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

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

While utilizing a similar modeling structure for case reporting delays as the benchmark model (9) , NobBS introduces a simple dependency between case counts over time; that is, changes in case counts between weeks are assumed to be related via a first-order random walk process on the logarithmic scale. This feature is critical in the context of infectious disease transmission, where the number of true infections in a given week mechanistically depends in part on the number of true infections in previous weeks due to the infectious process, whether the pathogen is transmitted directly or by vectors (18) . Hence, variations of autoregressive models are common in disease forecasting (19, 20) . When reporting delays are time-varying, as is often the case in epidemics (17), we show that the NobBS approach is less accurate compared to its performance in a stable delay distribution, but still shows improvement over the benchmark approach likely because the NobBS approach is informed by the number of cases experienced in previous weeks, not just the delay distribution, making it more robust to larger fluctuations.