Pandemic 2009 H1N1 influenza in two settings in a small community: the workplace and the university campus

Abstract

Data are rare on influenza outbreaks spreading through a workplace, but such transmission dynamics would be useful for comparison with the spread of the infection in other settings. We collected and compared infection data from two settings, a workplace and a university campus, during the 2009 pandemic influenza A(H1N1)pdm09 outbreak in a geographically contained community. Trajectories of infection were markedly alike in both settings. This suggests that transmission behaviour was similar in individuals in the two environments, despite the condition that individuals can leave the workplace setting in order to avoid transmission.

Survival dynamical systems: individual-level survival analysis from population-level epidemic models

“…Consequently, the MAP value of scriptR0≈1.06 is slightly smaller than that obtained in the previous analysis, and the SDS-based MAP for ρ is substantially larger than other estimates of the initially infected. Contrary to previous analysis [39], these values suggest that the high peak of an epidemic in early days of the academic year was not caused by high infectivity among newly infected students but rather by a high number of already infected individuals (high value of ρ ). This point was already made in [38].…”

“…As discussed in an earlier analysis of this dataset [38], these data may have been subject to both over-reporting and under-reporting: Some students may have assumed they had H1N1 when they had other influenza-like illnesses, while some students infected with H1N1 may not have sought medical care. However, such misreporting was considered to be relatively minor compared to the overall counts in the dataset [39]. This dataset was analysed earlier using a stochastic SIR model with parameters estimated using both likelihood-based and least-squares methods.…”

“…Assuming r individuals have recovered after infectious periods w 1 < · · · < w r < T , we have ℓRfalse(w1,…,wrfalsefalse|θ,kfalse)=false(k−rfalse)log⁡Hγfalse(Tfalse)+∑i=1rlog⁡hγfalse(wifalse),where Hγfalse(⋅false) and hγfalse(⋅false) are, respectively, the survival function and the probability density function of the exponential distribution with rate γ . Averaging the infectious periods used in the previous analysis [38,39], we assume here that the recovery times have an exponential distribution with mean γ −1 = 5.5 days (see also [40,41]), so γ was not estimated. The complete log-likelihood conditional on the population size n , the parameters and observables is then ℓ0(t1…,tk,w1,…,wr|θ,n)=ℓI(t1,…,tk|θ,n)+ℓ…”

“…2In fact, as described in [39], for the first 10 days of the outbreak, all suspected cases were tested and laboratory-confirmed to be H1N1, after which all cases were considered H1N1.…”

Estimating epidemic parameters: Application to H1N1 pandemic data

“…The data may have been subject to both over-reporting and under-reporting, as described in [18], as some students may have incorrectly presumed they had H1N1 when they had other influenza-like illnesses, while other students may have decided not to seek medical care. Overall though misreporting was considered as a relatively minor issue for the dataset [18]. The total student body population at the onset of an epidemic was taken as 18,234.…”