Bayesian inference for an emerging arboreal epidemic in the presence of control

Abstract

The spread of Huanglongbing through citrus groves is used as a case study for modeling an emerging epidemic in the presence of a control. Specifically, the spread of the disease is modeled as a susceptible-exposed-infectious-detected-removed epidemic, where the exposure and infectious times are not observed, detection times are censored, removal times are known, and the disease is spreading through a heterogeneous host population with trees of different age and susceptibility. We show that it is possible to characterize the disease transmission process under these conditions. Two innovations in our work are (i) accounting for control measures via time dependence of the infectious process and (ii) including seasonal and host age effects in the model of the latent period. By estimating parameters in different subregions of a large commercially cultivated orchard, we establish a temporal pattern of invasion, host age dependence of the dispersal parameters, and a close to linear relationship between primary and secondary infectious rates. The model can be used to simulate Huanglongbing epidemics to assess economic costs and potential benefits of putative control scenarios.

Asymptomatic spread of huanglongbing and implications for disease control

“…This short latency period is consequential for the rapid infection of nymphs and previously uninfected adult psyllids on the newly infected flush. It is important to note that the short latency period experimentally determined here is consistent with the cyclic exponential and Weibull models for duration of latency period used by Parry et al (2). The long asymptomatic period of the tree is accompanied by the rapid spread of infection among psyllids and new flush, thereby leading to large percentages, even 100%, of trees infected in a grove before any symptoms become manifest.…”

“…The geometry of groves and the foci of psyllid entry generally are not taken into consideration in these models, despite the fact that they clearly influence the initial spread of HLB and the distribution of symptomatic trees on the longer time scale of 1-2.5 y or, as recently documented, 6 y (6). Spatially explicit modeling, however, is incorporated in a recent study of HLB epidemic outbreaks (2). Although our specific focus and the comments above pertain to HLB, it is important to note that there is a considerable literature using spatially explicit models of transmission of plant pathogens incorporating both compartment models (7) (SI Appendix, refs.…”

Mathematical models are a powerful method to understand and control the spread of Huanglongbing

“…We review here those models which have been applied to HLB because they demonstrate the major insights models have already provided to this disease system. Recent modeling of HLB includes Jacobsen, Stupiansky & Pilyugin (2013), Parry et al (2014) and Lee et al (2015). These articles elucidate the spread of HLB using three different approaches, namely through differential equation modeling, statistical analysis, and individual-based modeling, respectively.…”

Sharka Epidemiology and Worldwide Management Strategies: Learning Lessons to Optimize Disease Control in Perennial Plants

“…Indeed, they allow the estimation of key factors like dispersal parameters (108,132), the simulation and the epidemiological or economic optimization of different control strategies using dedicated software (22, 104), and the prediction of crucial variables like times to eradication (50) and-durability of control tactics. Thus, models enable the theoretical demonstration of the improvement brought by new management strategies while overcoming the ethical, logistical, or economical obstacles associated with other methods.…”