Botanical Epidemiology: Some Key Advances and its Continuing Role in Disease Management

Epidemiology involves the study of the temporal, spatial, and spatio-temporal dynamics of disease in populations, and the utilization of results of experiments and surveys to describe, understand, compare, and predict epidemics. Such understanding and description of epidemics can lead directly to the development and evaluation of efficient control strategies and tactics. Mathematical and statistical models are key tools of the epidemiologist. Recent advances in statistics, including linear and nonlinear mixed models, are allowing a more appropriate matching of data type and experimental (or survey) design to the statistical model used for analysis, in order to meet the objectives of the investigator. Coupled ordinary and partial differential equations, as well as simpler growth-curve equations, are especially useful deterministic models for representing plant disease development in fields in time and space over single seasons or many years, and their use can lead to appraisal of control strategies through metrics such as the basic reproduction number, a summary parameter that may be calculated for many general epidemic scenarios. Recently, compelling arguments have been made for the use of Bayesian decision theory in developing and evaluating real-time disease prediction rules, based on measured disease or weather conditions and either empirical or mechanistic models for disease or control intervention. Through some simple calculations of predictor accuracy and (prior) probability of an epidemic (or the need for control), the success of any predictor can be quantified in terms of the estimated probability of random observations being epidemics when predicted to be epidemics or not epidemics. Overall, despite the many contributions in epidemiology over the past four decades, more effort is still needed to convince those outside of epidemiology to more fully use epidemiological results and insights into the development and evaluation of disease controls.