Short-term effects of tropical cyclones on the incidence of dengue: a time-series study in Guangzhou, China

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Abstract

Background

Limited evidence is available about the association between tropical cyclones and dengue incidence. This study aimed to examine the effects of tropical cyclones on the incidence of dengue and to explore the vulnerable populations in Guangzhou, China.

Methods

Weekly dengue case data, tropical cyclone and meteorological data during the tropical cyclones season (June to October) from 2015 to 2019 were collected for the study. A quasi-Poisson generalized linear model combined with a distributed lag non-linear model was conducted to quantify the association between tropical cyclones and dengue, controlling for meteorological factors, seasonality, and long-term trend. Proportion of dengue cases attributable to tropical cyclone exposure was calculated. The effect difference by sex and age groups was calculated to identify vulnerable populations. The tropical cyclones were classified into two levels to compare the effects of different grades of tropical cyclones on the dengue incidence.

Results

Tropical cyclones were associated with an increased number of dengue cases with the maximum risk ratio of 1.41 (95% confidence interval 1.17–1.69) in lag 0 week and cumulative risk ratio of 2.13 (95% confidence interval 1.28–3.56) in lag 0–4 weeks. The attributable fraction was 6.31% (95% empirical confidence interval 1.96–10.16%). Men and the elderly were more vulnerable to the effects of tropical cyclones than the others. The effects of typhoons were stronger than those of tropical storms among various subpopulations.

Conclusions

Our findings indicate that tropical cyclones may increase the incidence of dengue within a 4-week lag in Guangzhou, China, and the effects were more pronounced in men and the elderly. Precautionary measures should be taken with a focus on the identified vulnerable populations to control the transmission of dengue associated with tropical cyclones.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05486-2.

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Most cited references 39

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Distributed lag non-linear models

A Gasparrini, Timothy Armstrong, M. Kenward (2010)

Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross-basis’, a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley & Sons, Ltd.

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The current and future global distribution and population at risk of dengue

Jane Messina, Oliver J Brady, Nick Golding … (2019)

Dengue is a mosquito-borne viral infection that has spread throughout the tropical world over the past 60 years and now affects over half the world’s population. The geographical range of dengue is expected to further expand due to ongoing global phenomena including climate change and urbanization. We applied statistical mapping techniques to the most extensive database of case locations to date to predict global environmental suitability for the virus as of 2015. We then made use of climate, population and socioeconomic projections for the years 2020, 2050 and 2080 to project future changes in virus suitability and human population at risk. This study is the first to consider the spread of Aedes mosquito vectors to project dengue suitability. Our projections provide a key missing piece of evidence for the changing global threat of vector-borne disease and will help decision-makers worldwide to better prepare for and respond to future changes in dengue risk.

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Distributed Lag Linear and Non-Linear Models in R: The Package dlnm.

Antonio Gasparrini (2011)

Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data. This methodology rests on the definition of a crossbasis, a bi-dimensional functional space expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper offers an overview of the capabilities of the package, describing the conceptual and practical steps to specify and interpret DLNMs with an example of application to real data.

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Author and article information

Contributors

Chuanxi Li: lichuanxi567@163.com

Zhe Zhao: zhezhao@mail.sdu.edu.cn

Yu Yan: yanyu9615@163.com

Qiyong Liu: liuqiyong@icdc.cn

Qi Zhao: qi.zhao@sdu.edu.cn

Wei Ma: weima@sdu.edu.cn

Journal

Journal ID (nlm-ta): Parasit Vectors

Journal ID (iso-abbrev): Parasit Vectors

Title: Parasites & Vectors

Publisher: BioMed Central (London )

ISSN (Electronic): 1756-3305

Publication date (Electronic): 6 October 2022

Publication date PMC-release: 6 October 2022

Publication date Collection: 2022

Volume: 15

Electronic Location Identifier: 358

Affiliations

[1 ]GRID grid.27255.37, ISNI 0000 0004 1761 1174, Department of Epidemiology, School of Public Health, , Cheeloo College of Medicine, Shandong University, ; Jinan, China

[2 ]GRID grid.27255.37, ISNI 0000 0004 1761 1174, Shandong University Climate Change and Health Center, ; Jinan, China

[3 ]GRID grid.508381.7, ISNI 0000 0004 0647 272X, State Key Laboratory of Infectious Disease Prevention and Control, , National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, ; Beijing, China

[4 ]GRID grid.435557.5, ISNI 0000 0004 0518 6318, Department of Epidemiology, , IUF-Leibniz Research Institute for Environmental Medicine, ; Düsseldorf, Germany

Article

Publisher ID: 5486

DOI: 10.1186/s13071-022-05486-2

PMC ID: 9535872

PubMed ID: 36203178

SO-VID: 58e97ab0-ea69-4fe2-bfdd-eee56868b035

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 22 May 2022

Date accepted : 16 September 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100015360, State Key Laboratory of Infectious Disease Prevention and Control;

Award ID: 2018SKLID302

Award Recipient : Wei Ma

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 82073615

Award Recipient : Wei Ma

Custom metadata

ScienceOpen disciplines: Parasitology

Keywords: extreme weather event,tropical storm,typhoon,dengue,time series,stratified analysis

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ScienceOpen disciplines: Parasitology

Keywords: extreme weather event, tropical storm, typhoon, dengue, time series, stratified analysis

Short-term effects of tropical cyclones on the incidence of dengue: a time-series study in Guangzhou, China

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Abstract

Background

Methods

Results

Conclusions

Graphical Abstract

Supplementary Information

Related collections

Zika Virus

Most cited references 39

Distributed lag non-linear models

The current and future global distribution and population at risk of dengue

Distributed Lag Linear and Non-Linear Models in R: The Package dlnm.

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