Evidence-based controls for epidemics using spatio-temporal stochastic models in a Bayesian framework

Journal of the Royal Society Interface

Volumn 14, Issue 136, 2017, Pages

  Adrakey, Hola K ^a,b   Streftaris, George ^b   Cunniffe, Nik J ^a   Gottwald, Tim R ^c   Gilligan, Christopher A ^a   Gibson, Gavin J ^b  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b HERIOT WATT UNIVERSITY   (United Kingdom)

^c AGRICULTURAL RESEARCH SERVICE   (United States)

Author keywords

Bayesian inference; control strategies; emerging epidemic; non centred parametrization; spatio temporal model

Indexed keywords

AGRICULTURE; BAYESIAN NETWORKS; DISEASE CONTROL; HAZARDS; INFERENCE ENGINES; RISK ASSESSMENT; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC SYSTEMS;

BAYESIAN FRAMEWORKS; BAYESIAN INFERENCE; CONTROL STRATEGIES; EMERGING EPIDEMIC; EVIDENCE-BASED; NON-CENTERED PARAMETRIZATION; PARAMETRIZATIONS; SPATIO-TEMPORAL; SPATIO-TEMPORAL MODELS; STOCHASTIC-MODELING;

STOCHASTIC MODELS;

ARTICLE; BAYES THEOREM; CANKER; CITRUS; CONTROL STRATEGY; CONTROLLED STUDY; EPIDEMIC; EPIDEMIOLOGICAL MODEL; FLORIDA; HOST; INFECTION RISK; NONHUMAN; PLANT DISEASE CONTROL; PRIORITY JOURNAL; SPATIOTEMPORAL ANALYSIS; STOCHASTIC MODEL; BIOLOGICAL MODEL; COMPUTER SIMULATION; DECISION MAKING; MICROBIOLOGY; PLANT DISEASE; PREVENTION AND CONTROL;

BAYES THEOREM; CITRUS; COMPUTER SIMULATION; DECISION MAKING; EPIDEMICS; FLORIDA; MODELS, BIOLOGICAL; PLANT DISEASES;

EID: 85050973723     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2017.0386     Document Type: Article

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