Correct measurement of each day an infection incidence is essential to epidemic response. Nonetheless, delays in symptom onset, testing, and reporting obscure the dynamics of transmission, necessitating strategies to take away the results of stochastic delays from noticed information. Present estimators may be delicate to mannequin misspecification and censored observations; many analysts have as a substitute used strategies that exhibit robust bias. We develop an estimator with a regularization scheme to deal with stochastic delays, which we time period the strong incidence deconvolution estimator. We evaluate the tactic to present estimators in a simulation examine, measuring accuracy in quite a lot of experimental situations. We then use the tactic to check COVID-19 information in the US, highlighting its stability within the face of misspecification and proper censoring. To implement the strong incidence deconvolution estimator, we launch incidental, a ready-to-use R implementation of our estimator that may assist ongoing efforts to observe the COVID-19 pandemic.