Demographic-environmental effect on dengue outbreaks in 11 countries.

Sajib, AH; Akter, S; Saha, G; Hossain, Z

Demographic-environmental effect on dengue outbreaks in 11 countries.

Sajib, AH Akter, S Saha, G

Hossain, Z

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Publisher:: PUBLIC LIBRARY SCIENCE
Publication Type:: Journal Article
Citation:: PLoS One, 2024, 19, (9), pp. e0305854
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Sajib, AH
dc.contributor.author	Akter, S
dc.contributor.author	Saha, G https://orcid.org/0000-0001-6467-298X
dc.contributor.author	Hossain, Z
dc.contributor.editor	Colborn, J
dc.date.accessioned	2024-12-02T00:53:33Z
dc.date.available	2024-06-05
dc.date.available	2024-12-02T00:53:33Z
dc.date.issued	2024
dc.identifier.citation	PLoS One, 2024, 19, (9), pp. e0305854
dc.identifier.issn	1932-6203
dc.identifier.issn	1932-6203
dc.identifier.uri	http://hdl.handle.net/10453/182181
dc.description.abstract	BACKGROUND: Dengue outbreaks are common in tropical or temperate countries, and climate change can exacerbate the problem by creating conditions conducive to the spread of mosquitoes and prolonging the transmission season. Warmer temperatures can allow mosquitoes to mature faster and increase their ability to spread disease. Additionally, changes in rainfall patterns can create more standing water, providing a breeding ground for mosquitoes. OBJECTIVE: The objective of this study is to investigate the correlation between environmental and demographic factors and the dissemination of dengue fever. The study will use yearly data from 2000 to 2021 from 11 countries highly affected by dengue, considering multiple factors such as dengue cases, temperatures, precipitation, and population to better understand the impact of these variables on dengue transmission. METHODS: In this research, Poisson regression (PR) and negative binomial regression (NBR) models are used to model count data and estimate the effect of different predictor variables on the outcome. Also, histogram plots and pairwise correlation plots are used to provide an initial overview of the distribution and relationship between the variables. Moreover, Goodness-of-fit tests, t-test analysis, diagnostic plots, influence plots, and residual vs. leverage plots are used to check the assumptions and validity of the models and identify any outliers or influential observations that may be affecting the results. RESULTS: The findings indicate that mean temperature and log(Urban) had a positive impact on dengue infection rates, while maximum temperature, log(Precipitation), and population density had a negative impact. However, minimum temperature, log(Rural), and log(Total population) did not demonstrate any significant effects on the incidence of dengue. CONCLUSION: The impact of demographic-environmental factors on dengue outbreaks in 11 Asian countries is illuminated by this study. The results highlight the significance of mean temperature (Tmean), maximum temperature (Tmax), log(Urban), log(Precipitation), and population density in influencing dengue incidence rates. However, further research is needed to gain a better understanding of the role of additional variables, such as immunity levels, awareness, and vector control measures, in the spread of dengue.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	PUBLIC LIBRARY SCIENCE
dc.relation.ispartof	PLoS One
dc.relation.isbasedon	10.1371/journal.pone.0305854
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	General Science & Technology
dc.subject.mesh	Dengue
dc.subject.mesh	Humans
dc.subject.mesh	Disease Outbreaks
dc.subject.mesh	Animals
dc.subject.mesh	Temperature
dc.subject.mesh	Climate Change
dc.subject.mesh	Mosquito Vectors
dc.subject.mesh	Aedes
dc.subject.mesh	Demography
dc.subject.mesh	Environment
dc.subject.mesh	Animals
dc.subject.mesh	Humans
dc.subject.mesh	Aedes
dc.subject.mesh	Dengue
dc.subject.mesh	Environment
dc.subject.mesh	Temperature
dc.subject.mesh	Disease Outbreaks
dc.subject.mesh	Demography
dc.subject.mesh	Climate Change
dc.subject.mesh	Mosquito Vectors
dc.subject.mesh	Dengue
dc.subject.mesh	Humans
dc.subject.mesh	Disease Outbreaks
dc.subject.mesh	Animals
dc.subject.mesh	Temperature
dc.subject.mesh	Climate Change
dc.subject.mesh	Mosquito Vectors
dc.subject.mesh	Aedes
dc.subject.mesh	Demography
dc.subject.mesh	Environment
dc.title	Demographic-environmental effect on dengue outbreaks in 11 countries.
dc.type	Journal Article
utslib.citation.volume	19
utslib.location.activity	United States
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-12-02T00:53:29Z
pubs.issue	9
pubs.publication-status	Published online
pubs.volume	19
utslib.citation.issue	9

Abstract:

BACKGROUND: Dengue outbreaks are common in tropical or temperate countries, and climate change can exacerbate the problem by creating conditions conducive to the spread of mosquitoes and prolonging the transmission season. Warmer temperatures can allow mosquitoes to mature faster and increase their ability to spread disease. Additionally, changes in rainfall patterns can create more standing water, providing a breeding ground for mosquitoes. OBJECTIVE: The objective of this study is to investigate the correlation between environmental and demographic factors and the dissemination of dengue fever. The study will use yearly data from 2000 to 2021 from 11 countries highly affected by dengue, considering multiple factors such as dengue cases, temperatures, precipitation, and population to better understand the impact of these variables on dengue transmission. METHODS: In this research, Poisson regression (PR) and negative binomial regression (NBR) models are used to model count data and estimate the effect of different predictor variables on the outcome. Also, histogram plots and pairwise correlation plots are used to provide an initial overview of the distribution and relationship between the variables. Moreover, Goodness-of-fit tests, t-test analysis, diagnostic plots, influence plots, and residual vs. leverage plots are used to check the assumptions and validity of the models and identify any outliers or influential observations that may be affecting the results. RESULTS: The findings indicate that mean temperature and log(Urban) had a positive impact on dengue infection rates, while maximum temperature, log(Precipitation), and population density had a negative impact. However, minimum temperature, log(Rural), and log(Total population) did not demonstrate any significant effects on the incidence of dengue. CONCLUSION: The impact of demographic-environmental factors on dengue outbreaks in 11 Asian countries is illuminated by this study. The results highlight the significance of mean temperature (Tmean), maximum temperature (Tmax), log(Urban), log(Precipitation), and population density in influencing dengue incidence rates. However, further research is needed to gain a better understanding of the role of additional variables, such as immunity levels, awareness, and vector control measures, in the spread of dengue.

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http://hdl.handle.net/10453/182181