Does spatial proximity drive norovirus transmission during outbreaks in hospitals?

Harris, John P; Lopman, Ben A; Cooper, Ben S; O'Brien, Sarah J

The analysis is based on a probabilistic reconstruction of chains of transmission (trees) based on the dates of illness onset for patients affected in outbreaks. It makes use of methods developed for SARS transmission and later applied to norovirus [12] [13] [14] [15] . If we knew with certainty who acquired infection from whom it would be straightforward to quantify the role of proximity in norovirus outbreaks, for example, by using regression analysis. However, in practice, transmission events are unobserved, so instead we consider all possible infection trees consistent with the data. We used a previously described approach to calculate the probability, π ij , that patient i was infected by patient j for each pair of infected patients in each outbreak based on onset times and the serial interval distribution (the serial interval is the time from onset of symptoms in case i to case j), without using proximity data. The serial interval distribution tells us the probability of durations of 0,1, 2,… days between onset in a case and onset in secondary cases infected by this case. Given multiple possible sources for a case, we can use knowledge of this distribution to tell us how likely each is to be the true source. Full technical details are described in Wallinga & Teunis (2004) [12] .