The mean duration of outbreaks was compared between the northern and southern parts of Sweden by measuring the interquartile ranges of the outbreaks (i.e., the periods during which 50% of cases occurred).
They also reflect the magnitude of human and financial resources deployed in RVF prevention and management, the set of constraints and opportunities for resource mobilization encountered primarily by the relevant public institutions, and the range of effects that the outbreak of RVF has had on the livelihoods of the affected people.
Bayesian Markov Chain Monte Carlo (MCMC) techniques are the best established such methods and have been successfully employed in the analysis of a range of spatiotemporal outbreak data in the past [12] [14], as well as to purely temporal incidence data [15], [16].
Consequently, not only does translocation expand the range of an outbreak, but removal of target species could diminish local herd immunity by removal of vaccinated animals.
Ebola is much harder to catch: estimates of R0 in different parts of the outbreak range from 1.5 to 2.2.
For a disease like Ebola, much harder to catch, it is lower: estimates of Rο in different parts of the outbreak range from 1.5 to 2.2.
Rather than try to model the full outbreak process from infection to ED visit, as has been done for some limited outbreak types[ 17], we tried to model a wide range of outbreak signals, representing various ways that outbreaks in the community could alter ED syndrome counts.
The stochastic nature of the model ensures that simulations provide a representation of the possible range of outbreak scenarios based on existing knowledge about disease transmission.
However, we present this figure for qualitative comparisons, so that the reader can visualize the effects of baseline and outbreak temporal distribution, size, and duration on relative algorithm performance, over the range of outbreak signals we tested.
