In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 6 ( 2022-6-27), p. e1010218-
Abstract:
As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011–2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010218
DOI:
10.1371/journal.pcbi.1010218.g001
DOI:
10.1371/journal.pcbi.1010218.g002
DOI:
10.1371/journal.pcbi.1010218.g003
DOI:
10.1371/journal.pcbi.1010218.g004
DOI:
10.1371/journal.pcbi.1010218.g005
DOI:
10.1371/journal.pcbi.1010218.g006
DOI:
10.1371/journal.pcbi.1010218.g007
DOI:
10.1371/journal.pcbi.1010218.s001
DOI:
10.1371/journal.pcbi.1010218.s002
DOI:
10.1371/journal.pcbi.1010218.s003
DOI:
10.1371/journal.pcbi.1010218.s004
DOI:
10.1371/journal.pcbi.1010218.s005
DOI:
10.1371/journal.pcbi.1010218.s006
DOI:
10.1371/journal.pcbi.1010218.s007
DOI:
10.1371/journal.pcbi.1010218.s008
DOI:
10.1371/journal.pcbi.1010218.s009
DOI:
10.1371/journal.pcbi.1010218.s010
DOI:
10.1371/journal.pcbi.1010218.s011
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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