University of California
Robert C. Spear, PhD

Robert C. Spear, PhD

Professor Emeritus
School of Public Health
Division of Environmental Health Sciences
University of California
Berkeley, CA 94720-7360
Curriculum Vitae
Current Research Interests
• Mathematical modeling of toxicological and infectious processes
• Statistical issues in exposure assessment
Key Publications

S. Wang and R.C. Spear, Exploring the contribution of host susceptibility to epidemiological patterns of S. japonicuminfection using an individually-based model, Am. J. Trop. Med. Hyg., 92, 1245-1252 , 2015 doi10.4269/ajtmh.14-0691.

R. C. Spear, E. Seto, S. Liang, M. Birkner, A. Hubbard, D. Qiu, C. Yang, B. Zong, F. Xu, X. Gu, and G. M. Davis, Factors Influencing the Transmission of Schistosoma Japonicum in the Mountains of Sichuan Province, Am. J. Trop. Med. Hyg., 70(10), 48–56, 2004.

R.C. Spear and G.M. Hornberger, Eutrophication in Peel Inlet: II. Identification of Critical Uncertainties Via Generalized Sensitivity Analysis, Water Research 14:43–49, 1980.

R.C. Spear, W.J. Popendorf, W.F. Spencer and T.H. Milby, Worker Poisoning Due to Paraoxon Residue, J. of Occup. Med. 19(6):411–414, 1977.

R.C. Spear, W.J. Popendorf, J.T. Leffingwell, T.H. Milby, J.E. Davis and W.F. Spencer, Fieldworkers Response to Weathered Residues of Parathion, J. of Occup. Med. 19(6):406–410, 1977.

Research Adviser for Graduate Students
Brief Bio
Dr. Spear is an engineer by training. His research interests focus on the assessment and quantification of human exposures to toxic and infectious agents in the environment. His early work concerned the exposure of agricultural workers to pesticides. In recent years his research has concerned the use of mathematical and statistical techniques in the assessment and control of exposures to both chemical and biological agents. His current work is in collaboration with colleagues both at Berkeley and at the Sichuan Institute of Parasitic Disease in China focused on determinants of the incidence and control of the parasitic disease schistosomiasis. The innovative aspects of this work relate to the integration of traditional epidemiological field data, utilizing geographic information system technology, with that available from both high and low resolution remotely sensed data. Both sources of data are integrated through mathematical models that allow both tracking and forecasting of disease intensity over time. Recent work has focused on the importance of exposure versus individual susceptibility as bases for disease surveillance in low transmission environments.