iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/26624008
Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Dec 1;9(12):e0004211.
doi: 10.1371/journal.pntd.0004211. eCollection 2015 Dec.

Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia

Magali Teurlai  1   2   3 , Christophe Eugène Menkès  3 , Virgil Cavarero  4 , Nicolas Degallier  5 , Elodie Descloux  6 , Jean-Paul Grangeon  7 , Laurent Guillaumot  8 , Thérèse Libourel  9 , Paulo Sergio Lucio  10 , Françoise Mathieu-Daudé  11 , Morgan Mangeas  9
Affiliations

Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia

Magali Teurlai et al. PLoS Negl Trop Dis. .

Abstract

Background/objectives: Understanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control. Dengue fever spatio-temporal patterns result from complex interactions between the virus, the host, and the vector. These interactions can be influenced by environmental conditions. Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years, to identify some of the main underlying factors, and to predict the spatial evolution of dengue fever under changing climatic conditions, at the 2100 horizon.

Methods: We used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24,272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia. We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections.

Results: The spatial distribution of dengue fever cases is highly heterogeneous. The variables most associated with this observed heterogeneity are the mean temperature, the mean number of people per premise, and the mean percentage of unemployed people, a variable highly correlated with people's way of life. Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics. By the end of the 21st century, if temperature increases by approximately 3 °C, mean incidence rates during epidemics could double.

Conclusion: In New Caledonia, a subtropical insular environment, both temperature and socio-economic conditions are influencing the spatial spread of dengue fever. Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors. This study presents a methodology that can be used as a step by step guide to model dengue spatial heterogeneity in other countries.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. General map of New Caledonia.
The map shows the location of towns (white dots), tribes (black dots), and weather stations registering temperature (red crosses) and rainfall (blue crosses) in New Caledonia. The background colour represents the digital elevation model (altitude).
Fig 2
Fig 2. Maps of the 5 explanatory variables selected for modelling dengue incidence rates.
A: average mean temperature; B: average daily rainfall; C: average daily rainfall during the wettest quarter; D: percentage of unemployed people; E: average number of people per premise.
Fig 3
Fig 3. Spatial heterogeneity of dengue annual incidence rates in New Caledonia.
Map of annual incidence rates per commune averaged over epidemic years of the 1995–2012 period (years 1995, 1996, 1998, 2003, 2004, 2008, 2009).
Fig 4
Fig 4. Principal component analysis over the set of climatic variables (A) and socio-economic variables (B).
The figure shows the correlation circles of PCA performed on the variables most spatially correlated with dengue average (across epidemic years) annual incidence rates (see methods/multivariable modelling of present dengue incidence rates/spatial autocorrelation of the response variable). Pearson correlation coefficients between variables can be approximated by the angle between the corresponding arrows: 1 for a 0° angle, 0 for a 90° angle, and -1 for a 180° angle.
Fig 5
Fig 5. Results of the best multivariable model of the spatial structure of dengue incidence rates.
A: Predicted mean (across epidemic years) annual incidence rates as a function of the two best explanatory variables (mean temperature and mean number of people per premise). The axes represent the value of the two best explanatory variables. Predicted average annual incidence rates are represented by the colour (blue for low incidence rates to orange for high incidence rates) and by the contour lines giving incidence rates in number of cases per 10,000 people per year. Each commune that has been used to build the model is placed on the graph according to the observed value of the two explanatory variables in the commune. Its position on the graph hence provides the average (across epidemic years) annual incidence rate in the commune as predicted by the model. For each commune, the coloured dot represents the difference between the predicted and the observed incidence rate (model error). B: Scatter plot of the predicted and observed average (across epidemic years) annual incidence rates for each of the 28 communes. The RMSE of this model is 45 cases per 10,000 per year.
Fig 6
Fig 6. Maps of observed and predicted average annual incidence rates.
A: map of observed dengue annual incidence rates. B and C: maps of dengue annual incidence rates predicted by the SVM model (B) and the linear model (C) based on the mean temperature and the mean number of people per premise (over epidemic years of the study period). D and E: Trends of dengue spatial distribution under global warming. Average annual incidence rates during epidemics as projected over the 2080–2099 period under the RCP 4.5 (D) and the RCP 8.5 (E) scenarios.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. WHO. Dengue Guidelines for Diagnosis, Treatment, Prevention, and Control. [Internet]. Geneva: TDR: World Health Organization; 2009. Available: http://site.ebrary.com/id/10363988 - PubMed
    1. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The Global Distribution and Burden of Dengue. Nature. 2013;496: 504–507. 10.1038/nature12060 - DOI - PMC - PubMed
    1. Gubler DJ. Dengue and Dengue Hemorrhagic Fever. Clin Microbiol Rev. 1998;11: 480–496. - PMC - PubMed
    1. Guzman MG, Halstead SB, Artsob H, Buchy P, Farrar J, Gubler DJ, et al. Dengue: a Continuing Global Threat. Nat Rev Microbiol. 2010;8: S7–S16. 10.1038/nrmicro2460 - DOI - PMC - PubMed
    1. Kyle JL, Harris E. Global Spread and Persistence of Dengue. Annu Rev Microbiol. 2008;62: 71–92. 10.1146/annurev.micro.62.081307.163005 - DOI - PubMed

Publication types

Grants and funding

MT was supported financially by the Province Sud of New Caledonia which provided a research allocation for supporting Ph.D. students, and by the Institute for Research and Development. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.