Detecting Local Clusters in the Data on Disease Vectors Influenced by Linear Features

Spatial scan statistic has been applied in many disease vector studies. However it rarely takes into account some relevant contextual information. As a result, the interpretation of the test results has been challenging and some interpretations could be misleading. In this study, a new technique to apply spatial scan statistic for the detection of local clusters in disease vectors is proposed. This new technique takes into account relevant contextual information. In particular, it considers the influences of linear features on the distribution of disease vectors. A case study on malaria vectors was conducted to elucidate this new technique. The results of the case study indicate that the proposed approach can provide a more meaningful identification and interpretation of local malaria vector clusters than the original spatial scan statistic.