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Automated cough detection has significant applications for the surveillance of diseases and supports medical decisions, as cough sounds can be a useful biomarker. However, the implementation and evaluation of robust cough detection models can be challenging due to the lack of real-world data. This paper introduces and makes available a collection of 2,883 coughs and 3,074 non-cough sounds recorded in clinic waiting rooms that we hope will become a baseline for this task. Using this dataset, we evaluate different convolutional network architectures for classifying short audio segments as cough or non-cough. An ensemble model of convolutional neuronal networks provides the most robust performance and has a ROC AUC of $$98.1\%$$. Equally important, we construct a cough counter that incorporates the ensemble model to compute the number of coughs per day. Then, a simple linear model estimates the number of visits in which the patients report cough symptoms from the cough counts. This simple regression model can predict the number of cough visits in the clinic with an absolute mean error of 4.26 cough visits per day. Using additional information about when patients are in the clinic helps a similar regression model reach a mean absolute error of 3.65 cough visits per day. These results demonstrate the feasibility of using cough detection as a biomarker for the spread of respiratory viruses within the community.