathology · 2025-09-15 · 1 citations

  articleSenior author

  A vast amount of expert knowledge currently remains inaccessible to digital information systems. Expert knowledge elicitation is a systematic approach to accessing and synthesizing the insights of subject matter experts, especially when available objective data are incomplete. In plant pathology, expert knowledge elicitation is valuable for addressing urgent, uncertain, and/or future challenges, such as emerging disease threats, complex epidemiological systems, knowledge gaps when resources are limited, and future scenarios. This perspective explores when expert knowledge elicitation is most effective for addressing plant health challenges, emphasizing its role in informing timely, expert-based decisions. We discuss lessons learned from real-world implementations across diverse regions and pathosystems, highlighting strategies for eliciting, structuring, and interpreting expert-derived data, as well as associated caveats. We frame expert knowledge as a form of big data and outline how existing big-data streams (e.g., remote sensing, crowdsourced reports, and digital surveillance) can inform expert judgements. Outputs from expert knowledge elicitation can be captured as scalable datasets (text, tabular, audio, and video) that enable artificial intelligence-supported synthesis. We illustrate how expert knowledge can be integrated in Bayesian analyses, providing a transparent and rigorous approach to understanding uncertainty and improving inference. Finally, we outline future opportunities, including integration with artificial intelligence, to scale and strengthen expert knowledge elicitation in support of global plant health. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

  PublisherDOI

• Pathogen and pest communities in agroecosystems across climate gradients: Anticipating future challenges in the highland tropics

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-13 · 1 citations

  preprintSenior authorCorresponding

  CONTEXT: Tropical cropping systems must adapt to the current and future geographic distribution of pathogen and pest communities. An important research gap is how climate change may shift the distribution of pathogens and pests in tropical lowlands and highlands. OBJECTIVE: We evaluated the current geographic risk of 27 pathogens and pests in the production of four food security crops (banana, cassava, potato, and sweetpotato) in the Great Lakes region of Africa, and the potential future risk under climate change. Models for each pathogen and pest indicate the potential for changes in geographic distribution, with model fit indicating the potential for decision support systems to facilitate management. METHODS: First, cropland connectivity analysis identified locations likely important in the spread of crop-specific pathogens and pests, such as locations in Rwanda and Burundi. Second, we surveyed the 27 economically important pathogens and pests in Rwanda and Burundi, mapping the distribution of each across climate gradients and quantifying patterns of association. Third, we used machine learning to develop models of each species as a function of environmental variables, including host landscape variables. We also evaluated the increase in temperature across altitudes under future climate change scenarios in this region. RESULTS AND CONCLUSIONS: Among the ten machine-learning algorithms evaluated, random forests and support vector machines generally performed best for predicting severity and infestation. Host landscape variables were useful predictors for some species. Based on climate matching, 44% of the pathogens and pests could become more common with warmer temperatures at higher altitudes, while 17% may become less common. SIGNIFICANCE: This study indicates adaptation priorities for crop health in a region with multiple challenges to agricultural sustainability. The models developed here also indicate which species may have more potential and relevance for future development of pathogen and pest forecasts.

  PublisherDOI

• Climate change and plant disease

  Elsevier eBooks · 2025-01-01 · 1 citations

  book-chapterSenior author
  PublisherDOI

• Simple network models integrate global change, social dynamics and management interventions in biosecurity scenario analysis

  2025-06-13

  preprintOpen access

  Global change and public participation are both areas of considerable uncertainty in estimating the success of biosecurity response strategies but are poorly integrated in most available models. We introduce INApest() , a novel network simulation method which integrates social and global change factors, as well as pest biology and multiple management variables in scenario analyses of biosecurity responses. INApest() separates the management response into four key parameters: probability of detection; management adoption; eradication of local populations; spread reduction (e.g. through movement restrictions or hygiene measures). It also permits simulation of biosecurity responses which evolve organically as new incidences of the pest are detected and information about the pest and management technologies spread through the network. We demonstrate selected functionality of INApest() using Nassella neesiana (Chilean Needle Grass; CNG), a slow-spreading pasture weed that impacts animal health, as a case-study. Realistic historical CNG spread rates are reproduced under a no management scenario using dispersal kernels derived from known natural and human mediated spread mechanisms. Scenario analyses comparing over 15,000 parameter combinations reveal that communication of invasive threat to farms neighbouring known infestations significantly reduces the management efficacy (farm-scale eradication probability and spread reduction) required for successful containment. We use targeted simulation experiments to show how INApest() permits assessment of cross-border consequences of local management decisions, and the effect of communication between landowners on management success. INApest() has the potential to be used at multiple scales and to explore a wide range of management, global change and social scenarios.

  PublisherOA PDFDOI

• Wheat diseases and pests in Pakistan: a nationwide assessment.

  2025-09-29

  articleOpen accessCorresponding
  PublisherDOI

• Phytosanitary Challenges and Solutions for Roots and Tubers in the Tropics

  Annual Review of Phytopathology · 2025-09-03 · 4 citations

  reviewOpen accessSenior author

  Vegetatively propagated crops such as cassava, potato, sweetpotato, and yam, or roots and tubers (RTs), play a major role in food security in low- and middle-income countries, yet phytosanitary issues in the tropics lead to substantial yield and quality losses. Challenges to production include institutional limitations that prevent effective responses and potential buildup of pathogens during clonal propagation. Addressing these challenges in a climate change context and diverse sociocultural environments requires a multifaceted approach, including improving access and availability to clean seed by strengthening seed systems; breeding for host resistance and disseminating resistant varieties; strengthening on-farm seed management; and designing effective policies and regulations to deal with seedborne diseases. Vital cross-cutting activities that can help to tackle the phytosanitary challenges of RTs include capacity strengthening, research on emergent pathogens, and improving regional cooperation and harmonization of phytosanitary standards to manage transboundary seed movement.

  PublisherOA PDFDOI

Recent grants

• Acquisition of Equipment to Enhance Gene Expression Analysis Capacity at Kansas State University

  NSF · $354k · 2004–2007

• Collaborative Research: Distance- and Phylogeny-based Pathogen Transmission in Forest Communities

  NSF · $111k · 2005–2009

• Collaborative Research: Epidemic waves, landscape heterogeneity, and Spatial Scale

  NSF · $363k · 2005–2011

Frequent coauthors

• G. A. Forbes

  CGIAR

  114 shared

• Ravin Poudel

  92 shared

• Ari Jumpponen

  Kansas State University

  86 shared

• K. F. Andersen

  University of Florida

  84 shared

• Linda L. Kinkel

  University of Minnesota

  66 shared

• Timothy C. Paulitz

  Agricultural Research Service

  65 shared

• Robert L. Bowden

  Kansas State University

  65 shared

• Brian B. McSpadden Gardener

  The Ohio State University

  64 shared