Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.

The potential for modern biology to identify new sources for genetical, chemical and biological control of plant disease is remarkably high. Successful implementation of these methods within globally and locally changing agricultural environments demands new approaches to durable control. This, in turn, requires fusion of population genetics and epidemiology at a range of scales from the field to the landscape and even to continental deployment of control measures. It also requires an understanding of economic and social constraints that influence the deployment of control. Here I propose an epidemiological framework to model invasion, persistence and variability of epidemics that encompasses a wide range of scales and topologies through which disease spreads. By considering how to map control methods onto epidemiological parameters and variables, some new approaches towards optimizing the efficiency of control at the landscape scale are introduced. Epidemiological strategies to minimize the risks of failure of chemical and genetical control are presented and some consequences of heterogeneous selection pressures in time and space on the persistence and evolutionary changes of the pathogen population are discussed. Finally, some approaches towards embedding epidemiological models for the deployment of control in an economically plausible framework are presented.

Motivated by questions such as “Why do some diseases take off, while others die out?” and “How can we optimize the deployment of control methods,” we introduce simple epidemiological concepts for the invasion and persistence of plant pathogens. An overarching modeling framework is then presented that can be used to analyze disease invasion and persistence at a range of scales from the microscopic to the regional. Criteria for invasion and persistence are introduced, initially for simple models of epidemics, and then for models with greater biological realism. Some ways in which epidemiological models are used to identify optimal strategies for the control of disease are discussed. Particular attention is given to the spatial structure of host populations and to the role of chance events in determining invasion and persistence of plant pathogens. Finally, three brief case studies are used to illustrate the practical applications of epidemiological theory to understand invasion and persistence of plant pathogens. These comprise long-term predictions for the persistence and control of Dutch elm disease; identification of methods to manage the spread of rhizomania on sugar beet in the U.K. by matching the scale of control with the spatial and temporal scales of the disease; and analysis of evolutionary change in virus control to identify risks of inadvertent selection for damaging virus strains.

Little is known about how best to deploy scarce resources for disease control when epidemics occur in different but interconnected regions. We use a combination of optimal control methods and epidemiological theory for metapopulations to address this problem. We consider what strategy should be used if the objective is to minimize the discounted number of infected individuals during the course of an epidemic. We show, for a system with two interconnected regions and an epidemic in which infected individuals recover and can be reinfected, that equalizing infection in the two regions is the worst possible strategy in minimizing the total level of infection. Treatment should instead be preferentially directed at the region with the lower level of infection, treating the other subpopulation only when there is resource left over. The same strategy holds with preferential treatments of regions with lower levels of infection when quarantine is introduced.

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. Significance Fast-moving and destructive emerging epidemics are seldom left to run their course because of the imperative to control further spread. Contemporaneous control measures, however, greatly complicate the characterization of the disease transmission process and the extraction of the epidemiological parameters of interest. The spread of Huanglongbing on orchard scales is used as a case study for modeling an emerging epidemic in the presence of control. We show that even with missing and censored data, and with seasonal and host age dependencies, it is possible to infer the parameters of a fully spatiotemporal stochastic model of disease spread. The value of the fitted model is to provide an engine for simulation studies of the costs and benefits of proposed disease control strategies.