In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 19, No. 6 ( 2023-6-2), p. e1010156-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010156
DOI:
10.1371/journal.pcbi.1010156.g001
DOI:
10.1371/journal.pcbi.1010156.g002
DOI:
10.1371/journal.pcbi.1010156.g003
DOI:
10.1371/journal.pcbi.1010156.g004
DOI:
10.1371/journal.pcbi.1010156.g005
DOI:
10.1371/journal.pcbi.1010156.g006
DOI:
10.1371/journal.pcbi.1010156.g007
DOI:
10.1371/journal.pcbi.1010156.g008
DOI:
10.1371/journal.pcbi.1010156.g009
DOI:
10.1371/journal.pcbi.1010156.g010
DOI:
10.1371/journal.pcbi.1010156.g011
DOI:
10.1371/journal.pcbi.1010156.t001
DOI:
10.1371/journal.pcbi.1010156.s001
DOI:
10.1371/journal.pcbi.1010156.s002
DOI:
10.1371/journal.pcbi.1010156.s003
DOI:
10.1371/journal.pcbi.1010156.s004
DOI:
10.1371/journal.pcbi.1010156.s005
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2193340-6
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