Modelling the spread and mitigation of an emerging vector-borne pathogen: Citrus greening in the US

Author summaryCitrus Greening or Huanglongbing (HLB) disease is caused by a bacterium invading the phloem of citrus trees. The disease is transmitted by the Asian citrus psyllid (ACP), a small invasive insect that feeds and lays eggs on the developing flush shoots in citrus. ACP was first found in the U.S. in Florida in 1998. Citrus greening was detected in Florida in 2005 and rapidly spread through commercial and residential citrus. Both the insect and disease have subsequently been detected and are spreading in the two other main citrus U.S. producing states, Texas and California. Both federal and state agencies in cooperation with the citrus industry and growers are working to slow or halt the spread of this devastating disease. Predictive models, as developed in this work, are increasingly important in shaping regulatory and operational policies for emerging outbreaks. Our model was develop using survey data from Texas and we show how to transfer and test the model parameters in both southern California and California's central valley. The model can be used to screen coordinated and reactive management strategies, which help in the effort to slow the spread of the insect and disease. Predictive models, based upon epidemiological principles and fitted to surveillance data, play an increasingly important role in shaping regulatory and operational policies for emerging outbreaks. Data for parameterising these strategically important models are often scarce when rapid actions are required to change the course of an epidemic invading a new region. We introduce and test a flexible epidemiological framework for landscape-scale disease management of an emerging vector-borne pathogen for use with endemic and invading vector populations. We use the framework to analyse and predict the spread of Huanglongbing disease or citrus greening in the U.S. We estimate epidemiological parameters using survey data from one region (Texas) and show how to transfer and test parameters to construct predictive spatio-temporal models for another region (California). The models are used to screen effective coordinated and reactive management strategies for different regions.