Comparing the efficacy of control strategies for infectious disease outbreaks using field and simulation studies.

Diseases characterized by long distance inoculum dispersal (LDD) are among the fastest spreading epidemics in both natural and managed landscapes. Management of such epidemics is extremely challenging because of asymptomatic infection extending at large spatial scales and frequent escape from the newly established disease sources. We compared the efficacy of area- and timing-based disease management strategies in artificially initiated field epidemics of wheat stripe rust and complemented with simulations from an updated version of the spatially explicit model EPIMUL, using model parameters relevant to field epidemics. The model was further used to expand the number of epidemic mitigations beyond that feasible to incorporate in the field. The field experiment was conducted for 2 years in two locations having different climatic conditions. Culling and protection treatments were applied at different times after epidemic initiation and to different spatial extents surrounding the outbreaks. In each experiment, treatments were replicated four times in plots 33.5 m long and 1.52 m wide with a 0.76 × 0.76 m inoculated focus centered within each plot. Disease gradients were assessed along the center lines of the plots at 1.52 m intervals both upwind and downwind from the focus. Both field and simulation results indicated that control measures applied over the entire population were highly effective in suppressing the epidemics by more than 99% but may not always be logistically and economically feasible at large spatial scales. Comparison between the variable sized treatment areas and application timings suggested that implementing contiguous premises (CP) cull at 1 day after first sporulation in the outbreak focus reduced rust by 52% and 60% in Corvallis and Madras, respectively. However, altering the cull size did not significantly affect the disease epidemic development, which suggested that early timing had a greater influence in suppressing the epidemics than did increased area of application. However, sufficiently large, treated areas may compensate for a delay in application timing to some extent. Results from these replicated treatments may help to devise appropriate management strategies for other LDD pathogens.

Predictive Modeling of Brown Rust of Sugarcane Based on Temperature and Relative Humidity in Florida.

Logistic regression models were developed from 5 years (2014 to 2018) of disease severity and weather data in an attempt to predict brown rust of sugarcane at the Everglades Research and Education Center in Belle Glade, Florida. Disease severity (percentage area of the top visible dewlap leaf covered by rust) was visually assessed in the field every 2 weeks for two varieties susceptible to brown rust. A total of 250 variables were derived from weather data for 10- to 40-day periods before each brown rust assessment day. A subset of these variables were then evaluated as potential predictors of severity of brown rust based on their individual correlation or their biological meaningfulness. Analyses of correlation and stepwise logistic regression allowed us to identify afternoon humid thermal ratio (AHTR), temperature-based duration variables, and their interaction terms as the most significant variables associated with brown rust epidemics of sugarcane in Florida. The nine best predictive models were identified based on model accuracy, sensitivity, specificity, and estimates of the prediction error. The prediction accuracy of these models ranged from 73 to 85%. Single-variable model BR2 (based on AHTR) classified 89% of the epidemic and 81% of the nonepidemic status of the disease. More than 83% of the epidemics and 81% of the nonepidemic status of sugarcane brown rust was correctly classified via multiple-variable models. These models can be used as components of a rust disease warning system to assist in the management of brown rust epidemics of sugarcane in south Florida.

Weather-Based Predictive Modeling of Orange Rust of Sugarcane in Florida.

Epidemics of sugarcane orange rust (caused by Puccinia kuehnii) in Florida are largely influenced by prevailing weather conditions. In this study, we attempted to model the relationship between weather conditions and rust epidemics as a first step toward development of a decision aid for disease management. For this purpose, rust severity data were collected from 2014 through 2016 at the Everglades Research and Education Center, Belle Glade, Florida, by recording percentage of rust-affected area of the top visible dewlap leaf every 2 weeks from three orange rust susceptible cultivars. Hourly weather data for 10- to 40-day periods prior to each orange rust assessment were evaluated as potential predictors of rust severity under field conditions. Correlation and stepwise regression analyses resulted in the identification of nighttime (8 PM to 8 AM) accumulation of hours with average temperature 20 to 22°C as a key predictor explaining orange rust severity. The five best regression models for a 30-day period prior to disease assessment explained 65.3 to 76.2% of variation of orange rust severity. Prediction accuracy of these models was tested using a case control approach with disease observations collected in 2017 and 2018. Based on receiver operator characteristic curve analysis of these two seasons of test data, a single-variable model with the nighttime temperature predictor mentioned above gave the highest prediction accuracy of disease severity. These models have potential for use in quantitative risk assessment of sugarcane rust epidemics.

Comparison of Progress of Brown Rust and Orange Rust and Conditions Conducive for Severe Epidemic Development During the Sugarcane Crop Season in Florida.

Brown rust (caused by Puccinia melanocephala) and orange rust (caused by P. kuehnii) are two major diseases of sugarcane in Florida. To better understand the epidemiology of these two rusts, disease severity and weather variables were monitored for two seasons in cultivars CL90-4725 (susceptible to brown rust and resistant to orange rust) and CL85-1040 (susceptible to orange rust and resistant to brown rust). Brown rust was most severe during mid-May to mid-July, whereas orange rust severity peaked during two periods: mid-May to early August and then November to December. Overall, disease severity was higher for orange rust than for brown rust. Maximum disease severity was correlated with the number of hours at night with an average temperature of 20 to 22.2°C for brown rust one season and orange rust both seasons. Slightly higher correlation was obtained when relative humidity above 90% was included in the number of hours at night with an average temperature of 20 to 22.2°C for brown rust but not orange rust, suggesting that leaf wetness is not a limiting factor for either disease in Florida. Epidemics of brown rust began at lower night temperatures (16.7 to 22.2°C) in one season, but epidemics of orange rust lasted longer under higher temperatures. The correlation of rust severity on recently emerged leaves with conducive temperatures recorded in 10-, 20-, or 30-day windows starting 7 days before disease assessment suggested that earlier inoculum production is needed to create severe epidemics that result in yield loss.