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ARS Home » Southeast Area » Fort Pierce, Florida » U.S. Horticultural Research Laboratory » Subtropical Plant Pathology Research » Research » Publications at this Location » Publication #339886

Research Project: Mitigating High Consequence Domestic, Exotic, and Emerging Diseases of Fruits, Vegetables, and Ornamentals

Location: Subtropical Plant Pathology Research

Title: Evidence-based Controls for Epidemics Using Spatio-temporal Stochastic Model as a Bayesian Framwork

item Adrakey, Hola - Heriot-Watt University
item Gibson, Gavin - Heriot-Watt University
item Streftaris, George - Heriot-Watt University
item Gilligan, Christopher - University Of Cambridge
item Cunniffe, Nik - Cambridge University
item Gottwald, Timothy

Submitted to: Journal of the Royal Society Interface
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/30/2017
Publication Date: 11/29/2017
Citation: Adrakey, H.K., Gibson, G.J., Streftaris, G., Gilligan, C.A., Cunniffe, N.J., Gottwald, T.R. 2017. Evidence-based Controls for Epidemics Using Spatio-temporal Stochastic Model as a Bayesian Framwork. Journal of the Royal Society Interface. 14:20170386.

Interpretive Summary: The removal of infected hosts (plant or animal) during the course of a disease epidemic is considered as the most efficient control strategy to halt epidemics of highly infectious diseases. Therefore, when resources are scarce and the number of hosts that can be considered for removal is constrained, it is important that those hosts that may play the greatest role in the subsequent dynamic of the epidemic (the most important in spreading the disease) are preferentially targeted. This paper introduces a prospective approach to targeting control measures for highly infectious diseases where disease spreads regionally. In particular we introduce a prioritization scheme based on the idea that hosts with the highest threat should be considered for removal first. We developed a Bayesian type model (a type model that is based on some prior knowledge) to predict which individuals in the population would be the most efficacious to remove so as to achieve disease eradication the most quickly and by removing the least hosts. We use data from an epidemic of citrus canker (1995-2006), a severe disease of citrus that devastated the Florida and other citrus industries, as an example of a disease that could have been more effectively controlled/eradicated. The model will be useful to regulatory agencies charged with controlling plant and animal diseases and to researchers modeling disease epidemics.

Technical Abstract: The control of highly infectious diseases of agricultural and plantation crops and livestock represents a key challenge in epidemiological and ecological modelling, with implemented control strategies often being controversial. Mathematical models, including the spatio-temporal stochastic models considered here, are playing an increasing role in the design of strategies as agencies seek to strengthen the evidence on which selected strategies are based. Here, we investigate approaches to informing the choice of control strategies using spatio-temporal models within the Bayesian framework. Focusing on control strategies that are based on pre-emptive removal of infectious individuals, we assess a range of approaches to prioritizing individuals for removal that take account of observations of an emerging epidemic and that a measure that takes account of both the likelihood of infection and the potential infection hazard poses to susceptible leads to the most effective control strategies. We illustrate the approach using simulated data and historic data on an epidemic of citrus canker in Florida. A key feature of the approach is the use of functional-model representations of the epidemic model to couple epidemic trajectories under different control strategies. This induces strong positive correlations between the outcomes for the respective controls, serving to so reduce both the variance of the difference in outcomes and, consequently, the need for extensive simulation.