No abstract

For personal use only.

A novel method was used to incorporate in vivo host-pathogen dynamics into a new robust outbreak model for legionellosis. Dose-response and time-dose-response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best-fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best-fit TDR from the murine dosing study was the beta-Poisson with exponential-reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non-time-dependent distributions to explore the performance of the time-dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host-pathogen system.

No abstract

Experimental time-to-infection data is a useful, but often underutilized, material for examining the mechanics of in vivo pathogen growth. In this paper, the authors attempt to incorporate a time-dose-response (TDR) equation into a model which predicts the number of ill persons per day in a Giardia lamblia epidemic using data collected from a Pittsfield, Massachusetts outbreak. To this end, dose-response and TDR models were generated for Giardia exposure to beaver and human volunteers, and a maximum likelihood estimation approach was used to ensure that the models provided acceptable fits. The TDR equation that best-fit the human data was the beta-Poisson with exponential-reciprocal dependency model, and this was chosen to be incorporated into the outbreak model. The outbreak model is an expanded probability model that convolutes an assumed incubation distribution of the infectious agent with an exposure distribution. Since the beta-Poisson with exponential-reciprocal dependency models the time-to-infection density distribution, it is input as the incubation distribution. Several density functions, including the Weibull, lognormal, gamma, and uniform functions served as exposure distributions. The convolution of the time-dependent probability distribution with the lognormal distribution yielded the best-fit for the outbreak model.