Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm.
Predicting the specific magnitude and the temporal peak of the epidemic of individual local outbreaks is critical for infectious disease control. Previous studies have indicated that significant differences in spatial transmission and epidemic magnitude of dengue were influenced by multiple factors,...
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ftdoajarticles:oai:doaj.org/article:d341c8db986a4ac6b89a860edb33e2a8 2023-07-30T04:01:59+02:00 Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. Qinghui Zeng Xiaolin Yu Haobo Ni Lina Xiao Ting Xu Haisheng Wu Yuliang Chen Hui Deng Yingtao Zhang Sen Pei Jianpeng Xiao Pi Guo 2023-06-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0011418 https://doaj.org/article/d341c8db986a4ac6b89a860edb33e2a8 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0011418 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0011418 https://doaj.org/article/d341c8db986a4ac6b89a860edb33e2a8 PLoS Neglected Tropical Diseases, Vol 17, Iss 6, p e0011418 (2023) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2023 ftdoajarticles https://doi.org/10.1371/journal.pntd.0011418 2023-07-09T00:36:33Z Predicting the specific magnitude and the temporal peak of the epidemic of individual local outbreaks is critical for infectious disease control. Previous studies have indicated that significant differences in spatial transmission and epidemic magnitude of dengue were influenced by multiple factors, such as mosquito population density, climatic conditions, and population movement patterns. However, there is a lack of studies that combine the above factors to explain their complex nonlinear relationships in dengue transmission and generate accurate predictions. Therefore, to study the complex spatial diffusion of dengue, this research combined the above factors and developed a network model for spatiotemporal transmission prediction of dengue fever using metapopulation networks based on human mobility. For improving the prediction accuracy of the epidemic model, the ensemble adjusted Kalman filter (EAKF), a data assimilation algorithm, was used to iteratively assimilate the observed case data and adjust the model and parameters. Our study demonstrated that the metapopulation network-EAKF system provided accurate predictions for city-level dengue transmission trajectories in retrospective forecasts of 12 cities in Guangdong province, China. Specifically, the system accurately predicts local dengue outbreak magnitude and the temporal peak of the epidemic up to 10 wk in advance. In addition, the system predicted the peak time, peak intensity, and total number of dengue cases more accurately than isolated city-specific forecasts. The general metapopulation assimilation framework presented in our study provides a methodological foundation for establishing an accurate system with finer temporal and spatial resolution for retrospectively forecasting the magnitude and temporal peak of dengue fever outbreaks. These forecasts based on the proposed method can be interoperated to better support intervention decisions and inform the public of potential risks of disease transmission. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 17 6 e0011418 |
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Directory of Open Access Journals: DOAJ Articles |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Qinghui Zeng Xiaolin Yu Haobo Ni Lina Xiao Ting Xu Haisheng Wu Yuliang Chen Hui Deng Yingtao Zhang Sen Pei Jianpeng Xiao Pi Guo Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Predicting the specific magnitude and the temporal peak of the epidemic of individual local outbreaks is critical for infectious disease control. Previous studies have indicated that significant differences in spatial transmission and epidemic magnitude of dengue were influenced by multiple factors, such as mosquito population density, climatic conditions, and population movement patterns. However, there is a lack of studies that combine the above factors to explain their complex nonlinear relationships in dengue transmission and generate accurate predictions. Therefore, to study the complex spatial diffusion of dengue, this research combined the above factors and developed a network model for spatiotemporal transmission prediction of dengue fever using metapopulation networks based on human mobility. For improving the prediction accuracy of the epidemic model, the ensemble adjusted Kalman filter (EAKF), a data assimilation algorithm, was used to iteratively assimilate the observed case data and adjust the model and parameters. Our study demonstrated that the metapopulation network-EAKF system provided accurate predictions for city-level dengue transmission trajectories in retrospective forecasts of 12 cities in Guangdong province, China. Specifically, the system accurately predicts local dengue outbreak magnitude and the temporal peak of the epidemic up to 10 wk in advance. In addition, the system predicted the peak time, peak intensity, and total number of dengue cases more accurately than isolated city-specific forecasts. The general metapopulation assimilation framework presented in our study provides a methodological foundation for establishing an accurate system with finer temporal and spatial resolution for retrospectively forecasting the magnitude and temporal peak of dengue fever outbreaks. These forecasts based on the proposed method can be interoperated to better support intervention decisions and inform the public of potential risks of disease transmission. |
format |
Article in Journal/Newspaper |
author |
Qinghui Zeng Xiaolin Yu Haobo Ni Lina Xiao Ting Xu Haisheng Wu Yuliang Chen Hui Deng Yingtao Zhang Sen Pei Jianpeng Xiao Pi Guo |
author_facet |
Qinghui Zeng Xiaolin Yu Haobo Ni Lina Xiao Ting Xu Haisheng Wu Yuliang Chen Hui Deng Yingtao Zhang Sen Pei Jianpeng Xiao Pi Guo |
author_sort |
Qinghui Zeng |
title |
Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. |
title_short |
Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. |
title_full |
Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. |
title_fullStr |
Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. |
title_full_unstemmed |
Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. |
title_sort |
dengue transmission dynamics prediction by combining metapopulation networks and kalman filter algorithm. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2023 |
url |
https://doi.org/10.1371/journal.pntd.0011418 https://doaj.org/article/d341c8db986a4ac6b89a860edb33e2a8 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 17, Iss 6, p e0011418 (2023) |
op_relation |
https://doi.org/10.1371/journal.pntd.0011418 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0011418 https://doaj.org/article/d341c8db986a4ac6b89a860edb33e2a8 |
op_doi |
https://doi.org/10.1371/journal.pntd.0011418 |
container_title |
PLOS Neglected Tropical Diseases |
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17 |
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6 |
container_start_page |
e0011418 |
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