Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking

Sarah F. McGough; Michael A. Johansson; Marc Lipsitch; Nicolas A. Menzies

To simulate ILI data with a time-varying probability of reporting delay d=0, we drew, for each week, Pr(d=0) from Unif(0.2, 0.9) for all weeks in which the total number of eventually-observed cases exceeded the mean of the ILI series (14,000 cases), and from Unif(0, 0.65) for all weeks in which the total observed case count was less than or equal to 14,000. This probability was used to calculate the simulated number of cases that would be observed with d=0, out of the total number of cases that would be eventually observed for that week. The remaining cases were distributed to other delays ranging from 1-52 weeks using NB(0.9,0.4). This produced a rough approximation for a hypothetical scenario in which cases are reported faster (higher probability of d=0) during weeks with higher disease activity (more cases). All rights reserved. No reuse allowed without permission.