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The Adaptive Multiple Importance Sampling algorithm (AMIS) is an iterative technique which recycles samples from all previous iterations in order to improve the efficiency of the proposal distribution. We have formulated a new statistical framework based on AMIS to sample parameters of transmission models based on high-resolution geospatial maps of disease prevalence, incidence, or relative risk. We tested the performance of our algorithm on four case studies: ascariasis in Ethiopia, onchocerciasis in Togo, HIV in Botswana, and malaria in the Democratic Republic of the Congo.

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