Sentence examples for range of outbreak from inspiring English sources

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.

Next, we simulated a range of outbreak signal durations (lasting 1, 2, 4, 6, 8, 16, and 32 days) and a range of sizes using the forecast errors of the six algorithms[ 18].

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.

Our estimate of a person-to-person basic reproduction number R0 pp  ≈ 1 in 1976 suggests that Ebola would have been capable of generating a wide range of outbreak sizes in the absence of any extrinsic variation in epidemiological conditions.

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.

However, such interpretations required confirmation by the analysis of larger samples collected from wide range of outbreaks.

Comparison of model outputs with public health surveillance data to verify that they reflect the range of outbreaks observed to date may be a useful means of validating the simulated datasets and model parameters.

Strategies used to support validity have included the incorporation of uncertainty using sensitivity analyses, modelling a range of possible scenarios [ 9], and simulating a range of outbreaks so that those most similar to observed outbreaks for the disease under investigation can be used [ 4].

Starting in the mid-1990s, the frequency, severity, and geographic range of WNV outbreaks increased, and outbreaks of WNV meningitis and encephalitis affecting primarily adults struck Bucharest, Romania, in 1996, Volgograd, Russia, in 1999, and Israel, in 2000 [ 14– 16].

The SIR dynamics (Section "Epidemiological dynamics and information spreading: predictive metacommunity model") is calibrated on the range of the outbreak velocity and arrival times, and is simulated by maximizing the prediction accuracy of the observed outbreak patterns in terms of cumulative outbreaks.

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].