Using Routine Surveillance Data to Estimate the Epidemic Potential of Emerging Zoonoses: Application to the Emergence of US Swine Origin Influenza A H3N2v Virus

Cauchemez, Simon; Epperson, Scott; Biggerstaff, Matthew; Swerdlow, David; Finelli, Lyn; Ferguson, Neil M.

We present simple methods to estimate the reproduction number of emerging zoonoses from routine surveillance data. We consider three scenarios for the case-to-case variation in infectiousness: high (i.e., most transmission events are caused by a small proportion of cases like for SARS; k = 0.16), medium (k = 0.5), and low (k = 5) [12] . a n is the number of clusters. F is the proportion of first detected cases in each cluster that were infected by the reservoir. This research project was initiated to answer a seemingly straightforward question: 50% of the H3N2v-M cases that were detected in the US in 2011 had no contact with swine. What were the implications for the level of human-to-human transmission? At the time, however, the answer did not appear as straightforward as our analysis now shows it to be, with these simple estimators R = 12G or R = 12F, depending on the surveillance scenario we are in. Our approach has specific properties that potentially overcome some of the limitations of existing methods. First, the investigation effort required is less than that for other methods. For example, if there is active case finding (surveillance scenario 2), one only needs to investigate the source of infection of the first case detected in each cluster. Second, the statistical treatment of the data is extremely simple, making it possible for anyone to interpret raw surveillance statistics about the source of infection of cases (statistics G or F) in terms of human-to-human transmissibility (reproduction number R). Third, the method is robust to selection bias (i.e., the fact that larger clusters are more likely to be detected) and under-ascertainment (i.e., ability to detect all cases in a cluster once a cluster is identified).