Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million dengue infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes include the risk of an infection and its severity, the timing and magnitude of outbreaks, and risk factors. We make use of surveillance data on Aedes aegypti larvae counts collected by the Taiwan Centers for Disease Control (CDC) and high-resolution weather data to build a model for predicting the risk of dengue transmission. Our risk assessment consists of two stages. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes. Based on the results from stage one, administrative divisions above a threshold percentage of larvae positive locations are used for the stage two, where we estimate the larvae counts using a zero-inflated negative binomial model. The stage one model has high sensitivity and increases the quality of the stage two estimates. The stage two model delivers prediction intervals with high coverage probability.