It is important to have appropriate models for the surveillance and control of mosquito-borne diseases, such
as dengue fever (DF). These models need to be based on appropriate temporal and spatial scales. The aim of
this study was to illustrate the impact of different temporal and spatial scales on DF control decisions. We applied
the Getis–Ord Gi* statistic at different temporal and spatial scales to examine the local level of spatial
clusters at these scales in order to identify and visualize areas where numbers of adult female Aedes mosquitoes
were extreme and geographically homogenous. The modeled hotspot areas were different, depending on
whether they were modeled on weekly, monthly or yearly aggregated data. A similar result was found when
using different spatial scales for modeling, with different scales giving different hotspot regions. For 2006, the
highest risk areas (18 districts) were mostly identified in the central districts with a high rate of similarity
(95%) compared to the highest risk areas (19) identified in the averaged five-year period model. Knowledge
of appropriate temporal and spatial scales can provide an opportunity to specify the health burden of DF and
its vector within the hotspots, as well as set a platform that can help to pursue further investigations into associated
factors responsible for increased disease risk based on different temporal and spatial scales.
It is important to have appropriate models for the surveillance and control of mosquito-borne diseases, suchas dengue fever (DF). These models need to be based on appropriate temporal and spatial scales. The aim ofthis study was to illustrate the impact of different temporal and spatial scales on DF control decisions. We appliedthe Getis–Ord Gi* statistic at different temporal and spatial scales to examine the local level of spatialclusters at these scales in order to identify and visualize areas where numbers of adult female Aedes mosquitoeswere extreme and geographically homogenous. The modeled hotspot areas were different, depending onwhether they were modeled on weekly, monthly or yearly aggregated data. A similar result was found whenusing different spatial scales for modeling, with different scales giving different hotspot regions. For 2006, thehighest risk areas (18 districts) were mostly identified in the central districts with a high rate of similarity(95%) compared to the highest risk areas (19) identified in the averaged five-year period model. Knowledgeof appropriate temporal and spatial scales can provide an opportunity to specify the health burden of DF andits vector within the hotspots, as well as set a platform that can help to pursue further investigations into associatedfactors responsible for increased disease risk based on different temporal and spatial scales.
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