Selected article for: "primary infection and secondary transmission"

Author: Neri, Franco M.; Cook, Alex R.; Gibson, Gavin J.; Gottwald, Tim R.; Gilligan, Christopher A.
Title: Bayesian Analysis for Inference of an Emerging Epidemic: Citrus Canker in Urban Landscapes
  • Document date: 2014_4_24
  • ID: 01yc7lzk_52
    Snippet: There were clear trends in both the expectation and the precision of estimates for the secondary transmission rate, b. As in the case of a, the posterior distribution for b had a large variance when based upon data for the first three months, and adding extra monthly snapshots decreased the variance of the posterior (cf Figures 2B,E) . In contrast with the case of a, there was also a trend in the posteriors for b to decrease as time progressed. T.....
    Document: There were clear trends in both the expectation and the precision of estimates for the secondary transmission rate, b. As in the case of a, the posterior distribution for b had a large variance when based upon data for the first three months, and adding extra monthly snapshots decreased the variance of the posterior (cf Figures 2B,E) . In contrast with the case of a, there was also a trend in the posteriors for b to decrease as time progressed. The trend in b is more appropriately characterised by the sliding windows ( Figure 2E ), in which estimates are averaged over successive but overlapping six 30-day intervals (cf. model M DT slid in Table 1 , with DT = 6 months). Similar results were obtained for e ( Figures 2C,F) , suggesting that both forms of transmission were driven by environmental variables. Epidemics were dominated by secondary over primary infection: the forces of infection corresponding to b were much greater than those for e. Hence, in the following we will focus our analysis of environmental trends on the time dependence of b.

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