Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China.
BACKGROUND:Dengue is a serious vector-borne disease, and incidence rates have significantly increased during the past few years, particularly in 2014 in Guangzhou. The current situation is more complicated, due to various factors such as climate warming, urbanization, population increase, and human...
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ftdoajarticles:oai:doaj.org/article:bfc35b3669734ec09613383c50cd2267 2023-05-15T15:12:48+02:00 Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China. Guanghu Zhu Jiming Liu Qi Tan Benyun Shi 2016-04-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0004633 https://doaj.org/article/bfc35b3669734ec09613383c50cd2267 EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC4841561?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0004633 https://doaj.org/article/bfc35b3669734ec09613383c50cd2267 PLoS Neglected Tropical Diseases, Vol 10, Iss 4, p e0004633 (2016) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2016 ftdoajarticles https://doi.org/10.1371/journal.pntd.0004633 2022-12-31T11:54:57Z BACKGROUND:Dengue is a serious vector-borne disease, and incidence rates have significantly increased during the past few years, particularly in 2014 in Guangzhou. The current situation is more complicated, due to various factors such as climate warming, urbanization, population increase, and human mobility. The purpose of this study is to detect dengue transmission patterns and identify the disease dispersion dynamics in Guangzhou, China. METHODOLOGY:We conducted surveys in 12 districts of Guangzhou, and collected daily data of Breteau index (BI) and reported cases between September and November 2014 from the public health authority reports. Based on the available data and the Ross-Macdonald theory, we propose a dengue transmission model that systematically integrates entomologic, demographic, and environmental information. In this model, we use (1) BI data and geographic variables to evaluate the spatial heterogeneities of Aedes mosquitoes, (2) a radiation model to simulate the daily mobility of humans, and (3) a Markov chain Monte Carlo (MCMC) method to estimate the model parameters. RESULTS/CONCLUSIONS:By implementing our proposed model, we can (1) estimate the incidence rates of dengue, and trace the infection time and locations, (2) assess risk factors and evaluate the infection threat in a city, and (3) evaluate the primary diffusion process in different districts. From the results, we can see that dengue infections exhibited a spatial and temporal variation during 2014 in Guangzhou. We find that urbanization, vector activities, and human behavior play significant roles in shaping the dengue outbreak and the patterns of its spread. This study offers useful information on dengue dynamics, which can help policy makers improve control and prevention measures. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 10 4 e0004633 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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English |
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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 Guanghu Zhu Jiming Liu Qi Tan Benyun Shi Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
BACKGROUND:Dengue is a serious vector-borne disease, and incidence rates have significantly increased during the past few years, particularly in 2014 in Guangzhou. The current situation is more complicated, due to various factors such as climate warming, urbanization, population increase, and human mobility. The purpose of this study is to detect dengue transmission patterns and identify the disease dispersion dynamics in Guangzhou, China. METHODOLOGY:We conducted surveys in 12 districts of Guangzhou, and collected daily data of Breteau index (BI) and reported cases between September and November 2014 from the public health authority reports. Based on the available data and the Ross-Macdonald theory, we propose a dengue transmission model that systematically integrates entomologic, demographic, and environmental information. In this model, we use (1) BI data and geographic variables to evaluate the spatial heterogeneities of Aedes mosquitoes, (2) a radiation model to simulate the daily mobility of humans, and (3) a Markov chain Monte Carlo (MCMC) method to estimate the model parameters. RESULTS/CONCLUSIONS:By implementing our proposed model, we can (1) estimate the incidence rates of dengue, and trace the infection time and locations, (2) assess risk factors and evaluate the infection threat in a city, and (3) evaluate the primary diffusion process in different districts. From the results, we can see that dengue infections exhibited a spatial and temporal variation during 2014 in Guangzhou. We find that urbanization, vector activities, and human behavior play significant roles in shaping the dengue outbreak and the patterns of its spread. This study offers useful information on dengue dynamics, which can help policy makers improve control and prevention measures. |
format |
Article in Journal/Newspaper |
author |
Guanghu Zhu Jiming Liu Qi Tan Benyun Shi |
author_facet |
Guanghu Zhu Jiming Liu Qi Tan Benyun Shi |
author_sort |
Guanghu Zhu |
title |
Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China. |
title_short |
Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China. |
title_full |
Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China. |
title_fullStr |
Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China. |
title_full_unstemmed |
Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China. |
title_sort |
inferring the spatio-temporal patterns of dengue transmission from surveillance data in guangzhou, china. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2016 |
url |
https://doi.org/10.1371/journal.pntd.0004633 https://doaj.org/article/bfc35b3669734ec09613383c50cd2267 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 10, Iss 4, p e0004633 (2016) |
op_relation |
http://europepmc.org/articles/PMC4841561?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0004633 https://doaj.org/article/bfc35b3669734ec09613383c50cd2267 |
op_doi |
https://doi.org/10.1371/journal.pntd.0004633 |
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PLOS Neglected Tropical Diseases |
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e0004633 |
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