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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 #304343
Title: Bayesian analysis for inference of an emerging epidemic: citrus canker in urban landscapes

Author
item NERI, F - Cambridge University
item COOK, A - National University Of Singapore
item GIBSON, G - Heriot-Watt University
item Gottwald, Timothy
item GILLIGAN, C - Cambridge University

Submitted to: PLoS Computational Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/27/2014
Publication Date: 4/24/2014
Citation: Neri, F.M., Cook, A.R., Gibson, G.J., Gottwald, T.R., Gilligan, C.A. 2014. Bayesian analysis for inference of an emerging epidemic: citrus canker in urban landscapes. PLoS Computational Biology. 10(4):e1003587. https://doi.org/10.1371/journal.pcbi.1003587.
DOI: https://doi.org/10.1371/journal.pcbi.1003587

Interpretive Summary: Outbreaks of infectious plant diseases often require a rapid response from policy makers. The key to containment is an early and rigorous response. The choice of an adequate level of response relies upon available knowledge of how the pathogen spreads among other things. However, for new disease, for which little is known, quite often such knowledge is lacking or of little use. This poses a challenge to policy makers and researchers to determine: how quickly can the parameters of an emerging disease be estimated and how soon can the future progress of the epidemic be reliably predicted? In this paper we investigate data from an epidemic of Asiatic citrus canker in urban Miami for which most of these parameters were previously known. Through statistical modeling we analyze rates and extent of spread of the disease and characterize the rate of infection associated with historical extreme weather events. We use the model to make uninformed predictions (ignoring the data that we have) from the early stages of the epidemic, assuming complete ignorance of the future environmental influences. Such a prediction unsurprisingly failed. However, we found that we could improve predictions dramatically if we assume prior knowledge of either the main environmental trend, or the main environmental weather events. Thus, we have developed a modeling method that allows us to predict what will happen when a new pathogen is introduced with a minimum of prior information. This means that when researchers and regulatory agencies can compile a small about of accurate data about the spread of a newly introduced disease, we can predict the outcome much more accurately. This will greatly help policy makes make informed decisions very early during an epidemic.

Technical Abstract: Outbreaks of infectious diseases require a rapid response from policy makers. The choice of an adequate level of response relies upon available knowledge of the spatial and temporal parameters governing pathogen spread, affecting, amongst others, the predicted severity of the epidemic. Yet, when a new pathogen is introduced into an alien environment, such information is often lacking or of no use, and epidemiological parameters must be estimated from the first observations of the epidemic. This poses a challenge to epidemiologists: how quickly can the parameters of an emerging disease be estimated and how soon can the future progress of the epidemic be reliably predicted? We investigate these issues using a unique, spatially and temporally-resolved dataset for the invasion of a plant disease, Asiatic citrus canker in urban Miami. We use epidemiological models, Bayesian Markov-chain Monte Carlo, and advanced spatial statistical methods, to analyze rates and extent of spread of the disease. A rich and complex epidemic behavior is revealed. The spatial scale of spread is approximately constant over time, and can be estimated rapidly with great precision (although the evidence for long-range transmission is inconclusive). In contrast, the rate of infection is characterized by strong monthly fluctuations that we associate to extreme weather events. Uninformed predictions from the early stages of the epidemic, assuming complete ignorance of the future environmental drivers, fail because of the unpredictable variability of the infection rate. Conversely, predictions improve dramatically if we assume prior knowledge of either the main environmental trend, or the main environmental events. A contrast emerges between the high detail attained by modelling in the spatio-temporal description of the epidemic, and the bottleneck imposed on epidemic prediction by the limits of meteorological predictability. We argue that identifying such bottlenecks will be a fundamental step in future modelling of weather-driven epidemics.