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nd possibly stem the spread.
Bettencourt and Ribeiro developed an extension of standard epidemiological models that describes the probability of disease spread among a given population. The model then takes into account actual disease surveillance data gathered by health experts like the World Health Organization and looks for anomalies in the expected transmission rate versus the actual one. Based on this, the model provides health experts actual transmission probabilities for the disease. Unlike other statistical models that require huge amounts of data for accuracy, the Los Alamos tool works on very small populations such as a handful of infected people in a remote village.
After developing their Bayesian estimation of epidemic potential, Bettencourt went back and looked at actual epidemiological surveillance data collected during Bird Flu outbreaks in certain parts of the world. Their model accurately portrayed actual transmission scenarios, lending confidence to its methodology.
In addition to its utility in understanding the transmissibility of emerging diseases, the new method is also advantageous because it allows public health experts to study outbreaks of more common ailments such as seasonal influenza early on. This can assist medical professionals in making better estimates of potential morbidity and mortality, along with assessments of intervention strategies and resource allocations that can help a population better cope with a developing seasonal outbreak.
We are closing the loop on science-based prediction of transmission consequences in real time, said Ribeiro. A program of this type is something that needs to be implemented at a worldwide level to provide an integrated way to respond a priori to an emerging disease threat.
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| Contact: James Rickman jamesr@lanl.gov 505-665-9203 DOE/Los Alamos National Laboratory Source:Eurekalert |
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