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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/23043443
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. 2012 Oct 9:12:853.
doi: 10.1186/1471-2458-12-853.

Analyzing the spatio-temporal relationship between dengue vector larval density and land-use using factor analysis and spatial ring mapping

Muhammad Shahzad Sarfraz  1 , Nitin K Tripathi, Taravudh Tipdecho, Thawisak Thongbu, Pornsuk Kerdthong, Marc Souris
Affiliations

Analyzing the spatio-temporal relationship between dengue vector larval density and land-use using factor analysis and spatial ring mapping

Muhammad Shahzad Sarfraz et al. BMC Public Health. .

Abstract

Background: Dengue, a mosquito-borne febrile viral disease, is found in tropical and sub-tropical regions and is now extending its range to temperate regions. The spread of the dengue viruses mainly depends on vector population (Aedes aegypti and Aedes albopictus), which is influenced by changing climatic conditions and various land-use/land-cover types. Spatial display of the relationship between dengue vector density and land-cover types is required to describe a near-future viral outbreak scenario. This study is aimed at exploring how land-cover types are linked to the behavior of dengue-transmitting mosquitoes.

Methods: Surveys were conducted in 92 villages of Phitsanulok Province Thailand. The sampling was conducted on three separate occasions in the months of March, May and July. Dengue indices, i.e. container index (C.I.), house index (H.I.) and Breteau index (B.I.) were used to map habitats conducible to dengue vector growth. Spatial epidemiological analysis using Bivariate Pearson's correlation was conducted to evaluate the level of interdependence between larval density and land-use types. Factor analysis using principal component analysis (PCA) with varimax rotation was performed to ascertain the variance among land-use types. Furthermore, spatial ring method was used as to visualize spatially referenced, multivariate and temporal data in single information graphic.

Results: Results of dengue indices showed that the settlements around gasoline stations/workshops, in the vicinity of marsh/swamp and rice paddy appeared to be favorable habitat for dengue vector propagation at highly significant and positive correlation (p = 0.001) in the month of May. Settlements around the institutional areas were highly significant and positively correlated (p = 0.01) with H.I. in the month of March. Moreover, dengue indices in the month of March showed a significant and positive correlation (p <= 0.05) with deciduous forest. The H.I. of people living around horticulture land were significantly and positively correlated (p = 0.05) during the month of May, and perennial vegetation showed a highly significant and positive correlation (p = 0.001) in the month of March with C.I. and significant and positive correlation (p <= 0.05) with B.I., respectively.

Conclusions: The study concluded that gasoline stations/workshops, rice paddy, marsh/swamp and deciduous forests played highly significant role in dengue vector growth. Thus, the spatio-temporal relationships of dengue vector larval density and land-use types may help to predict favorable dengue habitat, and thereby enables public healthcare managers to take precautionary measures to prevent impending dengue outbreak.

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Figures

Figure 1
Figure 1
Phitsanulok Province with district boundaries (study area). Inset box shows location of Phitsanulok Province (red) in Thailand. Red stars indicate the 92 villages which were sampled three times (i.e. March, May and July) during 2009. Dots indicate all the villages in Phitsanulok Province. Total area of Phitsanulok is approximately 10,816 km2.
Figure 2
Figure 2
Total number of patients suffering from various diseases and dengue infection between 2006 and 2010. Number of suspected dengue cases (cases with clinical symptoms) reported and the total number of patients. Figure shows that dengue cases reported each year vary in the study area.
Figure 3
Figure 3
Changes in temperature, rainfall and humidity from January to December, 2009. The X-axis shows the monthly average climatic variation, while the Y-axis shows climatic factor ranges such as temperature, humidity and rainfall. Humidity and temperature remained the same throughout the year but rainfall varied. Normally in Phitsanulok the rainy season starts from April and ends in October.
Figure 4
Figure 4
Province level container index (C.I.). This map is a visual presentation of C.I., i.e. the percentage of water holding containers infested with larvae, using interpolation techniques on the basis of sampled villages. The legend colors show the levels of C.I. from low (green) to high (red).
Figure 5
Figure 5
Province level house index (H.I.). This map is a visual presentation of H.I., i.e. the percentage of houses infested with larvae, using interpolation techniques on the basis of sampled villages. The legend colors show the levels of H.I. from low (green) to high (red). As per the National Institute of Health (NIH) Thailand, a H.I. of less than 1% is considered to be low risk and greater than 10% is considered to be higher risk.
Figure 6
Figure 6
Province level Breteau index (B.I.). This map is a visual presentation of B.I., i.e. the number of positive containers per 100 houses inspected, using interpolation techniques on the basis of sampled villages. The legend colors show the levels of B.I. from low (green) to high (red). As per the National Institute of Health (NIH) Thailand, a B.I. of less than 5% is considered to be low risk and greater than 50% is considered to be higher risk.
Figure 7
Figure 7
Land-use/land-cover map showing the land-cover types.
Figure 8
Figure 8
Average Breteau index (B.I.) throughout the year for Phitsanulok, district-wise (base map) and according to temporal changes (rings). The base map shows the average B.I. on the basis of interpolation between the villages sampled. Spokes around the base maps indicate village locations, while the inner ring values show average index values of the sampled villages. Extending to the outside from the inner ring, the outer layers represent the index variations during March, May and July, respectively.
Figure 9
Figure 9
Relationships among dengue indices throughout the year in Phitsanulok (District-wise). Average B.I. (base map), average C.I. (inner ring) and average H.I. (outer ring), data was collected with the help of the Ministry of Public Health, Phitsanulok Province. Spokes around the base maps indicate the sampled village locations. The legend colors show the levels of B.I. from low (green) to high (red).
Figure 10
Figure 10
Methodology flow chart.
Figure 11
Figure 11
Relationship between low and high Breteau index (B.I.) having different land-cover and land-use. Twelve representative factors were extracted on the basis of factor analysis. Equal/approximately equal percentage of area showed that there was no difference between land-cover/land-use having a B.I. of less than 20 and a B.I. of greater than 50. Difference in land-cover and land-use area (percentage) represents significance difference in B.I.
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