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Dengue is a viral infection caused by one of the four closely related yet antigenically distinct virus serotypes, and transmitted by Aedes mosquitoes, primarily the Ae. aegypti and Ae. albopictus. With a tropical rainforest climate, rapid urbanization, changing demography and ecology, Singapore experiences endemic dengue, with the last large outbreak in 2013 culminating in 22,000 cases. In the absence of an effective vaccine against dengue, supressing the mosquito vector population remains the key thrust of Singapore’s dengue control program. However, with the limited resources available, it is important that public health professionals know when and where resources should be invested in so as to ensure effective deployment of resources and achieving maximum impact. Machine learning techniques were therefore used to develop dengue warning systems in Singapore. These warning systems provide early warning of an impending outbreaks, facilitating preparedness for public health response as well as highlight risk areas for prioritization of resources for preemptive source reduction exercise.

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