Skip to main content
Think and Do The Extraordinary
Support the Unit

Peter Ojiambo


Epidemiology and integrated management of plant diseases

Partners Building III 239



Program Overview:

My research interest encompasses botanical epidemiology and integrated management of plant diseases, with emphasis on understanding the basic biology and ecology of plant pathogens and the use of this information to develop disease management tools.  Application of mathematical and statistical models and computer technology to describe the dynamics of plant diseases in space and time is an integral aspect of my research program whose primary goal is to develop management decisions based on risk assessment and prediction of outbreaks of epidemics. I am also exploring research that bridges botanical epidemiology and pathogen population genetics.

Effective disease management through disease prediction and risk assessment requires an understanding of the biology and ecology of plant pathogens. Basic research is routinely conducted to understand the effects of changing weather patterns, shifts in pathogen populations, host resistance, and changes in cropping practices on the appearance of new and resurgence of existing plant diseases. My laboratory employs mathematical, statistical and computer models to study these effects and their possible consequences on disease development in space and time. I take advantage of modern statistical and ecological techniques, advanced computer technology and large weather and disease databases to develop and use prediction and risk assessment models as decision-making tools in the integrated management of plant diseases.

Ongoing research projects:

  1. Developing a Spatially Explicit Network Model to Assess Effectiveness of Within- and Between-node Disease Control on Epidemic Spread.
  2. Characterizing Epidemic Spread Across a Landscape using SEIR Compartmental Models
  3. Joint Modelling of Disease Outbreak and Epidemic Duration in Risk Assessment in Plant Disease Surveillance Programs.
  4. Imputing Locations of Multiple Sources of Epidemic Outbreak from Population Dynamic Models.
  5. Characterize the effects of Ecological Spillovers on the Spread of Epidemics Caused by Long Distance Dispersed Pathogens.

Current Graduate Students:

  1. Vinicius Garnica (PhD) — Started Spring 2021. Project: Effects of Environment on the Risk of Disease Occurrence and Cultivar Stability to Stagonospora nodorum blotch in Winter Wheat.
  2. Aleksander Tako (PhD) — Started Fall 2021. Project: Bloom blight Management  and Understanding the Systemic Phase of Fireblight Infection in Apple (Co-Advisor).
  3. Jophr Galian (PhD) — Started Fall 2022. Project: Decision Support System to Optimize Fungicide Use Against Downy Mildew Pathogens.
  4. Roden Lizardo (PhD) — Started Fall 2022. Project: Climate Modeling and Assessing  Biosecurity Risk of Establishment of Plant Parasitic Nematodes in the US (Co-Advisor).

Other Lab Personnel:

  1. Dr. Fangfang Guo (Post-Doctoral Research Associate)


  1. PP 790: Epidemiology: Theory & Application (Fall semester).
  2. PP 790-004: Advances in Evolutionary Ecology & Population Biology (Alternate Spring semester).

Selected Publications (View Publications on Google Scholar):

  • U. Adhikari, J. Brown, P.S. Ojiambo and C. Cowger. 2023. Effects of host and weather factors on the growth rate of Septoria nodorum blotch lesions on winter wheat. Phytopathology
  • I. Kikway, A.P. Keinath and P.S. Ojiambo. 2022. Field occurrence and overwintering of oospores of Pseudoperonospora cubensis in the southeastern United States. Phytopathology 112:1946-1955.
  • J. Atehnkeng, P.S. Ojiambo, A. Ortega-Beltran, J. Augusto, P.J. Cotty and R. Bandyopadhyay, R. 2022. Impact of frequency of application on the long-term efficacy of the biocontrol product Aflasafe in reducing aflatoxin contamination in maize. Frontiers in Microbiology 13: 1049013. doi: 10.3389/fmicb.2022.1049013.
  • W. Copes, and P.S. Ojiambo. 2021. Efficacy of hypochlorite as a disinfestant against fungal pathogens in agricultural and horticultural plant production: a systematic review and meta-analysis. Phytopathology 111:1369-1379.
  • A.M.E. Ojwang’, T. Ruiz, S. Bhattacharyya, S. Chatterjee, P.S. Ojiambo, and D.H. Gent. 2021. A general framework for spatio-temporal modeling of epidemics with multiple epicenters: Application to an aerially dispersed plant pathogen. Frontiers in Applied Mathematics and Statistics 7:721352. DOI: 10.3389/fams.2021.721352.
  • J.M. Luis, I. Carbone, G.A., Payne, D. Bhatnagar, J.W. Cary, G.G. Moore, M.D. Lebar, Q. Wei, B. Mack, and P.S. Ojiambo. 2020. Characterization of morphological changes within stromata during sexual reproduction in Aspergillus flavus. Mycologia 112:908-920.
  • M.H. Lewis, I. Carbone, J.M. Luis, G.A. Payne, K.L. Bowen, A. Hagan, R. Kemerait, R. Heiniger, and P.S. Ojiambo. 2019. Biocontrol strains differentially shift the genetic structure of indigenous soil populations of Aspergillus flavus. Frontiers in Microbiology doi: 10.3389/fmicb.2019.01738.
  • P.S. Ojiambo, P. Battilani, J. Cary, B. H. Blum, and I. Carbone. 2018. Cultural and genetic approaches to manage aflatoxin contamination: recent insights provide opportunities for improved control. Phytopathology 108: 1024-1037.
  • K.N. Neufeld, A.P. Keinath, B. Dutta, B.K. Gugino, D.B. Langston, M.L. Ivey, M.T. McGrath, S.A. Miller, E.J. Sikora and P.S. Ojiambo. 2018. Predicting the risk of cucurbit downy mildew in eastern United States using an integrated aerobiological modeling system. International Journal of Biometeorology 62: 655-668.
  • K.N. Neufeld, A.P. Keinath and P.S. Ojiambo. 2017. A model for predicting the infection risk of cucumber by Pseudoperonospora cubensis. Microbial Risk Analysis 6: 21‒30.
  • A. Thomas, I. Carbone, K. Choe, L.M. Quesada-Ocampo and P.S. Ojiambo. 2017. Resurgence of cucurbit downy mildew in the United States: Insights from comparative genomic analysis of Pseudoperonospora cubensisEcology and Evolution 7: 6231-6246.
  • L.K. Mehra, C. Cowger and P.S. Ojiambo. 2017. A model for predicting onset of Stagonospora nodorum blotch in winter wheat based on pre-planting and weather factors. Phytopathology 107: 635-644.
  • L.K. Mehra, C. Cowger, K. Gross and P.S. Ojiambo. 2016. Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models. Frontiers in Plant Science 7:390. doi: 10.3389/fpls.2016.00390.
  • P.S. Ojiambo, D.H. Gent, L.M. Quesada-Ocampo, M.K. Hausbeck and G.J. Holmes. 2015. Epidemiology and population biology of Pseudoperonospora cubensis: A model system for management of downy mildews. Annual Review of Phytopathology 53: 223-246.
  • P.S. Ojiambo and E.L. Kang. 2013. Modeling spatial frailties in survival analysis of cucurbit downy mildew epidemics. Phytopathology 103: 216-227.


View on Google Scholar


BS, Agriculture (First Class Hons.), University of Nairobi, Kenya (1994)
MS, Plant Pathology, University of Nairobi, Kenya (1997)
Ph.D, Plant Pathology, The University of Georgia (2004)