Although the mathematical model was not disclosed, Dr. Self stated that the data fit the projections overall. However, she said that “every year we see 800 fewer-than-expected small outbreaks, and every 3 years we see 1 fewer than expected large outbreak.” Not surprisingly, there tend to be more small food-borne outbreaks, which are also much easier to “miss” than large-scale ones, and therefore there are likely many small outbreaks that are either not documented or not officially identified and reported.
Large-scale outbreaks, on the other hand, which happen less frequently than smaller ones, are easier to spot but may not actually swell to their full “potential” before intervention stalls them. This may lead to outbreaks that the model would predict to be “large” being classified somewhere lower on the relative scale.
There are several possible limitations on larger outbreaks’ size, including public health interventions, food-safety policies, outbreak investigation and response protocols that may terminate large outbreaks before they reach their natural limitations, as well as some natural limitations on large outbreak size, the team observed. Dr. Self also noted in response to an audience question that since outbreak response tends to improve over time, it is possible that this could have played a role in the results particularly toward the end of the study time frame.
“We would like to look at outbreaks by pathogen,” she concluded, adding that the team hypothesized that they would see similar relationships in this study, albeit possibly with “different slopes.” She also noted that the power law tool might be particularly useful in the allocation of resources and outbreak planning. “In some of the largest outbreaks we’ve observed, the resources to subtype have been lacking. They [public health organizations] have run out of reagents at times in order to subtype a large outbreak. This [model] gives us a better estimate of what is needed,” she said.
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