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Rabies is a fatal viral disease typically transmitted through the bite of rabid animal. Domestic dogs cause over 99% of human rabies deaths. Over half of the world's population lives in a country where the canine rabies virus variant is endemic and dog bites are common. An estimated 29 million people worldwide receive post-bite vaccination after being exposed to animals suspected of rabies. Accurate and timely risk assessment of rabies in biting dogs is critical to ensure that rabies PEP is administered to all persons with a suspected rabies exposure, while avoiding PEP administration in situations where rabies can be definitively ruled out. In this study, a logistic regression model was developed to quantify the risk of rabies in biting dogs, using data from Haiti's animal rabies surveillance program. Significant risk factors identified in the model were used to quantify the probability of rabies in biting dogs. The risk of rabies in a biting dog as assessed through Haiti's rabies surveillance program was highly elevated when the dog displayed hypersalivation (OR = 34.6, 95% CI 11.3-106.5) or paralysis (OR = 19.0, 95% CI 4.8-74.8) and when the dog was dead at the time of the assessment (OR = 20.7, 95% CI 6.7-63.7). Lack of prior rabies vaccination, biting 2 or more people, and if the dog was a puppy also increased the probability that a biting dog would have rabies. The model showed high sensitivity (100%) and specificity (97%) when examined using validation data. This model enables us to project the risk of rabies in biting dogs in Haiti shortly after the bite event and make provisional PEP recommendations prior to laboratory testing or dog quarantine results. Application of this model may improve adherence to PEP for bite victims who can be educated on the quantitative risk of the exposure event. This model can also be used to reduce unnecessary PEP costs when the risk of rabies is determined as sufficiently low and the animal is available for observation.