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. 2014 Oct 16;11(10):10694-709.
doi: 10.3390/ijerph111010694.

Predictiveness of disease risk in a global outreach tourist setting in Thailand using meteorological data and vector-borne disease incidences

Affiliations

Predictiveness of disease risk in a global outreach tourist setting in Thailand using meteorological data and vector-borne disease incidences

Suwannapa Ninphanomchai et al. Int J Environ Res Public Health. .

Abstract

Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological factors on disease incidence that may be useful for the implementation of disease prevention and vector control programs on the tourism island, where climatic factors fluctuate.

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Figures

Figure 1
Figure 1
Number of dengue and malaria cases reported on monthly basis on Koh Chang during the years 2001–2011.
Figure 2
Figure 2
Reported dengue cases (black dotted line) and fitted cases (red line) generated by model for the years 2001–2011.
Figure 3
Figure 3
Relative risk of dengue cases as functions of respective climate variables at various lag times.
Figure 4
Figure 4
Reported malaria cases (black dotted line) and fitted cases (red line) generated by model for the years 2001–2011.
Figure 5
Figure 5
Relative risk of malaria cases as functions of respective climate variables at various lag times.

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