Malaria is one of the leading causes of morbidity in Farafangana, an urban area in the south-eastern coast of Madagascar with around 35,000 inhabitants. Predictive models of incidence are needed to strengthen prevention measures in case of an epidemic risk. In a context of large scale vector-control interventions, with changing mosquito density and behaviour, entomological surveillance data could be useful to better forecast malaria incidence. Our primary objective was to build a predictive model of malaria incidence (up to two months in advance) in the primary health care center (PHC) of Farafangana, including past incidence (at least two months back), climatic, environmental, vector-control and entomological covariates. Our secondary objective was to quantify how entomological covariates might have improved the model fit. Diagnosed malaria incidence data at the PHC of Farafangana came from the Fever Sentinel Surveillance Network of the Institut Pasteur de Madagascar. Climatic data (temperature and precipitations) and environmental data (Normalized Difference Vegetation Index – NDVI) were extracted from the International Research Institute for Climate and Society (missing values were imputed by exponential smoothing). Vector-control covariates, insecticide treated nets from mass distributions and indoor residual spraying, were binary (1 value means effective). From January 2014 until March 2017, human landing mosquito collections were performed every two months for two consecutive nights (from 6 pm to 6 am), inside and outside five houses spread over one central district, close to the PHC. As entomological data were not measured continuously, we made the hypothesis that they could be repeated until the next capture session. We focused our work on the three most abundant vectors: Anopheles gambiae (n=209), An. coustani (n=215) and An. funestus (n=19). Aggressiveness was calculated for each vector species and capture time (number of bites per human and per evening (f